From 00f415cf1139a61724b3f8c1009d94775919dc93 Mon Sep 17 00:00:00 2001 From: Gabriel Stechschulte <63432018+GStechschulte@users.noreply.github.com> Date: Thu, 17 Aug 2023 23:10:25 +0200 Subject: [PATCH] Change `plots` submodule name to `interpret` (#705) Co-authored-by: Osvaldo A Martin Co-authored-by: Tomas Capretto --- bambi/__init__.py | 1 + bambi/interpret/__init__.py | 11 + bambi/{plots => interpret}/create_data.py | 2 +- bambi/{plots => interpret}/effects.py | 4 +- bambi/{plots => interpret}/plot_types.py | 2 +- bambi/{plots => interpret}/plotting.py | 6 +- bambi/{plots => interpret}/utils.py | 0 bambi/plots/__init__.py | 4 +- docs/notebooks/plot_comparisons.ipynb | 2777 +++++++-------------- docs/notebooks/plot_predictions.ipynb | 1917 +++++++------- docs/notebooks/plot_slopes.ipynb | 127 +- pyproject.toml | 2 +- tests/test_plots.py | 2 +- 13 files changed, 1974 insertions(+), 2881 deletions(-) create mode 100644 bambi/interpret/__init__.py rename bambi/{plots => interpret}/create_data.py (99%) rename bambi/{plots => interpret}/effects.py (99%) rename bambi/{plots => interpret}/plot_types.py (98%) rename bambi/{plots => interpret}/plotting.py (98%) rename bambi/{plots => interpret}/utils.py (100%) diff --git a/bambi/__init__.py b/bambi/__init__.py index 8780fc761..0fb9e11b1 100644 --- a/bambi/__init__.py +++ b/bambi/__init__.py @@ -9,6 +9,7 @@ from .models import Model from .priors import Prior from .version import __version__ +from . import interpret __all__ = [ diff --git a/bambi/interpret/__init__.py b/bambi/interpret/__init__.py new file mode 100644 index 000000000..d1af7d1b2 --- /dev/null +++ b/bambi/interpret/__init__.py @@ -0,0 +1,11 @@ +from bambi.interpret.effects import comparisons, predictions, slopes +from bambi.interpret.plotting import plot_comparisons, plot_predictions, plot_slopes + +__all__ = [ + "comparisons", + "slopes", + "predictions", + "plot_comparisons", + "plot_predictions", + "plot_slopes", +] diff --git a/bambi/plots/create_data.py b/bambi/interpret/create_data.py similarity index 99% rename from bambi/plots/create_data.py rename to bambi/interpret/create_data.py index 16bea877a..b532a3545 100644 --- a/bambi/plots/create_data.py +++ b/bambi/interpret/create_data.py @@ -4,7 +4,7 @@ import pandas as pd from bambi.models import Model -from bambi.plots.utils import ( +from bambi.interpret.utils import ( ConditionalInfo, enforce_dtypes, get_covariates, diff --git a/bambi/plots/effects.py b/bambi/interpret/effects.py similarity index 99% rename from bambi/plots/effects.py rename to bambi/interpret/effects.py index 2203a3d08..1f09f8ebd 100644 --- a/bambi/plots/effects.py +++ b/bambi/interpret/effects.py @@ -10,8 +10,8 @@ import xarray as xr from bambi.models import Model -from bambi.plots.create_data import create_differences_data, create_predictions_data -from bambi.plots.utils import ( +from bambi.interpret.create_data import create_differences_data, create_predictions_data +from bambi.interpret.utils import ( average_over, ConditionalInfo, identity, diff --git a/bambi/plots/plot_types.py b/bambi/interpret/plot_types.py similarity index 98% rename from bambi/plots/plot_types.py rename to bambi/interpret/plot_types.py index e7bb53c23..43c273414 100644 --- a/bambi/plots/plot_types.py +++ b/bambi/interpret/plot_types.py @@ -3,7 +3,7 @@ import numpy as np import pandas as pd -from bambi.plots.utils import Covariates, get_unique_levels, get_group_offset, identity +from bambi.interpret.utils import Covariates, get_unique_levels, get_group_offset, identity def plot_numeric( diff --git a/bambi/plots/plotting.py b/bambi/interpret/plotting.py similarity index 98% rename from bambi/plots/plotting.py rename to bambi/interpret/plotting.py index 39384f207..01cbdec03 100644 --- a/bambi/plots/plotting.py +++ b/bambi/interpret/plotting.py @@ -8,9 +8,9 @@ from pandas.api.types import is_categorical_dtype, is_numeric_dtype, is_string_dtype from bambi.models import Model -from bambi.plots.effects import comparisons, slopes, predictions -from bambi.plots.plot_types import plot_categoric, plot_numeric -from bambi.plots.utils import get_covariates, ConditionalInfo +from bambi.interpret.effects import comparisons, slopes, predictions +from bambi.interpret.plot_types import plot_categoric, plot_numeric +from bambi.interpret.utils import get_covariates, ConditionalInfo from bambi.utils import get_aliased_name, listify diff --git a/bambi/plots/utils.py b/bambi/interpret/utils.py similarity index 100% rename from bambi/plots/utils.py rename to bambi/interpret/utils.py diff --git a/bambi/plots/__init__.py b/bambi/plots/__init__.py index 8a28e23ea..d60d42adb 100644 --- a/bambi/plots/__init__.py +++ b/bambi/plots/__init__.py @@ -1,5 +1,5 @@ -from bambi.plots.effects import comparisons, predictions, slopes -from bambi.plots.plotting import plot_comparisons, plot_predictions, plot_slopes +from bambi.interpret.effects import comparisons, predictions, slopes +from bambi.interpret.plotting import plot_comparisons, plot_predictions, plot_slopes __all__ = [ diff --git a/docs/notebooks/plot_comparisons.ipynb b/docs/notebooks/plot_comparisons.ipynb index 727ff1e01..2f2f66831 100644 --- a/docs/notebooks/plot_comparisons.ipynb +++ b/docs/notebooks/plot_comparisons.ipynb @@ -1,1897 +1,986 @@ { - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot Comparisons\n", - "\n", - "`comparisons` and `plot_comparisons` are a part of Bambi's sub-package `plots` that feature a set of functions used to interpret complex regression models. This sub-package is inspired by the R package [marginaleffects](https://vincentarelbundock.github.io/marginaleffects/articles/predictions.html#conditional-adjusted-predictions-plot). These two functions allow the modeler to **compare** the predictions made by a model for different contrast and covariate values. Below, it is described why comparing predictions is useful in interpreting generalized linear models (GLMs), how this methodology is implemented in Bambi, and how to use `comparisons` and `plot_comparisons`. It is assumed that the reader is familiar with the basics of GLMs. If not, refer to the Bambi [Basic Building Blocks](https://bambinos.github.io/bambi/notebooks/how_bambi_works.html#Link-functions) example.\n", - "\n", - "Due to the link function in a GLM, there are typically three quantities of interest to interpret:\n", - "\n", - "1. the linear predictor $\\eta = X\\beta$ where $X$ is an $n$ x $p$ matrix of explanatory variables.\n", - "2. the mean $\\mu = g^{-1}(\\eta)$ where the link function $g(\\cdot)$ relates the linear predictor to the mean of the outcome variable $\\mu = g^{-1}(\\eta) = g^{-1}(X\\beta)$\n", - "3. the response variable $Y \\sim \\mathcal{D}(\\mu, \\theta)$ where $\\mu$ is the mean parameter and $\\theta$ is (possibly) a vector that contains all the other \"auxillary\" parameters of the distribution.\n", - " \n", - "Often, with GLMs, $\\eta$ is linear in the parameters, but nonlinear in relation of inputs to the outcome $Y$ due to the link function $g$. Thus, as modelers, we are usually more interested in interpreting (2) and (3). For example, in logistic regression, the linear predictor is on the log-odds scale, but the quantity of interest is on the probability scale. In Poisson regression, the linear predictor is on the log-scale, but the response variable is on the count scale. Referring back to logistic regression, a specified difference in one of the $x$ variables does _not_ correspond to a constant difference in the the probability of the outcome.\n", - "\n", - "It is often helpful with GLMs, for the modeler and audience, to have a summary that gives the expected difference in the outcome corresponding to a unit difference in each of the input variables. Thus, the goal of `comparisons` and `plot_comparisons` is to provide the modeler with a summary and visualization of the average predicted difference." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Average Predictive Differences\n", - "\n", - "Here, we adopt the notation from Chapter 14.4 of [Regression and Other Stories](https://avehtari.github.io/ROS-Examples/) to describe average predictive differences. Assume we have fit a Bambi model predicting an outcome $Y$ based on inputs $X$ and parameters $\\theta$. Consider the following scalar inputs:\n", - "\n", - "$$w: \\text{the input of interest}$$\n", - "$$c: \\text{all the other inputs}$$\n", - "$$X = (w, c)$$\n", - "\n", - "Suppose for the input of interest, we are interested in comparing $w^{\\text{high}}$ to $w^{\\text{low}}$ (perhaps age = $60$ and $40$ respectively) with all other inputs $c$ held constant. The _predictive difference_ in the outcome changing **only** $w$ is:\n", - "\n", - "$$\\text{average predictive difference} = \\mathbb{E}(y|w^{\\text{high}}, c, \\theta) - \\mathbb{E}(y|w^{\\text{low}}, c, \\theta)$$\n", - "\n", - "Selecting the maximum and minimum values of $w$ and averaging over all other inputs $c$ in the data gives you a new \"hypothetical\" dataset and corresponds to counting all pairs of transitions of $(w^\\text{low})$ to $(w^\\text{high})$, i.e., differences in $w$ with $c$ held constant. The difference between these two terms is the average predictive difference." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Computing Average Predictive Differences\n", - "\n", - "The objective of `comparisons` and `plot_comparisons` is to compute the expected difference in the outcome corresponding to three different scenarios for $w$ and $c$ where $w$ is either provided by the user, else a default value is computed by Bambi (described in the default values section). The three scenarios are:\n", - "\n", - "1. user provided values for $c$.\n", - "2. a grid of equally spaced and central values for $c$.\n", - "3. empirical distribution (original data used to fit the model) for $c$. \n", - "\n", - "In the case of (1) and (2) above, Bambi assembles all pairwise combinations (transitions) of $w$ and $c$ into a new \"hypothetical\" dataset. In (3), Bambi uses the original $c$, but replaces $w$ with the user provided value or the default value computed by Bambi. In each scenario, predictions are made on the data using the fitted model. Once the predictions are made, comparisons are computed using the posterior samples by taking the difference in the predicted outcome for each pair of transitions. The average of these differences is the average predictive difference. \n", - "\n", - "Thus, the goal of `comparisons` and `plot_comparisons` is to provide the modeler with a summary and visualization of the average predictive difference. Below, we demonstrate how to compute and plot average predictive differences with `comparisons` and `plot_comparions` using several examples." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import arviz as az\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "\n", - "import bambi as bmb\n", - "from bambi.plots import comparisons, plot_comparisons" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Zero Inflated Poisson" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We model and predict how many fish are caught by visitors at a state park using survey [data](\"https://stats.idre.ucla.edu/stat/data/fish.csv\"). Many visitors catch zero fish, either because they did not fish at all, or because they were unlucky. We would like to explicitly model this bimodal behavior (zero versus non-zero) using a Zero Inflated Poisson model, and to compare how different inputs of interest $w$ and other covariate values $c$ are associated with the number of fish caught. The dataset contains data on 250 groups that went to a state park to fish. Each group was questioned about how many fish they caught (`count`), how many children were in the group (`child`), how many people were in the group (`persons`), if they used a live bait and whether or not they brought a camper to the park (`camper`)." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "fish_data = pd.read_stata(\"http://www.stata-press.com/data/r11/fish.dta\")\n", - "cols = [\"count\", \"livebait\", \"camper\", \"persons\", \"child\"]\n", - "fish_data = fish_data[cols]\n", - "fish_data[\"livebait\"] = pd.Categorical(fish_data[\"livebait\"])\n", - "fish_data[\"camper\"] = pd.Categorical(fish_data[\"camper\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Auto-assigning NUTS sampler...\n", - "Initializing NUTS using jitter+adapt_diag...\n", - "Multiprocess sampling (4 chains in 4 jobs)\n", - "NUTS: [count_psi, Intercept, livebait, camper, persons, child]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " |████████████████████████████████| 100.00% [8000/8000 00:03<00:00 Sampling 4 chains, 0 divergences]\r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 4 seconds.\n" - ] - } - ], - "source": [ - "fish_model = bmb.Model(\n", - " \"count ~ livebait + camper + persons + child\", \n", - " fish_data, \n", - " family='zero_inflated_poisson'\n", - ")\n", - "\n", - "fish_idata = fish_model.fit(\n", - " draws=1000, \n", - " target_accept=0.95, \n", - " random_seed=1234, \n", - " chains=4\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### User Provided Values\n", - "\n", - "First, an example of scenario 1 (user provided values) is given below. In both `plot_comparisons` and `comparisons`, $w$ and $c$ are represented by `contrast` and `conditional`, respectively. The modeler has the ability to pass their own values for `contrast` and `conditional` by using a dictionary where the key-value pairs are the covariate and value(s) of interest. For example, if we wanted to compare the number of fish caught for $4$ versus $1$ `persons` conditional on a range of `child` and `livebait` values, we would pass the following dictionary in the code block below. By default, for $w$, Bambi compares $w^\\text{high}$ to $w^\\text{low}$. Thus, in this example, $w^\\text{high}$ = 4 and $w^\\text{low}$ = 1. The user is not limited to passing a list for the values. A `np.array` can also be used. Furthermore, Bambi by default, maps the order of the dict keys to the main, group, and panel of the matplotlib figure. Below, since `child` is the first key, this is used for the x-axis, and `livebait` is used for the group (color). If a third key was passed, it would be used for the panel (facet)." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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UKT4mSt9P6RqwEbDW1NjvqKnZw9SRsIvlv+joaH3wgX8ehwUAAK0vPMyioZcFbnJ7WxYUE/MBAADaG0IYAACACQhhAAAAJgiaxVrRRJGdpIVus6sAAAAtxEgYAACACQhhAAAAJiCEAQAAmIAQBgAAYAJCGAAAgAkIYQAAIHCqq6TCHdJnb9b8b3VVQL9u+/btGjdunBITE2WxWPTOO+9c9DN5eXlKS0tTVFSU+vTpo1WrVgW0xlosUQEAAAKjIEfaNFvyHP2uLTZRGrtM6ndLQL6ytLRUAwcO1AMPPNCkF3AXFhbq5ptv1k9/+lO9/vrr+uijjzR16lR179494C/wJoQBAAD/K8iR/nC/pAveE+1x1rTf8VpAglhGRoYyMjKa3H/VqlXq1auXVqxYIUnq27evPvnkEz377LOEMAAAEAQMQzp3pml9q6ukP81SnQBWcyJJlpoRsj6jpLDwi58voqNksTS91kuwa9cupaen+7SNGTNGq1ev1rlz5xQRERGQ75UIYQAAoCnOnZGWJPrpZEbNLcqlSU3rPvdozRtjAsDlcslut/u02e12VVZW6vjx43I4HAH5XomJ+QAAoJ2zXDDKZhhGve3+xkgYAAC4uIiONSNSTXF4p/S7n1y8371vSsnDmvbdAZKQkCCXy+XTVlxcrA4dOqhbt24B+16JEAYAAJrCYmn6LcHLRtc8Belxqv55YZaa45eNbtqcsAAaOnSo3nvvPZ+2zZs3a9CgQQGdDyZxOxIAAPhbWHjNMhSSpAtv6X27P3ZpQALY6dOnlZ+fr/z8fEk1S1Dk5+fryJEjkqQ5c+bo/vvv9/afMmWKDh8+rKysLB04cEBr1qzR6tWrNXPmTL/XdiFCGAAA8L9+t9QsQxF7wcT22MSALU8hSZ988omuvfZaXXvttZKkrKwsXXvttZo/f74kyel0egOZJKWkpOj999/Xtm3bdM011+ipp57Sb37zm4AvTyFJFqN29lmI8ng8stlscrvdio2NNbscAACCXllZmQoLC5WSkqKoqKiWnay6qmaO2Omvpc72mjlgJt+C9IfGfkdNzR7MCQMAAIETFi6ljDC7iqDE7UgAAAATEMIAAABMQAgDAAAwASEMAADABIQwAABQrxBfQKFF/PG7MTWEZWdna/DgwYqJiVF8fLzGjx+vgwcP+vQxDEMLFy5UYmKioqOjNWrUKH3++ecmVQwAQOirXSn+zJkzJlcSvGp/Ny1ZVd/UJSry8vI0bdo0DR48WJWVlZo3b57S09NVUFCgTp1qXo3wzDPPaPny5XrllVd0xRVX6Je//KVuuukmHTx4UDExMWaWDwBASAoPD1eXLl1UXFwsSerYsWPAX2bdVhiGoTNnzqi4uFhdunRReHjz1zwLqsVajx07pvj4eOXl5emGG26QYRhKTExUZmamZs+eLUkqLy+X3W7XsmXL9Mgjj1z0nCzWCgDApTMMQy6XS6dOnTK7lKDUpUsXJSQk1BtO2+RirW63W5LUtWtXSTXve3K5XEpPT/f2sVqtGjlypHbu3FlvCCsvL1d5ebl33+PxBLhqAABCj8VikcPhUHx8vM6dO2d2OUElIiKiRSNgtYImhBmGoaysLA0fPlz9+/eXJLlcLkmS3W736Wu323X48OF6z5Odna1FixYFtlgAANqJ8PBwvwQO1BU0T0dOnz5dn376qX7/+9/XOXbhUJ9hGA3em54zZ47cbrd3KyoqCki9AAAALREUI2GPPfaYcnJytH37dvXs2dPbnpCQIKlmRMzh+O4t7MXFxXVGx2pZrVZZrdbAFgwAANBCpo6EGYah6dOn6+2339aWLVuUkpLiczwlJUUJCQnKzc31tlVUVCgvL0/Dhg1r7XIBAAD8xtSRsGnTpmndunV69913FRMT450DZrPZFB0dLYvFoszMTC1ZskSpqalKTU3VkiVL1LFjR91zzz1mlg4AANAipoawlStXSpJGjRrl07527VpNnjxZkjRr1iydPXtWU6dO1cmTJzVkyBBt3ryZNcIAAECbFlTrhAUC64QBAIDW1NTsETRPRwIAALQnhDAAAAATEMIAAABMQAgDAAAwASEMAADABIQwAAAAExDCAAAATEAIAwAAMAEhDAAAwASEMAAAABMQwgAAAExACAMAADABIQwAAMAEhDAAAAATEMIAAABM0KwQNnr0aJ06dapOu8fj0ejRo1taEwAAQMhrVgjbtm2bKioq6rSXlZVpx44dLS4KAAAg1HW4lM6ffvqp9+eCggK5XC7vflVVlTZt2qQePXr4rzoAAIAQdUkh7JprrpHFYpHFYqn3tmN0dLT+8z//02/FAQAAhKpLCmGFhYUyDEN9+vTRxx9/rO7du3uPRUZGKj4+XuHh4X4vEgAAINRcUghLTk6WJFVXVwekGAAAgPbikkLY+b744gtt27ZNxcXFdULZ/PnzW1wYAABAKGtWCPvtb3+rRx99VHFxcUpISJDFYvEes1gshDAAAICLaFYI++Uvf6mnn35as2fP9nc9AAAA7UKz1gk7efKkbr/9dn/XAgAA0G40K4Tdfvvt2rx5c4u/fPv27Ro3bpwSExNlsVj0zjvv+ByfPHmyd0mM2u0HP/hBi78XAADAbM26HXn55ZfrySef1O7duzVgwABFRET4HH/88cebdJ7S0lINHDhQDzzwgCZMmFBvn7Fjx2rt2rXe/cjIyOaUDAAAEFSaFcJefvllde7cWXl5ecrLy/M5ZrFYmhzCMjIylJGR0Wgfq9WqhISE5pQJAAAQtJoVwgoLC/1dR4O2bdum+Ph4denSRSNHjtTTTz+t+Pj4BvuXl5ervLzcu+/xeFqjTAAAgEvSrDlhrSUjI0O/+93vtGXLFj333HPas2ePRo8e7ROyLpSdnS2bzebdkpKSWrFiAACAprEYhmFc6ocefPDBRo+vWbPm0guxWLRhwwaNHz++wT5Op1PJycl64403dNttt9Xbp76RsKSkJLndbsXGxl5yXQAAAJfC4/HIZrNdNHs063bkyZMnffbPnTun/fv369SpU/W+2NtfHA6HkpOT9eWXXzbYx2q1ymq1BqwGAAAAf2hWCNuwYUOdturqak2dOlV9+vRpcVENOXHihIqKiuRwOAL2HQAAAK3Bb3PCwsLCNGPGDP3qV79q8mdOnz6t/Px85efnS6qZ8J+fn68jR47o9OnTmjlzpnbt2qVDhw5p27ZtGjdunOLi4nTrrbf6q2wAAABTNPsF3vX5xz/+ocrKyib3/+STT3TjjTd697OysiRJkyZN0sqVK/XZZ5/ptdde06lTp+RwOHTjjTdq/fr1iomJ8WfZAAAAra5ZIaw2LNUyDENOp1MbN27UpEmTmnyeUaNGqbHnAj744IPmlAcAABD0mhXC9u3b57MfFham7t2767nnnrvok5MAAABoZgjbunWrv+sAAABoV1o0J+zYsWM6ePCgLBaLrrjiCnXv3t1fdQEAAIS0Zj0dWVpaqgcffFAOh0M33HCDRowYocTERD300EM6c+aMv2sEAAAIOc0KYVlZWcrLy9N7772nU6dO6dSpU3r33XeVl5enn//85/6uEQAAIOQ067VFcXFxevPNNzVq1Cif9q1bt+qOO+7QsWPH/FVfizX11QEAAAD+0NTs0ayRsDNnzshut9dpj4+P53YkAABAEzQrhA0dOlQLFixQWVmZt+3s2bNatGiRhg4d6rfiAAAAQlWzno5csWKFMjIy1LNnTw0cOFAWi0X5+fmyWq3avHmzv2sEAAAIOc2aEybVjHy9/vrr+tvf/ibDMNSvXz/de++9io6O9neNLcKcMAAA0Jqamj2aNRKWnZ0tu92un/70pz7ta9as0bFjxzR79uzmnBYAAKDdaNacsJdeeklXXXVVnfarr75aq1atanFRAAAAoa5ZIczlcsnhcNRp7969u5xOZ4uLAgAACHXNCmFJSUn66KOP6rR/9NFHSkxMbHFRAAAAoa5Zc8IefvhhZWZm6ty5cxo9erQk6c9//rNmzZrFivkAAABN0KwQNmvWLH3zzTeaOnWqKioqJElRUVGaPXu25syZ49cCAQAAQlGzl6iQpNOnT+vAgQOKjo5WamqqrFarP2vzC5aoAAAArSmgS1TU6ty5swYPHtySUwAAALRLzZqYDwAAgJYhhAEAAJiAEAYAAGACQhgAAIAJCGEAAAAmIIQBAACYgBAGAABgAlND2Pbt2zVu3DglJibKYrHonXfe8TluGIYWLlyoxMRERUdHa9SoUfr888/NKRYAAMCPTA1hpaWlGjhwoJ5//vl6jz/zzDNavny5nn/+ee3Zs0cJCQm66aabVFJS0sqVAgAA+FeLVsxvqYyMDGVkZNR7zDAMrVixQvPmzdNtt90mSXr11Vdlt9u1bt06PfLII61ZKgAAgF8F7ZywwsJCuVwupaene9usVqtGjhypnTt3mlgZAABAy5k6EtYYl8slSbLb7T7tdrtdhw8fbvBz5eXlKi8v9+57PJ7AFAgAANACQTsSVstisfjsG4ZRp+182dnZstls3i0pKSnQJQIAAFyyoA1hCQkJkr4bEatVXFxcZ3TsfHPmzJHb7fZuRUVFAa0TAACgOYI2hKWkpCghIUG5ubnetoqKCuXl5WnYsGENfs5qtSo2NtZnAwAACDamzgk7ffq0/v73v3v3CwsLlZ+fr65du6pXr17KzMzUkiVLlJqaqtTUVC1ZskQdO3bUPffcY2LVAAAALWdqCPvkk0904403evezsrIkSZMmTdIrr7yiWbNm6ezZs5o6dapOnjypIUOGaPPmzYqJiTGrZAAAAL+wGIZhmF1EIHk8HtlsNrndbm5NAgCAgGtq9gjaOWEAAAChjBAGAABgAkIYAACACQhhAAAAJiCEAQAAmIAQBgAAYAJCGAAAgAkIYQAAACYghAEAAJiAEAYAAGACQhgAAIAJCGEA4C8VpdJCW81WUWp2NQCCHCEMAADABIQwAAAAExDCAMBfqqu++/nQTt99ALgAIayFzlRUqvcTG9X7iY0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" + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot Conditional Adjusted Predictions\n", + "\n", + "This notebook shows how to use, and the capabilities, of the `plot_predictions` function. The `plot_predictions` function is a part of Bambi's sub-package `interpret` that features a set of tools used to interpret complex regression models that is inspired by the R package [marginaleffects](https://vincentarelbundock.github.io/marginaleffects/articles/predictions.html#conditional-adjusted-predictions-plot). " ] }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plot_comparisons(\n", - " model=fish_model,\n", - " idata=fish_idata,\n", - " contrast={\"persons\": [1, 4]},\n", - " conditional={\"child\": [0, 1, 2], \"livebait\": [0, 1]},\n", - ") \n", - "fig.set_size_inches(7, 3)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The plot above shows that, comparing $4$ to $1$ persons given $0$ children and using livebait, the expected difference is about $26$ fish. When not using livebait, the expected difference decreases substantially to about $5$ fish. Using livebait with a group of people is associated with a much larger expected difference in the number of fish caught. \n", - "\n", - "`comparisons` can be called to view a summary dataframe that includes the term $w$ and its contrast, the specified `conditional` covariate, and the expected difference in the outcome with the uncertainty interval (by default the 94% highest density interval is computed). " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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termestimate_typevaluechildlivebaitcamperestimatelower_3.0%upper_97.0%
0personsdiff(1.0, 4.0)0.00.01.04.8344722.5634727.037150
1personsdiff(1.0, 4.0)0.01.01.026.42318823.73972929.072748
2personsdiff(1.0, 4.0)1.00.01.01.2020030.6316291.780965
3personsdiff(1.0, 4.0)1.01.01.06.5719435.4692757.642248
4personsdiff(1.0, 4.0)2.00.01.00.3013840.1436760.467608
5personsdiff(1.0, 4.0)2.01.01.01.6484171.1404152.187190
\n", - "
" - ], - "text/plain": [ - " term estimate_type value ... estimate lower_3.0% upper_97.0%\n", - "0 persons diff (1.0, 4.0) ... 4.834472 2.563472 7.037150\n", - "1 persons diff (1.0, 4.0) ... 26.423188 23.739729 29.072748\n", - "2 persons diff (1.0, 4.0) ... 1.202003 0.631629 1.780965\n", - "3 persons diff (1.0, 4.0) ... 6.571943 5.469275 7.642248\n", - "4 persons diff (1.0, 4.0) ... 0.301384 0.143676 0.467608\n", - "5 persons diff (1.0, 4.0) ... 1.648417 1.140415 2.187190\n", + "Based on these three specifications, the mean of the distribution of $y$, given $X$, is determined by $X\\beta: \\mathbb{E}(y|X) = g^{-1}(X\\beta)$. \n", + "\n", + "GLMs are a broad family of models where the output $y$ is typically assumed to follow an exponential family distribution, e.g., Binomial, Poisson, Gamma, Exponential, and Normal. The job of the link function is to map the linear space of the model $X\\beta$ onto the non-linear space of a parameter like $\\mu$. Commonly used link function are the _logit_ and _log_ link. Also known as the _canonical_ link functions. This brief introduction to GLMs is not meant to be exhuastive, and another good starting point is the Bambi [Basic Building Blocks](https://bambinos.github.io/bambi/notebooks/how_bambi_works.html#Link-functions) example.\n", + "\n", + "Due to the link function, there are typically three quantities of interest to interpret in a GLM:\n", + "1. the linear predictor $\\eta$\n", + "2. the mean $\\mu = g^{-1}(\\eta)$\n", + "3. the response variable $Y \\sim \\mathcal{D}(\\mu, \\theta)$ where $\\mu$ is the mean parameter and $\\theta$ is (possibly) a vector that contains all the other \"nuissance\" parameters of the distribution.\n", "\n", - "[6 rows x 9 columns]" + "As modelers, we are usually more interested in interpreting (2) and (3). However, $\\mu$ is not always on the same scale of the response variable and can be more difficult to interpret. Rather, the response scale is a more interpretable scale. Additionally, it is often the case that modelers would like to analyze how a model parameter varies across a range of explanatory variable values. To achieve such an analysis, Bambi has taken inspiration from the R package marginaleffects, and implemented a `plot_predictions` function that plots the conditional adjusted predictions to aid in the interpretation of GLMs. Below, it is briefly discussed what are conditionally adjusted predictions, how they are computed, and ultimately how to use the `plot_predictions` function." ] }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "comparisons(\n", - " model=fish_model,\n", - " idata=fish_idata,\n", - " contrast={\"persons\": [1, 4]},\n", - " conditional={\"child\": [0, 1, 2], \"livebait\": [0, 1]},\n", - ") " - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But why is `camper` also in the summary dataframe? This is because in order to peform predictions, Bambi is expecting a value for each covariate used to fit the model. Additionally, with GLM models, average predictive comparisons are conditional in the sense that the estimate depends on the values of all the covariates in the model. Thus, for unspecified covariates, `comparisons` and `plot_comparisons` computes a default value (mean or mode based on the data type of the covariate). Thus, $c$ = `child`, `livebait`, `camper`. Each row in the summary dataframe is read as \"comparing $4$ to $1$ persons conditional on $c$, the expected difference in the outcome is $y$.\"" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Multiple contrast values\n", - "\n", - "Users can also perform comparisons on multiple contrast values. For example, if we wanted to compare the number of fish caught between $(1, 2)$, $(1, 4)$, and $(2, 4)$ `persons` conditional on a range of values for `child` and `livebait`." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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termestimate_typevaluechildlivebaitcamperestimatelower_3.0%upper_97.0%
0personsdiff(1, 2)0.00.01.00.5276270.2954510.775465
1personsdiff(1, 2)0.01.01.02.8836942.6056903.177685
2personsdiff(1, 2)1.00.01.00.1313190.0673390.195132
3personsdiff(1, 2)1.01.01.00.7179650.5929680.857893
4personsdiff(1, 2)2.00.01.00.0329600.0152120.052075
5personsdiff(1, 2)2.01.01.00.1802700.1231730.244695
6personsdiff(1, 4)0.00.01.04.8344722.5634727.037150
7personsdiff(1, 4)0.01.01.026.42318823.73972929.072748
8personsdiff(1, 4)1.00.01.01.2020030.6316291.780965
9personsdiff(1, 4)1.01.01.06.5719435.4692757.642248
10personsdiff(1, 4)2.00.01.00.3013840.1436760.467608
11personsdiff(1, 4)2.01.01.01.6484171.1404152.187190
12personsdiff(2, 4)0.00.01.04.3068452.2670976.280005
13personsdiff(2, 4)0.01.01.023.53949420.99093126.240169
14personsdiff(2, 4)1.00.01.01.0706830.5659311.585718
15personsdiff(2, 4)1.01.01.05.8539784.8589576.848519
16personsdiff(2, 4)2.00.01.00.2684230.1240330.412274
17personsdiff(2, 4)2.01.01.01.4681471.0248001.960934
\n", - "
" - ], - "text/plain": [ - " term estimate_type value ... estimate lower_3.0% upper_97.0%\n", - "0 persons diff (1, 2) ... 0.527627 0.295451 0.775465\n", - "1 persons diff (1, 2) ... 2.883694 2.605690 3.177685\n", - "2 persons diff (1, 2) ... 0.131319 0.067339 0.195132\n", - "3 persons diff (1, 2) ... 0.717965 0.592968 0.857893\n", - "4 persons diff (1, 2) ... 0.032960 0.015212 0.052075\n", - "5 persons diff (1, 2) ... 0.180270 0.123173 0.244695\n", - "6 persons diff (1, 4) ... 4.834472 2.563472 7.037150\n", - "7 persons diff (1, 4) ... 26.423188 23.739729 29.072748\n", - "8 persons diff (1, 4) ... 1.202003 0.631629 1.780965\n", - "9 persons diff (1, 4) ... 6.571943 5.469275 7.642248\n", - "10 persons diff (1, 4) ... 0.301384 0.143676 0.467608\n", - "11 persons diff (1, 4) ... 1.648417 1.140415 2.187190\n", - "12 persons diff (2, 4) ... 4.306845 2.267097 6.280005\n", - "13 persons diff (2, 4) ... 23.539494 20.990931 26.240169\n", - "14 persons diff (2, 4) ... 1.070683 0.565931 1.585718\n", - "15 persons diff (2, 4) ... 5.853978 4.858957 6.848519\n", - "16 persons diff (2, 4) ... 0.268423 0.124033 0.412274\n", - "17 persons diff (2, 4) ... 1.468147 1.024800 1.960934\n", + "The objective of plotting conditional adjusted predictions is to visualize how a parameter of the (conditional) response distribution varies as a function of (some) interpolated explanatory variables. This is done by holding all other explanatory variables constant at some specified value, a _reference grid_, that may or may not correspond to actual observations in the dataset used to fit the model. By default, the `plot_predictions` function uses a grid of 200 equally spaced values between the minimum and maximum values of the specified explanatory variable as the reference grid.\n", + "\n", + "The `plot_predictions` function uses the fitted model to then compute the predicted values of the model parameter at each value of the reference grid. The `plot_predictions` function then uses these predictions to plot the model parameter as a function of (some) explanatory variable. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import arviz as az\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", "\n", - "[18 rows x 9 columns]" + "import bambi as bmb" ] }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "multiple_values = comparisons(\n", - " model=fish_model,\n", - " idata=fish_idata,\n", - " contrast={\"persons\": [1, 2, 4]},\n", - " conditional={\"child\": [0, 1, 2], \"livebait\": [0, 1]}\n", - ")\n", - "\n", - "multiple_values" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice how the contrast $w$ varies while the covariates $c$ are held constant. Currently, however, plotting multiple contrast values can be difficult to interpret since the contrast is \"abstracted\" away onto the y-axis. Thus, it would be difficult to interpret which portion of the plot corresponds to which contrast value. Therefore, it is currently recommended that if you want to plot multiple contrast values, call `comparisons` directly to obtain the summary dataframe and plot the results yourself." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Default contrast and conditional values\n", - "\n", - "Now, we move onto scenario 2 described above (grid of equally spaced and central values) in computing average predictive comparisons. You are not required to pass values for `contrast` and `conditional`. If you do not pass values, Bambi will compute default values for you. Below, it is described how these default values are computed.\n", - "\n", - "The default value for `contrast` is a _centered difference_ at the mean for a contrast variable with a numeric dtype, and _unique levels_ for a contrast varaible with a categorical dtype. For example, if the modeler is interested in the comparison of a $5$ unit increase in $w$ where $w$ is a numeric variable, Bambi computes the mean and then subtracts and adds $2.5$ units to the mean to obtain a _centered difference_. By default, if no value is passed for the contrast covariate, Bambi computes a one unit centered difference at the mean. For example, if only `contrast=\"persons\"` is passed, then $\\pm$ $0.5$ is applied to the mean of persons. If $w$ is a categorical variable, Bambi computes and returns the unique levels. For example, if $w$ has levels [\"high scool\", \"vocational\", \"university\"], Bambi computes and returns the unique values of this variable.\n", - "\n", - "The default values for `conditional` are more involved. Currently, by default, if a dict or list is passed to `conditional`, Bambi uses the ordering (keys if dict and elements if list) to determine which covariate to use as the main, group (color), and panel (facet) variable. This is the same logic used in `plot_comparisons` described above. Subsequently, the default values used for the `conditional` covariates depend on their ordering **and** dtype. Below, the psuedocode used for computing default values covariates passed to `conditional` is outlined:\n", - "\n", - "```python\n", - "if v == \"main\":\n", - " \n", - " if v == numeric:\n", - " return np.linspace(v.min(), v.max(), 50)\n", - " elif v == categorical:\n", - " return np.unique(v)\n", - "\n", - "elif v == \"group\":\n", - " \n", - " if v == numeric:\n", - " return np.quantile(v, np.linspace(0, 1, 5))\n", - " elif v == categorical:\n", - " return np.unique(v)\n", - "\n", - "elif v == \"panel\":\n", - " \n", - " if v == numeric:\n", - " return np.quantile(v, np.linspace(0, 1, 5))\n", - " elif v == categorical:\n", - " return np.unique(v)\n", - "```\n", - "\n", - "Thus, letting Bambi compute default values for `conditional` is equivalent to creating a hypothetical \"data grid\" of new values. Lets say we are interested in comparing the number of fish caught for the contrast `livebait` conditional on `persons` and `child`. This time, lets call `comparisons` first to gain an understanding of the data generating the plot." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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termestimate_typevaluepersonschildcamperestimatelower_3.0%upper_97.0%
0livebaitdiff(0.0, 1.0)1.0000000.01.01.6946461.2528032.081207
1livebaitdiff(0.0, 1.0)1.0000001.01.00.4224480.2990520.551766
2livebaitdiff(0.0, 1.0)1.0000003.01.00.0269230.0127520.043035
3livebaitdiff(0.0, 1.0)1.0612240.01.01.7874121.3429792.203158
4livebaitdiff(0.0, 1.0)1.0612241.01.00.4455550.3172530.580117
5livebaitdiff(0.0, 1.0)1.0612243.01.00.0283930.0134520.045276
6livebaitdiff(0.0, 1.0)1.1224490.01.01.8852701.4229372.313218
7livebaitdiff(0.0, 1.0)1.1224491.01.00.4699290.3353730.609249
8livebaitdiff(0.0, 1.0)1.1224493.01.00.0299440.0141650.047593
9livebaitdiff(0.0, 1.0)1.1836740.01.01.9885001.5016502.424762
\n", - "
" + "- mpg: Miles/(US) gallon\n", + "- cyl: Number of cylinders\n", + "- disp: Displacement (cu.in.)\n", + "- hp: Gross horsepower\n", + "- drat: Rear axle ratio\n", + "- wt: Weight (1000 lbs)\n", + "- qsec: 1/4 mile time\n", + "- vs: Engine (0 = V-shaped, 1 = straight)\n", + "- am: Transmission (0 = automatic, 1 = manual)\n", + "- gear: Number of forward gear" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [mpg_sigma, hp, wt, hp:wt, cyl, gear]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " 100.00% [8000/8000 00:19<00:00 Sampling 4 chains, 0 divergences]\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 20 seconds.\n" + ] + } ], - "text/plain": [ - " term estimate_type value ... estimate lower_3.0% upper_97.0%\n", - "0 livebait diff (0.0, 1.0) ... 1.694646 1.252803 2.081207\n", - "1 livebait diff (0.0, 1.0) ... 0.422448 0.299052 0.551766\n", - "2 livebait diff (0.0, 1.0) ... 0.026923 0.012752 0.043035\n", - "3 livebait diff (0.0, 1.0) ... 1.787412 1.342979 2.203158\n", - "4 livebait diff (0.0, 1.0) ... 0.445555 0.317253 0.580117\n", - "5 livebait diff (0.0, 1.0) ... 0.028393 0.013452 0.045276\n", - "6 livebait diff (0.0, 1.0) ... 1.885270 1.422937 2.313218\n", - "7 livebait diff (0.0, 1.0) ... 0.469929 0.335373 0.609249\n", - "8 livebait diff (0.0, 1.0) ... 0.029944 0.014165 0.047593\n", - "9 livebait diff (0.0, 1.0) ... 1.988500 1.501650 2.424762\n", + "source": [ + "# Load data\n", + "data = bmb.load_data('mtcars')\n", + "data[\"cyl\"] = data[\"cyl\"].replace({4: \"low\", 6: \"medium\", 8: \"high\"})\n", + "data[\"gear\"] = data[\"gear\"].replace({3: \"A\", 4: \"B\", 5: \"C\"})\n", + "data[\"cyl\"] = pd.Categorical(data[\"cyl\"], categories=[\"low\", \"medium\", \"high\"], ordered=True)\n", "\n", - "[10 rows x 9 columns]" + "# Define and fit the Bambi model\n", + "model = bmb.Model(\"mpg ~ 0 + hp * wt + cyl + gear\", data)\n", + "idata = model.fit(draws=1000, target_accept=0.95, random_seed=1234)" ] }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "contrast_df = comparisons(\n", - " model=fish_model,\n", - " idata=fish_idata,\n", - " contrast=\"livebait\",\n", - " conditional=[\"persons\", \"child\"],\n", - ")\n", - "\n", - "contrast_df.head(10)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As `livebait` was encoded as a categorical dtype, Bambi returned the unique levels of $[0, 1]$ for the contrast. `persons` and `child` were passed as the first and second element and thus act as the main and group variables, respectively. It can be see from the output above, that an equally spaced grid was used to compute the values for `persons`, whereas a quantile based grid was used for `child`. Furthermore, as `camper` was unspecified, the mode was used as the default value. Lets go ahead and plot the commparisons." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can print the Bambi model object to obtain the model components. Below, we see that the Gaussian linear model uses an identity link function that results in no transformation of the linear predictor to the mean of the outcome variable, and the distrbution of the likelihood is Gaussian." ] }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plot_comparisons(\n", - " model=fish_model,\n", - " idata=fish_idata,\n", - " contrast=\"livebait\",\n", - " conditional=[\"persons\", \"child\"],\n", - ") \n", - "fig.set_size_inches(7, 3)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The plot shows us that the expected differences in fish caught comparing a group of people who use livebait and no livebait is not only conditional on the number of persons, but also children. However, the plotted comparisons for `child` = $3$ is difficult to interpret on a single plot. Thus, it can be useful to pass specific `group` and `panel` arguments to aid in the interpretation of the plot. Therefore, `subplot_kwargs` allows the user to manipulate the plotting by passing a dictionary where the keys are `{\"main\": ..., \"group\": ..., \"panel\": ...}` and the values are the names of the covariates to be plotted. Below, we plot the same comparisons as above, but this time we specify `group` and `panel` to both be `child`." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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9Yfx/Lx/DzlO9Y27JSEREREQ0HRjKiahgNfUGcaIzgMoM55K/fLADCUUgHJchSdIUt46IiIiIaGwM5URUkFK95AlFgcU4di/5qZ4AjncFoJGAi+qKp6GFRERERERjYygnooLU3BfC8a7MesmFEHj5YCcA4KL6YjjM+qluHhERERFRRhjKiajgCCGwt7kPCVmBNYNe8iMdfpzuC0GvlbBuYdk0tJCIiIiIKDMM5URUcJp7QzjWGUBFBr3kihDYekjtJV89txR2E3vJiYiIiCh/MJQTUUFRFIFdTX2IywpsGfSS7z/jQYcvApNeg7XsJSciIiKiPMNQTkQF5VRPEMc6/ajKoJc8oSh45XAXAODKBWUwG7RT3TwiIiIionFhKCeigiErArub+iAEMlpxfXdTP/qCMdiMOqyZVzoNLSQiIiIiGh+GciIqGCe7AziR4Yrr0YSM14+oveRXLSqDQcdyR0RERET5h7+lElFBSMgKdjX2QZKQ0TD0vx3vgT+agMtqwMUNrmloIRERERHR+DGUE1FBONYZwKmezHrJfeE4/na8GwDwkfMqoNOw1BERERFRfuJvqkSU92IJBbub+qCVNDDpx+4l33q4E3FZoM5lwbIqxzS0kIiIiIhoYhjKiSjvHev0o7E3iKqisXvJ271h7G3uBwBcv6wCkiRNdfOIiIiIiCaMoZyI8lo0IePdxj4YtZoxF2sTQuCFDzogACyvdqKuxDo9jSQiIiIimiCGciLKawfbfDjdG0JlBr3kxzrV1dm1GgkfOa9iGlpHRERERDQ5DOVElLf8kTh2nuyF1aSDXjt6uZIVgRc+aAcArJ5bApfVMB1NJCIiIiKaFIZyIspbe5v70eGLoMJhGvPcPc396PJHYdZrcdUi9zS0joiIiIho8hjKiSgvdfoi2N3cjzKbEVrN6Iu1ReIyXjncCQBYv9id0T7mRERERET5gKGciPKOEAI7T/XCF46jxGYc8/zXj3QhEE2gxGrAqrmuaWghEREREVF2MJQTUd451RPEoTZfRlugdfkjeOtkDwDgoysqodOwrBERERFR4eBvr0SUV+Kygp2neiErAnaTftRzhRD46/52KAJYXGHHogrHNLWSiIiIiCg7chrKN2/ejIsvvhh2ux1utxs33XQTjh49OuwcIQTuv/9+VFVVwWw2Y926dTh48GCOWkxEU+1Qmw8nugKoLh67l/xIhx/Hk1ug3bC8chpaR0RERESUXTkN5du3b8ddd92FnTt3YuvWrUgkErj22msRDAYHzvnXf/1X/PznP8eDDz6IXbt2oaKiAtdccw38fn8OW05EUyEYTWBnYy/Mei2MutEXa4vLCv56QN0C7fL5pRnNPSciIiIiyje6XN78xRdfHPZ8y5YtcLvd2LNnD6688koIIfDAAw/g29/+Nm655RYAwKOPPory8nI8/vjj+NKXvpSLZhPRFDnQ6kVbfxjz3fYxz33zRA/6gjE4TDqsW1Q2Da0jIiIiIsq+nIbys3m9XgCAy6WuntzY2IiOjg5ce+21A+cYjUasXbsWO3bsSBvKo9EootHowHOfzzfFrSaibAhEE9jb3A+HWT/mFmieUAzbjnYBAK5bVjlmrzqpWB+JiNJjfSSiXMqbhd6EENi4cSMuv/xyLFu2DADQ0dEBACgvLx92bnl5+cBrZ9u8eTOcTufAo7a2dmobTkRZcbDVi05fBG67acxzX/igA3FZoL7EgpU1zmlo3czA+khElB7rIxHlUt6E8rvvvhv79+/H73//+3Nek6ThvWZCiHOOpdx3333wer0Dj5aWlilpLxFlTyCawJ4Me8lPdAVwoNULCcCNK6pGrAUjCcVkaLUSNGPcZyZifSQiSo/1kYhyKS+Gr99zzz147rnn8MYbb6CmpmbgeEVFBQC1x7yycnBl5a6urnN6z1OMRiOMRi74RFRIDrX50OGLYMEYc8njsoI/7WsFAKya68poH/OhQrEE2n1hXDavFBWOsXvkZxrWRyKi9FgfiSiXctpTLoTA3XffjaeffhqvvfYaGhoahr3e0NCAiooKbN26deBYLBbD9u3bsWbNmuluLhFNgWA0gd1NfXBm0Eu+/Vg3eoMx2E06XLu0Ylz3iSZknO4L4cK6Yqxb5B7zXkRERERE0yGnPeV33XUXHn/8cfzpT3+C3W4fmCfudDphNpshSRLuvfde/PjHP8aCBQuwYMEC/PjHP4bFYsFtt92Wy6YTUZYcaveh0xcZc8X1bn8U2491AwBuWF4Jkz7zxd3isoLGniCWVztx7dIKGHR5M3OHiIiIiGa5nIbyhx56CACwbt26Yce3bNmCO+64AwCwadMmhMNh3Hnnnejv78eqVavw8ssvw24fe8skIspvwWgCuxr7YDeN3ksuhMCf3m+FrAgsLLdheXXmi7vJisCp7gAWlttw3bIKmA1cqZ2IiIiI8kdOQ7kQYsxzJEnC/fffj/vvv3/qG0RE0+pQuw9d/gjmlY3+R7Z9LR6c6g5Cp5HwsZXVGS/upgiBUz0B1JVYcP3yKthN+mw0m4iIiIgoaziGk4hyIhRT55LbjKP3kodiCTx/oB0AsH6xGy6rIeN7dPujcJr1uH555biuIyIiIiKaLgzlRJQT77d40OGNoHyMVdBfOtiBYEyG227E5QtKM37/uKzAE4rh0rklqHSOb5V2IiIiIqLpwlBORNOu3RvGzlN9KLYaRu0lb+wJYldTPwDg786vhk6Teclq7Q+jvsSK82uLJttcIiIiIqIpw1BORNMqISv42/EeeMNxuO0j95LHZQVP7z0DAPhQfTEaSq0Z3yMUTUCBwOp5JeNapZ2IiIiIaLoxlBPRtHr/jBeH232oc1lGPe/Vw53oDcbgMOmwYVnluO5xxhPG0koHFpVzlwYiIiIiym8M5UQ0bXoCUbx1ogc2o27UHuwz/SH87XgPAOCm86vHtY1ZXzAGm0mHS+eWQDPK0HgiIiIionzAUE5E00JRBN483oPeQHTUxd0SioI/7j0DAWBljROLKx0Z30NWBLr8EVxUV4yqIi7uRkRERET5j6GciKbFoXYfDrR6UVtsgWaUfca3He1Gpy8Kq0GLj66oGtc9OrwRVDpN+NCc4sk2l4iIiIhoWjCUE9GU84bi+Nvxbhh1GliMuhHPa/eGse1oFwDgxpVVsI5y7tmicRmhWAKr55XCbtJPus1ERERERNOBoZyIppSiCLx5ogcd3sioQ8plReDpva1QBLC00oHl1c6M7yGEQFNfEIsr7TivKvPh7kREREREucZQTkRT6lC7D++19KN6jGHrbxzvRqsnDJNeg4+dXwVplHPP1uGLoNhiwFWL3NBrWdaIiIiIqHDwt1cimjL9wRjeONYNg1YD2yhD0Vs9Ybx6uBMAcOOKKjjGMfw8FEsgEE3gyoVlcI+ygBwRERERUT5iKCeiKSErAtuPdaPDN/qw9bis4KndLVAEsKzKgfNrizK+hyIEWvpCWFlThJU1mV9HRERERJQvGMqJaEocaPVi/xkP6lyjD1t/+WAHuvxR2I06/N351eMatt7mCaPcYcKVC8ug5Z7kRERERFSAGMqJKOu6/VG8cawbFoMOFsPIw9ZPdgfw1sleAMDNF1aPa7V1fySOmKzgyoVlcFkNk24zEREREVEuMJQTUVYlZAXbj3WhNxBFhXPkOd6RuIw/7DkDALh4jguLKzJfNV1WBNo8YVxQW4yllVxtnYiIiIgKF0M5EWXV3tMeHGzzob7EOuqw9T+/3wZvOA6X1YDrl1eM6x6n+0KocVlwxcJSaDhsnYiIiIgKGEM5EWXN8U4/th3tgtOsh0mvHfG8A61evNfigQTg1otqYNSNfO7Z+kMxaDXAVYvKxrVKOxERERFRPmIoJ6Ks6PBG8NLBDiiKgNs+8rD1/lAMz7ynDlu/cmEZ6kusGd8jllDQ5Yvg0oYSzHfbJ91mIiIiIqJcYygnoknzReJ44YN29AZjqHFZRjxPVgSe3NWCSFxBbbEZH15SPq77NPcFsaDcjkvnlUy2yUREREREeYGhnIgmJZqQsfVgBxp7gmgoHX0e+WtHOnG6LwSjToOPX1w3rm3MOn0ROEx6rF/sHnVoPBERERFRIWEoJ6IJUxSBvx3rxv4zPswpsUKnGbmknOoOYNvRbgDAzRdUj2sbs3BMhi8cxxULSlFVZJ50u4mIiIiI8gVDORFN2Hst/Xj7VB8qnaZRe6+D0QT+Z3cLBIAP1RdjRU1RxvdQhMDpviCW1zhxfm3m1xERERERFQKGciKakKaeIF4/2g2bUQeHeeRV0IUQ+OPeM/BFEiizGfHRFVXjus+ZvhDKHSasW+SGTsuSRUREREQzC3/DJaJx84Ri2Hq4E5G4jHLHyCutA8COk7040uGHViPhHy+phUGXednpD8UgJODqJeXjGu5ORERERFQoGMqJaFxiCQWvHu5ES18I9a7RtzNr7g3ihQ/aAQDXL6tApTPz+eDRhIwuXwSrG0qwqILbnxERERHRzMRQTkTj8vbJHuxv9WJOiXXU1dMD0QR+/+5pKAJYUePEpXMz38ZMEQJNvUEsqXRg9Xxuf0ZEREREMxdDORFl7FCbDztO9sJtG31hN0UIPLnr9MA88psvqIY0ylZpZ2vtD6PMZsTVS8ph1HH7MyIiIiKauRjKiSgjnb4IXjvSCY0koXiM+d2vHu7Eye4gDFoNbltVN65g7Q3HISsC6xeXo8xunGyziYiIiIjy2oRC+fr16+HxeM457vP5sH79+ozf54033sCNN96IqqoqSJKEZ599dtjrd9xxByRJGva49NJLJ9JkIpqEcEzG1kMd6A3EUFM8+rzwox0+vJ7cj/ymC6rHXAhuqFhCQbs3jFVzXVhSyXnkZ8tW7SUimmlYH4mokE0olG/btg2xWOyc45FIBH/7298yfp9gMIiVK1fiwQcfHPGc6667Du3t7QOP559/fiJNJqIJUhSB7ce6cKwzgDml1lGHofcHY/if3WcAAKsaXOPeV/x0XxCLKuy4bH7puIa7zxbZqr1ERDMN6yMRFTLdeE7ev3//wPeHDh1CR0fHwHNZlvHiiy+iuro64/fbsGEDNmzYMOo5RqMRFRUV42kmEWXR/lYvdjX1o7rIDP0o+4THEgoee6cZ4biMmmIzblheOa779AaisBh0uHJB2ajz1WejbNdeIqKZgvWRiGaCcYXy888/f2AYebqhQGazGf/+7/+etcYB6l8+3W43ioqKsHbtWvzoRz+C2+0e8fxoNIpoNDrw3OfzZbU9RLNJqyeM1490wWLQwm7Sj3ieEAJ/3HsG7d4IrEYdbrukDrpRAvzZ4rKCnkAUVy8pR63Lko2mzyjZqr2sj0Q007A+EtFMMK5Q3tjYCCEE5s6di3fffRdlZWUDrxkMBrjdbmi12evh2rBhA2699VbU19ejsbER//Iv/4L169djz549MBrTLwC1efNmfP/7389aG4hmq0A0ga0HO+CPxDGvzDbquW8c68aBVi80EnDbJXUosoy+ENzZWvpCmFtmwyUNrsk0ecbKVu1lfSSimYb1kYhmAkkIIXLdCACQJAnPPPMMbrrpphHPaW9vR319PZ544gnccsstac9J95fO2tpaeL1eOByObDebaEZSFIHnP2jHu419mF9mG7XX+2iHD799uxkCwN+dX4VVDePbV9wTisEfieMfLq5DQ6l1ki2fGj6fD06ns+DrCOsjEWUb6yMRUXrjqY/j6ikf6tixY9i2bRu6urqgKMqw17773e9O9G1HVVlZifr6ehw/fnzEc4xG44i96ESUmb2n+7GnuR81xZZRA3m3P4ondrVAALikwTXuQJ5QFHT4IrhyYVneBvJ8M5nay/pIRDMZ6yMRFaoJhfL//M//xJe//GWUlpaioqJi2CrJkiRNWSjv7e1FS0sLKivHt4AUEWXug1YvXj3SBYdJD5tx5BIRicv43c5mRBMK6kss+OiK8f//8kxfGHUuC1bPHV+Yn61yVXuJiPId6yMRFbIJhfIf/vCH+NGPfoRvfvObk7p5IBDAiRMnBp43NjZi3759cLlccLlcuP/++/H3f//3qKysRFNTE771rW+htLQUN99886TuS0TpHenw4cUPOqCRpFH3F5cVgd+/exo9gSicZr26sJtmfDss+sJxAMCVC8tgHSX806Bs1V4iopmG9ZGICtmEfhPu7+/HrbfeOumb7969G1ddddXA840bNwIAbr/9djz00EM4cOAAfvvb38Lj8aCyshJXXXUVnnzySdjt9knfm4iGO9EVwAsHOiArYtQV0IUQ+PP+NhzvCkCvlfCpS+tHXZk9nXBMRrs3jDXzS7HAPfoicjQoW7WXiGimYX0kokI2vq6tpFtvvRUvv/zypG++bt06CCHOeTzyyCMwm8146aWX0NXVhVgshubmZjzyyCOora2d9H2JaLimniD+eqANkbg85pZkb53owbuNfZAAfPxDdaguMo/rXrGEgua+IC6oK8a6RWXDhhjS6LJVe4mIZhrWRyIqZBPqKZ8/fz7+5V/+BTt37sTy5cuh1w/vJfvKV76SlcYR0dRr6QvhL/vbEIgkMKdk9MXWDrV58cIHHQCADcsrsbRqfCvSJhQFp3oCWFLpwDVLy2HUZW8LxdmAtZeIKD3WRyIqZBPaEq2hoWHkN5QknDp1alKNyqaZslUH0VTo8kfw9N5WdPujaCi1QjNKr/WZ/hD+82+nEJcFVjW48LGVVePq5VaEwImuAOpLLLjlgho4LeMb8p5L+VJHsl178+XnIqLClS91hPWRiPLNlG+J1tjYOKGGEVH+8Ibi+Ov+dnR4I5jvto0ayD2hGH73djPissDCchs+umJ8gVwIgcaeICqdJtyworKgAnk+Ye0lIkqP9ZGICtmE5pQTUWELx2S88EE7mnqCmFs2eg95KJbAIzua4I8mUOEw4R8vroNWM7554Kf7QnCa9bh+eSXc9pFXdSciIiIimm0m1FP+uc99btTX/+u//mtCjSGiqReXFbx8qAOH2n2YW2obdSuzuKzgd283o8sfhcOkw2dW18OkH9888Nb+MAw6Da5fXjHmInI0OtZeIqL0WB+JqJBNeEu0oeLxOD744AN4PB6sX78+Kw0jouxTFIHtR7vw3ul+1LksMOhGDuSyIvDEu6fR3BeCSa/BHWsaUGQxjOt+Hb4IFAhcv6wS893cynCyWHuJiNJjfSSiQjahUP7MM8+cc0xRFNx5552YO3fupBtFRFPjncZe7DjZiwqHGRbDyP/3F0LgT/tacbjDD51GwqcvnYMK5/iGnfcGoojEZFy3vALnVTkn23QCay8R0UhYH4mokGVtTrlGo8H/+T//B//3//7fbL0lEWXR3tP9eP1oN4osBjjMoy+09srhTuxu7ocE4B8vrkVD6ehbpZ3NE4rBE4rjqsVluLCueBKtprGw9hIRpcf6SESFIqsLvZ08eRKJRCKbb0lEWfBBqxdbD3XCpNei1GYc9dy3T/bg9aPdAIC/O78aS8fZy+2LxNHtj+LKhaVY1VAy4TZT5lh7iYjSY30kokIwoeHrGzduHPZcCIH29nb89a9/xe23356VhhFRdhzv9OOlgx3QQEKFY/Qh6Hub+/Hn/e0AgKsXu3FJg2tc9+oLxtAbiGLNvBJcvqAMmnGu0k6jY+0lIkqP9ZGICtmEQvl777037LlGo0FZWRl+9rOfjbn6JRFNn6aeIP56oB2xhIL6ktGHoB9o9eKPe88AANbMK8H6xe5x3avdG0YkLmP9YjfWzC8d97ZpNDbWXiKi9FgfiaiQTSiUv/7669luBxFlWasnjL/ub0cwmsCcMQL5kQ4fntx1GgLAh+qLccPySkij7F0+lCIETveGYNBrcMOKKqyscWZ8LY0Pay8RUXqsj0RUyCYUylO6u7tx9OhRSJKEhQsXoqysLFvtIqJJ6PRF8Jf9begNRjG3zDZqSD7ZHcDj75yGIoAVNU7cdEF1xqFaVgQaewJwWQ24blkl5rtt2foRaBSsvURE6bE+ElEhmtBCb8FgEJ/73OdQWVmJK6+8EldccQWqqqrw+c9/HqFQKNttJKJx6PJF8Nz7bejwRDC3zAbNKAH7dG8Qv3u7GQlFYEmlA7deVDvq+UPFZQUnugOoLjbj7y+qYSCfBqy9RETpsT4SUSGbUCjfuHEjtm/fjj//+c/weDzweDz405/+hO3bt+NrX/tatttIRBnq9kfx3PttaO0PZxTIt+xoQkxWsMBtwycurs14Hng0IeNkdwAL3FbcckENKp3mbP0INArWXiKi9FgfiaiQSUIIMd6LSktL8Yc//AHr1q0bdvz111/HP/zDP6C7uztb7Zs0n88Hp9MJr9cLh8OR6+YQTZneQBR/2teG030hzCuzjRqwm3uDeGRHE6IJBQ2lVty+eg4Musz+RheJy2jqDeK8Kgc2LK+EwzT6nuczQb7UkWzX3nz5uYiocOVLHWF9JKJ8M546MqE55aFQCOXl5eccd7vdHCJElAN9wRiee78NzX1BzC+zjxnIt+xoQiyhYG6pFZ8ZRyAPRhNo6Q/h/NoiXLesAhbDpJaloHFi7SUiSo/1kYgK2YSGr69evRrf+973EIlEBo6Fw2F8//vfx+rVq7PWOCIaW18whr+834am3rF7yJt6hgTysvEFcn8kjlZPCJc0uHD98koG8hxg7SUiSo/1kYgK2YR+q37ggQewYcMG1NTUYOXKlZAkCfv27YPRaMTLL7+c7TYS0QhSq6yfTgZynWbkgN3YE8SjyTnk88ts+NSl9RkHcl84jk5/BGvmleKqxW7otRP6ex5NEmsvEVF6rI9EVMgmNKccUP/6+Nhjj+HIkSMQQmDp0qX45Cc/CbM5vxZ84pwgmqla+kJ4/kA7OrzqKuuj9ZAf7/TjsXeaEZcF5rtt+PSl9RkHa284ji5fBJfNL8G6RW7oZmEgz6c6ks3am08/FxEVpnyqI6yPRJRPpnxO+ebNm1FeXo5/+qd/Gnb8v/7rv9Dd3Y1vfvObE3lbIsrQqe4Anj/Qjv5QHPPco6+yfrDNiyd2tUBWBBaW2/DJVeMP5JcvKMW6Re6MV2enqcHaS0SUHusjERWyCXV5/frXv8bixYvPOX7eeefhP/7jPybdKCIa2ZEOH57b1wZfJIG5pdZRA/ne0/34/bunISsCy6qd+NQ4esg9oRi6/BFcsbCMgTxPsPYSEaXH+khEhWxCPeUdHR2orKw853hZWRna29sn3SgiSm//GQ9ePtgJWRGYU2Id9dy3T/Xiz++3AQAuqivGzRdWjxrgh/KEYugORLF2QRmuWFjGQJ4nWHuJiNJjfSSiQjahnvLa2lq89dZb5xx/6623UFVVNelGEdFwQgjsae7D8wfUXyxqXZZRz912tGsgkK+ZVzKuQN7hjaA3GMO6hWW4koE8r7D2EhGlx/pIRIVsQj3lX/jCF3DvvfciHo9j/fr1AIBXX30VmzZtwte+9rWsNpBothNCYOepXrx+pBtmgxblDtOI5ypC4C/727HzVC8AYP1iN65e7IaUQSCXFYHm3iDMBi0+uqISK2uKoGEgzyusvURE6bE+ElEhm1Ao37RpE/r6+nDnnXciFosBAEwmE775zW/ivvvuy2oDiWYzRRF480QPth/rhtOsR6nNOOK5cVnB/+xuwcE2HyQA1y+vxGXzSzO6TyQuo6k3iFqXBR9ZWoG6kpF74il3WHuJiNJjfSSiQjbhLdEAIBAI4PDhwzCbzViwYAGMxpEDQ65wSwsqVAlZwRvHe/C3490otRpRbDWMeG44JuN3O5vQ1BuCViPh1otqsKKmKKP7eEIxdPgiWF7txDVLy1FkGfk+s1W+1ZFs1d58+7mIqPDkWx1hfSSifDHlW6Kl2Gw2XHzxxZN5CyJKIxyT8eqRTuxu6ke5wwSnWT/iuZ5QDI/saEKXPwqTXoNPrarH3DJbRvfp9EUQjCWwbpEbl88vhUE3+/YgL0SsvURE6bE+ElEhyulv4G+88QZuvPFGVFVVQZIkPPvss8NeF0Lg/vvvR1VVFcxmM9atW4eDBw/mprFE06Q/GMOz+1rx7qk+VBeZRw3krZ4w/mP7SXT5o3CYdPjiFfMyDuRn+kOIyQo2LKvAVYvKGMiJiIiIiHIgp7+FB4NBrFy5Eg8++GDa1//1X/8VP//5z/Hggw9i165dqKiowDXXXAO/3z/NLSWaHmf6Q/jD3jM40u7D3DIbrMaRB7McavPi4TdOwhdJwG034n+vnYcK58iLwKUoQqCxJwC9VoOPrazCRfWujBaCIyIiIiKi7JvU8PXJ2rBhAzZs2JD2NSEEHnjgAXz729/GLbfcAgB49NFHUV5ejscffxxf+tKXprOpRFPucLsPLx3sgD8cx3y3fcStyIQQ+NvxHrx0sAMCwAK3DZ+4pA4mvXbMe8iKwKmeAMpsRly/vBJzSkff65yIiIiIiKZWTkP5aBobG9HR0YFrr7124JjRaMTatWuxY8eOEUN5NBpFNBodeO7z+aa8rUSTkZAV7Grqw/Zj3ZAgYW6ZbcSe64Si4E/72rCnuR8AsKrBhY+uqMpoL/FYQkFjbxB1LjNuWF6VUa86zSysj0RE6bE+ElEu5e0k0o6ODgBAeXn5sOPl5eUDr6WzefNmOJ3OgUdtbe2UtpNoMgLRBJ4/0I6XD3XCatCh1mUZMZAHowlseasJe5r7IQH46IpKfGxlZoE8GE3gVE8AiyvsuOWCGgbyWYr1kYgoPdZHIsqlvA3lKWcHFCHEqPNf77vvPni93oFHS0vLVDeRaEI6vBE8vecMdjf3o9ppRskoe5C3ecL45bYTaOwJwqDT4DOr67FmXmlGc8H7gjG0ecO4pMGFm86vHnVrNZrZWB+JiNJjfSSiXMrb4esVFRUA1B7zysrKgeNdXV3n9J4PZTQa83K/dKKhDrX58OqRTvQFYphfZoNOO/Lfx95v8eDp984gLguUWA341KX1KHdk1tPd5gkjJitYv8iN1fNKRr0PzXysj0RE6bE+ElEu5e1v6A0NDaioqMDWrVsHjsViMWzfvh1r1qzJYcuIJi6akLH9aBf+tK8V4ZiM+e6RA7msCDx/oB1P7m5BXBZYWG7DnevmZxTIFSFwqjsArUbCjSuqcMXCMgZyIiIiIqI8lNOe8kAggBMnTgw8b2xsxL59++ByuVBXV4d7770XP/7xj7FgwQIsWLAAP/7xj2GxWHDbbbflsNVEE9Ptj+LVw5043O6H224cdRh5IJrA/+xqwYnuAABg7cIyXLO0HJoMhqvHEgoaewKoKjZjw3mVqCuxZO1nICIiIiKi7MppKN+9ezeuuuqqgecbN24EANx+++145JFHsGnTJoTDYdx5553o7+/HqlWr8PLLL8Nut+eqyUTjJoTAoXYfXj/She5AFHNKrTDqRt6+rLk3iN+/exq+SAJ6rYT/dVEtllc7M7pXKJbA6b4QFlXYcd15FaPOUyciIiIiotzLaShft24dhBAjvi5JEu6//37cf//909cooiyKxGW8ebwb7zT2Qa/VYIHbPmJvtxACb55Q9x9XBFBqM+K2VXWoyHD+eH8ohm5fFBfVF+OapeWwGPJ2yQgiIiIiIkrib+1EU6SlL4RtR7twoiuACqcZTrN+xHPDMRl/2HsGh9vVfVFX1Dhx8wXVo/aoD9XuDSMcl3HV4jJcNr+U88eJiIiIiAoEQzlRlsUSCvY092HHyV4EownMLbNBP0pIbukL4Yldp9EfikOrkXDD8kqsanBltN1ZQlHQ3BOExajDR1dUYWWNM6PriIiIiIgoPzCUE2VRlz+CbUe7cajNh2KLAfPdI69/oAiBN45145XDnVAEUGzR47ZL6lFdbM7oXoFoAi19Icwts+LDS8pR6+KCbkREREREhYahnCgLYgkFB1q9ePNEN/qCMcxxWWHUjzz03BuO4392t6CxJwgAWF7txE3nV8NsGHu4uhACHb4IQjEZl84twdpFZbAZ+X9lIiIiIqJCxN/kiSapsSeIHSd6cLwrALtJh4Vu+6hDyA+2efH03laE4zIMWg0+trIKF9QVZTTsPC4raOoNwmnS48aVVVhR7YRGw+HqRERERESFiqGcaIL6gzG809iLfS0exGWB+hLLqAuzReIy/rK/HXtP9wMAqovM+PjFtSjNcNsyfySOVk8YC9w2fHhpOSqdmQ1zJyIiIiKi/MVQTjROsiLw/hkPdpzoQZc/isoxVlYHgJPdAfxxzxl4wnFIAK5YUIoPLy2HTpPZKunt3jBCURlr5pXgyoVl3O6MiIiIiGiG4G/2ROPQH4xh+7FuvH/GA6tBh4XlI+87DqhzzV862IG3T/UCAFxWA/7XhTWYU2rN6H4JRUFTTxB2kx4fXVmJlTVFHK5ORERERDSDMJQTZUAIgUPtPmw/2o0OXwR1LsuYvdVNPUE8/d4Z9ARiAIBLGlzYsKwi473HA9EEzvSH0FBqxTVLy1FTzNXViYiIiIhmGoZyojEEogm8ebwbu5v7oddoxuwdj8ZlvHSoAztP9QEAHCYdbrmwBgvLR94ebShFCLT2hxFXFKxq4OrqREREREQzGX/TJxqBoggc7vBhx8letPSFUFVkhsM0+tzxox1+PLuvFd5wHABwUX0xrl9WmdFWZwAQTPaOVzhNuGJBGZZWOjhcnYiIiIhoBmMoJ0qj0xfBWyd6cLDNB71Gwny3bdRF2YLRBJ4/0I73WjwAgGKLHjdfUIP5bltG91OEQLsngkhCxoX1xVi7sAxFFkM2fhQiIiIiIspjDOVEQ4RjMnY39WFXcx+84Thqi0efO64IgT3N/Xjxgw6E4zIkAGvmleCapRUw6DJbWT0QTaC1PwS33YSPLCvHsiruPU5ERERENFswlBNB3ebsSIcPO0/1ork3hFKrEYvKHaNe0+4N40/72nC6LwQAqHCYcPMF1ah1ZbYgm6wInOkPQRYCH5pTjMvml8FlZe84EREREdFswlBOs16rJ4wdJ3pwpMMPnUbC/DIbdNqRe7mjcRmvHunCjpM9UARg0Gnw4SXlWD23BNoMe7g9oRg6fBHUFltw+YJSLCq3s3eciIiIiGgWYiinWcsbjmN3Ux/eO+1BMJpATbFl1AXZFCGw77QHLx3sgD+aAAAsq3LghhVVcJpHXwAuJZZQ0NIfgkEr4coFpVg1twT2MRaPIyIiIiKimYuhnGYdWRE42ObFm8d70OGLoNxuQlWRedRrWvpC+PP+NpzpDwMASqwGfHRFFRZVZL7NWacvAn8kgfluGy6bX4qGUuukfxYiIiIiIipsDOU0q3T5I3jzeA8+aPXCpNeOuee4LxzHSwc7BlZVN+g0WL/IjTXzSkYd4n72e7R5w3DbjVi3shLLq4syXgSOiIho2gR7gDO7gbJFgKsh160hIpo1GMppVoglFOxr8WDHiR70h2OoK7aOOlQ9GpfxxvEevHmiG3FZAAAurCvGR84rz3i4eTQu44wnDJ1Wwpp5JVjVUIJiLuRGRET5Jh4BWncDTW8CgS7A4mIoJyKaRgzlNKMpisCpngB2nurFia4gnGY9FrrtkEboHZcVgd3NfXj1cBcCyXnjdS4LblheOa5V1ds8YUQSMua7bVg9twRzyzLbr5yIiGjaKArQfQRofAPoOwmYiwENfzUkIppurLw0Y7X0hfBuUx8Ot/sAAHNKrCMOGxdC4HC7Dy8d7ER3IApAnTf+kfMqcF6VY8QQf/Z79ARi6A1GUV1sxuq5FVhS6YA+w2HuRERE08Z7Bmh+C2jfD0ACShYAWj0Q8ea6ZUREsw5DOc043f4odjf1YX+rF5GYjOoiMyzG9P/UhRA40R3A1kOdA4u4WQxarF/sxiUNLug0YwdqRQj0B2PoCUZRZDbg2qXluKCuGNYR7klERJQzoT7g9DtA6y4gFgScNYCBo7mIiHKJqYFmjG5/FO+39GN/qxfeUBwVTjNqi0cect7cG8TLhzrR2BMEABi0GqyZV4IrF5bBpB95vnmKIgR6/FH0h2IoshiwdqEbK2qcKLUZs/YzERERZUUsCLTuBZp3AKEewFYOOGtz3SoiIgJDOc0AXb4I3m/xYH+rF75wHKU2IxaWjzxv/HRfCK8d6cSxzgAAQKuRsKrBhbULyzJaxE0RAt3+KDyhGEpsBly9pBzLqp1wcRE3IiLKN/EI0PmBGsa9Leq88bLFgMSpVURE+YKhnApWmyeM/Wc8+KDNB384jjK7CQvLTSOG8ebeIF470oXjXWoY10jqiurrF7tRZMksUPvCcbR7w3DZDLhuWQWWVjnhNGe2GjsREdG0ScSAroNA89uApxnQW4DShVzIjYgoD7EyU0FRFIHmvhDeb/HgWKcfwZgMt82Iqgpz2vOFEGjqDeH1I1040T0Yxi+oLca6RWUoyXCoeTQu40x/CHqdBqvmluDSuSXsGSciovwjx9UV1Zt3qiuq64yAa566iNtY+psBoUx9G4mIaBiGcioIcVnBia4A9rV40NgdREJR4LabUDPCnHFFCBzt8GP7sW6c7gsBGOwZX7fInXGgjssKOn0RhOPq9maXzSvFnFJr1n4uIiKirIiHga5DQMsuoL9JDeHFc9RQPhoh1BB/4hU1xM//8HS0loiIhmAop7wWTcg41hHAnuY+tPSHoJEklDtMsBjS/9OVFYH9ZzzYfqwbXX51azOdRsKFdcVYu7AMxeMI4x1eNYxXF5nxkQYXllZxezMiIsozUT/QeRBoeQfwtgF6Y4ZhXAHa3wdOvAr4zqjHJC23RCMiygGGcspLkbiMw+0+7G3uxxlPGAatBjXFFhh16VdFD8dk7Grqw9uneuENxwEARp0Gl84twZp5JRkt4AaoYbzdG0EkLqO22IyL5riwuMKe0WrsRERE00JR1CDddVjdZzzYBRjtQMl8QDvGr3aJqBrgG7cDoV71mNYA1K0G5q4DAp1T3nwiIhour0P5/fffj+9///vDjpWXl6OjoyNHLaKp1hOI4nhnAPvPeNDujcCs12JOiXXEHuq+YAxvnezBnqZ+xGR1HpzVqMPl80qwam5JxmE6mpDR4Y0gllBQ4zLjQ/UuLGIYJyKifBINAD3H1B7u/iZ1yLq5GChdBGjG+O9V2AM0vaGuwp6IqMf0FmDOFUDDlYAhOTWLoZyIaNrldSgHgPPOOw+vvPLKwHOtliFppoklFDT3BnGo3YfjXQH4w3HYTXrMLbVClyaMCyFwqieIt0/24nC7DyJ53G034vL5pVhZW5TxMPNwTEaHLwxFCNS5rLiovhgLym0j9sgTERFNu0A30HEAaN0DBLvVoek2N2CwjX6dEGp4b/ob0L5vcBE3axnQsBaovUTtJSciopzK+1Cu0+lQUVGR62bQFOgJRHGyK4D9rV50eCIQECi1GVHpSL+tWTQu470WD94+1Yvu5HxxAFhYbsNl80sxv8w24nZoZwvHZLR5w5AAzC2z4cL6Iswrs3HOOBER5Qch1H3F2/apgTziAcyu5LZmY/zhWI4Bbe+pYdx7ZvB4yXx1iLp7KfcpJyLKI3kfyo8fP46qqioYjUasWrUKP/7xjzF37twRz49Go4hGBwObz+ebjmZShqIJGU09IRxu9+FEdwC+ZK94rcsCgy79LwgdvgjebezDe6f7EU2of+U3aDW4oK4Il84tQbnDlPH9YwkFrZ4whBBY4LbhQ3NcaCi1QqvJLMwTFTLWR6ICkIgBvSfUIeo9R4FYSO0VL1sCjPWH52C3ui95yztAPKge0+iA6gvVYerO2qlvf4FifSSiXMrrUL5q1Sr89re/xcKFC9HZ2Ykf/vCHWLNmDQ4ePIiSkpK012zevPmceeiUW7GEgjZPGM29QRzu8KPLF4EkSaP2iscSCg60erGrqW9gSzMAKLUZcelcFy6sKx7XfO9EagG3hIyGEitWzS3BfLeNYZxmFdZHojwW6FZDeOtewN+uHrNXAkX1o1+nJNSe9NNvq/PNU8zFQP3lQN2lg/PFaUSsj0SUS5IQQox9Wn4IBoOYN28eNm3ahI0bN6Y9J91fOmtra+H1euFwOKarqbNeLKHgTH8Izb0hHOn0o9cfRUIRsJt0KLUZ0w4TF0KgzRPBntN92NfiQSSu9oprJGBJpQOXNLjGNUQdUFdx7/JFEEkoqC42Y1WDC4srHCP2yhOl4/P54HQ6C76OsD4S5Rk5AfSdAjr2A11H1CHqJqfaMz7WXO9Ap9oj3vIuEAskD0qAe4m6knr5eRMbot59BFj29+p88wywPhIRpTee+pjXPeVns1qtWL58OY4fPz7iOUajEUbjGHtz0pRQFIE2bxinutVF27r9UciKAofZgOpi84iLpwWiCexr8WBvcz86fJGB4y6rARfXF+PC+uKMtzQD1HDviyTQ7Y9AI0modVmwsrYIi8rtMBu4gBvNXqyPRHkiFlLD75k9gKdJ7e22lQOOqtGHqMfD6lzxlncAT/PgcaMTqFsF1F4KWFwTa5MQ6h8F5PjEri9wrI9ElEsFFcqj0SgOHz6MK664ItdNoSQhBHoCMXX19DYfWr1hROIyHCY9aotHnicelxUc7fDjvRYPjnb4oCTHa+g0EpZWOXBRfTHmldmgGUeveFxW0BOIDsxTX1lbhGXVTtS7LGlXcSciIpo2igIEOtQh5q171Z5urRFw1AD6UdZGUWT1mjO71R51JRmaJY26YFvtKvXrWIu/jUSOqW2J+gFTkTrkvbhhYu9FREQTkteh/Otf/zpuvPFG1NXVoaurCz/84Q/h8/lw++2357pps1oqiJ/uC+Fohx9tnjD8kTgsBh3KbEZYDOn/WSlCoKk3iH2nPfigzTswPB0AaorNuLCuGCtrisbVm60IAV84jp5AFJIElNpNWNXgwsIKO9z2zBeAIyIiyjo5AfjbgP5moPOQGspjATX8lsxXF2FLRwh11fTW3UDbXjUwp9gq1KHl1R8CTBMcVi0Udd/yYLfaM++oAuZdDZQtAqylE3tPIiKasLwO5WfOnMEnPvEJ9PT0oKysDJdeeil27tyJ+voxFj2hrFMUge5AFGf6QzjWGUBrfxj+aBwmnRYuqwGVzvQLtgkh0OoJY/8ZLw60euENDw6Lc5r1WFlThAvqisa1gjqgbmnWE4giGEuo71NbhMUVDswptXCPcSIiyh05rgbq/iag8yAQ6AISYXVPcYsLKKob+Vp/hzo8vf099boUgxWouhCovkhd+G0co8iGiQXUBeUSEcBcBNR8SJ177poH6LhfORFRruR1KH/iiSdy3YRZLS4raPdEkkHcj05/FKFoAkadFi7b6EG83RvBgVY1iPcFYwOvmfQaLKty4vy6IswpsY57eHpvMAZvKAaTXotKpwnLqt2YW2aDy8pfJoiIKEcSMXVP8b5GoOuQGqjlGGC0A/ZyQG8Z+dpgjxrE295Te9VTNHqgYpnaI162eOLD0+MRINSt9rbrLUBxPVCxAiiZN/H550RElFV5Hcpp+sVldfuypp4gjnT40ROIIi4rsBh0cFkMqC1O/4uFIgRa+8P4oM2Lg22+YUFcr5WwuMKBFTVOLCy3p115fSSKEPCG4+gNqCuiltqMuGixGsSri8zQcEszIiLKhYgX8LSoQ9N7jgKhXrWX3OQEnNWAboQRYEKoc7jb3wc63gd8Q4K4pFWHkFddAJQvH32u+WgSUbU9Ea/aA26rABquVIfM26sADddZISLKJwzlNNAj3twXxOHkqulxWd2+rMpphnGE/cBlRaCxR73mYJsXvkhi4DWdRsKiCjuWVzsntAVZKJZATyCGSCwBh0WP82uLsKjCgfoSy7j2JyciIsoKRQGCXWoQ7zkGeE6rq5VLkrr6ubMW0I2werdQ1Os6P1DDeHDI0HRJo4blqguBiuUT31M8HgJCfWqPuFYPWMvUReBK5qlt0/JXPiKifMUKPUtFEzJa+8No6UvtIx5DNCHDbtKPGsTDMRnHuvw43O7DsU7/sMXaDDoNFlfYcV6VEwvLbeOe2x2MJtAfiiEUk2HUa1DtNOO8aifmllpRzOHpREQ03VLzw70tQOfhwYXatEbAXAyULBh5WLkcA3qOq0G88yAQ9Q2+ptECpYuAypXqnG6DbfxtU2S1LWEPEA8COjNgKwPq1gCuOck/EvC/nUREhYChfBYJRBNo7Q/jdG8Qx7sC6A3GkFAUOEx6VDhNaXughRDo8kdxtMOPo51+NPcGB7YvAwCrQYvFlQ6cV+nAPLdt3EPT1SAeRziWgMWgRbnDhEUVDtS6zKhycng6ERFNs9T88N5TQPchdWE0Oab2YJuL1bA70noo4X51TnnXITWQy4NTuaAzAmVL1N5w93njH5ouFLUXPOJVe8UBdc66oxpwL1YXkHNUs0eciKgAsXLPYIoi0BOMos0TwanuAE73hdTVzwXgMI+8j3gkLqOxJ4hjnWoQ94Tiw14vsxuxpMKBpZV21Lgs41qsTVbUOeKeUAwJRcBm1KG22IyFFXbUFJtRbjcxiBMR0fSKBQFvK9B3Sg3UoR51O7Ox5ocrCXVxt+4j6nX+9uGvm4qA8mXqgm2u+eMPzIqsDpEP9an7kxud6vzw0gWAvUJ9mIsnvho7ERHlBYbyGURRBPpCMXT6Imj3RtDYE0R/MIZQLAGdRoMiiwFzS23QnhV6FSHQ5gnjWGcAJ7r8ON0XGtYbrtNImFtmxcJyOxaV21FiG2HOXLo2CYFANAF/JIFgNAFJApwmPZZWOTCvzIbqYjNKrIa0q7gTERFNidT8cG8r0HsS6G9Uw69QkvPD69LPDxdCva77CNB9FOg9Mbw3HBJQPAdwLwXcS9Se6/H+9y0RUYekR7wAhBrsK1eqQbyoXl0xnf/NJCKaURjKC1wkLqPdq25bdrwzgL5gDIFk+LUZdSiyGFBdZB4WeoUQ6PZHcbI7gJPdQZzqCQybGw4AJVYD5rttWFRhx9xSW8YLtQkhEIrJ8IbjajsAWE06lFgNuLi+GBVFJlQXmWE36bP5MRAREY0u4lNXOveeUYeWB7vUHnKNTu1tLp6bvic77AF6j6nX9BxLhuUhDDZ1yzL3EvXreBdqU2T1PSMeddV0nUFtT91qoGSuOizd5JzoT01ERAWAobyAyIpAIJKALxKHNxzH6b4QTnYH4AnFISsCVoMODrMOFU7TsCHl6rxwtec89fAPWSkdAIw6DeaV2bCg3IYFbvu49v1OyAp8kURySLoCs0GHUpsBF88pRrnThHK7CUUWPXvDiYho+kQD6tZj/g6g76TaK57qfTZYAbMr/fzwiFftAe89CfQeB4Ldw1/XaNUAX7ZY3b7MUaWuoD4eckKdfx7uU5+bnGpPeMkC9f3slVykjYhoFmEoz2OBaAJn+kNo7Q+j2x9FbyCGcEJGNC4jLivQSBKcZsM5c8NTw9Gbe4No7A2hqSeIQHR4CNdpJNSXWDCvzIZ5ZTZUFZnPGdY+koSiJP84kEAknoBWo4HDrMOyaifqSyyoKjKjzGbk3HAiIpo+4X7A36kG8f5GNYxHvOpQdZ1RDb4l84evli6Eup933yn10Xvi3BAOSQ3vpQuA0oWAqwHQjjMwKwn1jwSxgNo7D6h/FKhbpbaJveFERLMaQ3keEULAE4qj1RPGye4AGnuCyYXZBEx6HYx6DexGHcpsxmGrnEcTMk52B9DcG0JzbxCn+0KIJoYPR9dpJNS6LGgotaKh1Io6lyXjldJjCSU5LzyOaEKBRgLsJj3qXRbMKbWi3GFEucMEq5H/nIiIaBooihrCAx1q+O49qYbpqF8N2nqTujJ5cYO6Z/fAdbK6X3h/YzKINwLRs4ajQ1IXd3PNUwNzyTxAb8m8bUJRg3c0AMT8aiDX6NRh7pYSoPpDarAvqp34nuRERDSjMEXlSEJW0BeKwROKwxOKo8sXQbsvAl84Dn8kAZ1GSrswmyIE+gIxnO4P4XRfCC19IXR4IxBnvb9Rp0Gdy4L6EgsaSm2oKTZnHMLjsgJfWB0iH5cV6HUa2E06NJSpYd5tN6HcYeS8cCIimnpCqD3eoV41ePvb1WAd8Qz2OustgNEB2MqH94RHvEB3M9DfpD48Leoq5kNJWsBZA7jmqiHc1ZB5CBdCXZgtFlBDeCKiDoc3WJMrry9Vh6JbS9VAbnJykTYiIjoHQ/k0CcdkdPuj6A5E0eYJ40x/CL5IAuG4DEkAGo0Ei0ELq0GHcoc6J1wIAV9E3Vv8TH8IZ/rDOOMJnbMoGwAUmfWodVkwp8SC+hLrOfPKRyMr6grpvnAc4eRwdGdyOHqty4wymwlldiPMhnP3MSciIsqqRFQN38FudTh6f6M69zoaUHu6NVrAYFdDr6N6cD53PKL2fnuaAc9p9RHxnPv+erO6irlrrvooqst8OHoiMjgMPR4GIKm98gZrcnX0OsDmBqxl6vB0zTjnmhMR0azEUD4FgtEE+oIx9Idi6A3E0OYJoycYhT+SQEJWoNVoYDPqUGIxwGTQDgRwbziOM/1htHrCaPOoX8+eCw6oQ9Gri8yoc1lQ67KgzmWBw5x5r3UqhPsjcYRiCUiSBJtRh6oiE+a5bah0mlHhMDGEExHR1At71AAe6FSDtPfMWb3ONvVhdavDwAE1EHvPJB8t6uOcueAAIAH2cqBojrpVWfEcNTRnsjCbklCHw0dT88CFOjfdYFd70511gz3g1tL0W6gRERFlgKF8EqIJdesvbygOTziOHn8U7d4wfOEEgrEE4rICCYBJr4PVqENNkbogm6wI9ASiONblR7sngjZvGO3eCEIx+Zx7SADcDiNqiy2oKbagptiMcocp40XZAHU4eiCSgD+aQCQuD2yXVu4wYm5ZCcod6grpTguHoxMR0RSRE2rPdbg/OR+8Ww3VoZ7kXHBlMPTaK9UeaCHUa3yt6rm+VvUR6k1/D3Ox2ltdVK9+ddYAOlOG7Yup26ZF/eofBDRadV66vUJ9P1uZ2gNuLVV724mIiLKEoTwDsiLgCcXgSc6z7gvG0O6JoD8UQzguIxJXw7RGkmDWa2ExaFFksUCvleCLJNDli6CpN4gObwQdvgi6/FHIytmzwAGNBLjtJlQVmVGd3M+7wmnOeI9wQA3goZiMYDSBUEyGIgS0Ggl2kw51yeHtZXYj3AzhRESUbUIAUZ/a+x31D34f6FSDdDwExEJqANdoAJ1FDb42t9oz7e9QV0D3t6l7ivvbksPE0zAXq6uiO2uSX2sBoy2zdsqxwR7weHJeukavtqV0gdqjbq9Q28VV0YmIaIoxlKfhj6groPcFY2qQTvZiB2MJCKGOpjPrtTDrtSixGmDUaRCIqnPGm3qD6PRF0eWLoNMfSTv/GwAMOg0qHCZUFZlQ6TSjymmG22HMeDE2QF30LRyT4Y8mEIwmICsCOo0Eq1GHYose51U5UGY3otRmRJndyNXRiYgoe+KRwdAd6gUCXeow8qhPDd4DC6pJam+13gSYitW51sGe5B7iR5MrqLervefpSBo1HDtq1Dnkzmr1ayYrlw8sxBZUH4mwekxrUK+3VyTngZcBllK1J9wwjpXWiYiIsoApLY1djX1443g3AAl6rQYWgxYOsx4lVoM6TD0QRVt/GN2BGLr9as/32VuQpWgkwGU1osJhRIXThAqHGRVOE4os+owWYksoCoJRdV9y9SGQkBUkkj3tqbYtqbCjwmlCidWIYqseNqMOEld4JSKiyVAUNWRHvINfQ31qj3bYo4bceDLoajRq0NVb1eAdDwPBLjWsB7rUEB7oTL/4WorRoQ5dd1SpD3slYKsAtGP8uiKUZPgODWlT8r/LOpPaptRw9tQ8cEtJ5j3rREREU4ihPI1ufxRtnggMOg16AzH0BqPoCcTQH4yds/VYihq+DSizm1BuN8LtULcNK7MZoRtH77esCPgj6rZo4bgMjQTYTDoYtFoUmfWwmnRwmPSwm3TJ+xlRYjWOa445ERHRACGS+2r7knOqhww5D3arATceVod8S5Lac603A1qTGnjlmBrUUyumB3vUMC7HRr5navsyWzngqBwM32P1UiuyGr7j4eTX5FB4AbUnXm9V38dRqfZ8m5yAuUgd6s554ERElKcYytP4y/52/O1ET9rXjDrNwHDwEpsBbrsJbrsRJVZDxuFbVgRCsQQicQXRhIxYQu35lqD+vmM36VFTbEZDqRVuh7odmdWgHVe4JyIiOoeiqD3Vob7kkPPkiudR/2DwFor6HyOdEdAY1GOJSLKXvAcI9qpfQz3q9mUjkTRqb7TNra6cngrhtvLRw7cQ6vumerzjIUCOD/5BQGcCdGY1dDuq1KHnpiI1eJuLGL6JiKjgMJSnUV1kRpFFjyqnGSU2A0qtagAvtRthH8ew8LisIBKXEU2oXyNxOTknXd2T3KzXwuU0w2XTo9hsgMWog82oQ5ndCOc4tjgjIiICoIbuRHhwDnXUr+6pHfYCwU4g1J98LQBAqAE4Ne86HlLPD/er+4KHetUe8xHHiCWZipKrkg95pOZoa0bZWjM15DwVvIcOOdca1XBtcgJli9T3NDnUHnajQ12QTZ/hqupERER5jqE8jauXuGEx6jDfPfpcs4SiIJZQEE0M/6oIBYAEnUaCSa+FSa9FldOMiiITXFYDisx6FFkMcJr1HHZORESZGQjcITVUp1YyjwWSvd/9am92IpLs3Q4DkcDguUMDcNSfXCHdhzFDt0YPWIoBc8mQfbnLBudla8/6I7IQ6iJviYjaw63E1e3Q5BggRweDN5Cc720GrMlh7NZSNeSbnOqDc76JiGgWYCgfhSIEYonB3u5o8iskASEkaDUSDDoNjDp1MbjKIhNKLAY4zHrYknO/bUYdbCbduFZVJyKiWUYINTCnerFTPd3xYHJ1834g6k0O644mg7gv2cMcTIbtZG93LAjE/IN7f49Fo0sO/XapXy0u9XuLK7kYml0dNp6iyGrYlmPqHwFSw9sTMagBX1KDeuqh0anh2uRQ389UpC4IZ7QDBpsavrniORERzWIM5SOIJGSc6ArAqNfApNPCbNCiutgMl9UAh0kPq1ELq0EHa3LIuUmv4WrnREQ0nBwfXCgtMSQ4p3qsw57BxdViAXXoeMSrBupUr/jZ86ujgSHbjY1FGux1NhcNmXs95GGwqqE71cMtx9R2J6JqW4LdyTndGgyGboP60BnU97bOU3vPU0HbYAX0FnVeut58bm86ERERDWAoT2NJlQMldiMcJh1sRnWlc4tBy9BNRDTbJVK9wqlHdMgjMrggWsSrzskO9amBOzW3OxocDOeJ1AriyaCecdBO0psBo3NwrnUqfA99GB3D53UPm8cdVrc2UxLqImqAOlRdq1fDtM6kLtBmLlIfqaCtt6g923qzutq5zpCtT5eIiGhWYihPo6bYgppiDqUjIpqxhEgOu04GavnscJ08Fg2qW3yFutVVx6Pe5PDy5DDx1Fzts4N5IpLZ0PGzafTqUG+jPdnrbB+ywJl98KvJofZUK7IaqlPztpXE4POIR+3lVuTB0A1JXSBNZ1IXYrOXq/O4B3q3zYPBW2dS9x4nIiKiKcVQTkREhWlg8bDY4Bznge+jaq92ak522JNcTbwfiPQDEX9yGHlo+NDyoQE99X4TCdcpGr0adg02wGgdDL+G5DBvY/KhtwI6ixqCU6FaSSQXShsStuUoEIqq25EJqOdr9IBWp87d1uiSwbpEvY8pGeJ15uRx82C45zxuIiKivMBQTkRE00uIwRCtxJPfJ5/Hw+pQ79SQ79TXsFftpY74BxdDSw3/Ti0ylgrUQ8O5kshOmzXawR7kYUO4LYM9zDrTkIdR7cmWpCG92cmvQlY/g9Qc7Xiyh13jSwbrVMjWDy6KZrCqwT3Vy60zqF+1hiH3TB3j/G0iIqJCwlBORESZi4eB3hPqYmOxQDJEBwZXAY+Fh/dAx4PqUO94aPg86lRwTsQBJTYYzIU8Ne3W6IcEWrP6vdY4+DUVorX65PPk95rkQ5LUHvPklpdIjQZPrnsGSGpwl7TqV41ODd0ardojnuql1qWGhxvV+6QWS9Mak3O5TYPt1Bo5fJyIiCgHhBBIiAQSSgJxJQ5ZkRFX4rDpbbDosz/SrCBC+a9+9Sv89Kc/RXt7O8477zw88MADuOKKK3LdLCKi2WfXb4CXvzP195E0w1f41hoATfL71GJkGv25W2+lepklXfJ46toh4VZADdlDQ3QqQEva5JBwHaBNBeTkomep1cRT76vRDW9Havj4QMBPc28iIiKaMCEEZCGrD+Wsr8nvI3IEsUQMUSWKaCKqPpdjiMqD38fkGCJyRD2WiCCaiCIsh4edn1AGQ3lCJFBuKcdN82/Ch+s/nPWfK+9D+ZNPPol7770Xv/rVr3DZZZfh17/+NTZs2IBDhw6hrq4u180jIppdNMn/bAybx5wMpEPnNQ99DO091uiS3+sH3yM1VFurByQ9oEu955CQLKUeqWOS+nUg/OqHhGX94HDu1D1ToXmg9/us+54TsLWjfw5EREQFSggBRSjnhtuzvo/LccSVwUdCSSAqRweDauqrnBh2TkyJDfQsn93TnFASSIjBY6ngm+qVHhaEhxyXFRkJkYCiDLY79TMM/apMZh2YMciKjJgSm5L3zvtQ/vOf/xyf//zn8YUvfAEA8MADD+Cll17CQw89hM2bN+e4dUREs8ycy4F131KHcUtSMiinviYD99Be42E92obhrw0E9VTQHnJM0qQJ+NpzrycimoWEEBAQw74XEEgeGnh+9nkDrw25ZuD4WcfOef8R7jlwXAzeVxHK8GNCQIGSvg1i+DXpjgMY/voI1yhCSfta6vthX4ecN/Qe6c5JKImB184OgAMPKJCVc48nRGJYCB76HqnPZeh1qePpwuao9z+7HRh+PPWeqXvOJjpJB61GC62UfKT7fsgxjaRJ+5pRa5y6Nk7ZO2dBLBbDnj178M///M/Djl977bXYsWNH2mui0Sii0ejAc5/PN6VtJCIqFFmpj+XLgKL6s4Z7awfDORFRAcpGffRGvbjuj9cNBNeUEUPzkIA69Nyhx4YG27PPJZoqGmggSRIkSMO+aiTN4PfJc1LHNJJm2CN17OxzNZIGGmgGXtNK2uGvnf1+0ECjGX5d6hytRjvweio8ayQNdJLunPYAgJRcEGboz5Q6fvbPOtDu5DoyqfuUmEqm5DPP61De09MDWZZRXl4+7Hh5eTk6OjrSXrN582Z8//vfn47mEREVlKzUR0lSt9kiIppBslEfPREPAvFAllqUXwbCzJCFLlNBJvlk+DlnHRs4XzrrHAwGpGH3Ovv9pOH3OztMDTtfks45nu7eaYPZkGtHOp4KaGdfPzSwpt4/FepS36euHfYeyfcZKfAODbZnP0+FUkkz+LoWWkgaNewODb8D50uaYWFWI2mg0+gGrtUmR6ENC8ujBPBhn/MIn9/Zn/fQ/72H/Ts76/qh7z1wr+RnPfTew/73OPvf6JDP8+z/nYZ+PftnGvhsh/zvMJUkkfqTXB5qa2tDdXU1duzYgdWrVw8c/9GPfoTf/e53OHLkyDnXpPtLZ21tLbxeLxwO/iJJROPn8/ngdDoLvo6wPhJRtrE+DorLcbzZ+ibCifC5oREYFtaAs8LZWSEhefHA9angNXBd8r01miHfDwl5Q99/WI8foAYxnBWmzg6wqXaMMADq7FA10OY055wdwEdz9mc1ntdHatM5gX+095fSnJum/aP9/On+cDDsGglpj5/zvz8VvPHUx7zuKS8tLYVWqz2nV7yrq+uc3vMUo9EIo3HqxvsTERUq1kciovSyUR/1Wj2uqrsqSy0iotkkr/dpMRgMuOiii7B169Zhx7du3Yo1a9bkqFVERERERERE2ZHXPeUAsHHjRnz605/Ghz70IaxevRoPP/wwTp8+jf/9v/93rptGRERERERENCl5H8o//vGPo7e3Fz/4wQ/Q3t6OZcuW4fnnn0d9fX2um0ZEREREREQ0KXkfygHgzjvvxJ133pnrZhARERERERFlVV7PKSciIiIiIiKayRjKiYiIiIiIiHKEoZyIiIiIiIgoRwpiTvlkCCEAqJu3ExFNRKp+pOrJTMH6SESTxfpIRJTeeOrjjA/lfr8fAFBbW5vjlhBRofP7/XA6nbluRtawPhJRtrA+EhGll0l9lMRM+9PmWRRFQVtbG+x2OyRJGvN8n8+H2tpatLS0wOFwTEMLZy5+ltnDzzJ7JvJZCiHg9/tRVVUFjWbmzPoZb30E+G8xm/hZZgc/x+xhfRzE+phb/Cyzh59l9oz3sxxPfZzxPeUajQY1NTXjvs7hcPAfbpbws8wefpbZM97Pcib1AKVMtD4C/LeYTfwss4OfY/awPrI+5gt+ltnDzzJ7xvNZZlofZ86fNImIiIiIiIgKDEM5ERERERERUY4wlJ/FaDTie9/7HoxGY66bUvD4WWYPP8vs4Wc5Ofz8soefZXbwc8wefpaTw88ve/hZZg8/y+yZys9yxi/0RkRERERERJSv2FNORERERERElCMM5UREREREREQ5wlBORERERERElCMM5UREREREREQ5wlA+xBtvvIEbb7wRVVVVkCQJzz77bK6bVJA2b96Miy++GHa7HW63GzfddBOOHj2a62YVpIceeggrVqyAw+GAw+HA6tWr8cILL+S6WQVv8+bNkCQJ9957b66bUjBYH7OD9TF7WB+nDmvk+LA+ZgfrY/awPk6dqaqPDOVDBINBrFy5Eg8++GCum1LQtm/fjrvuugs7d+7E1q1bkUgkcO211yIYDOa6aQWnpqYGP/nJT7B7927s3r0b69evx9/93d/h4MGDuW5awdq1axcefvhhrFixItdNKSisj9nB+pg9rI9TgzVy/Fgfs4P1MXtYH6fGlNZHQWkBEM8880yumzEjdHV1CQBi+/btuW7KjFBcXCx+85vf5LoZBcnv94sFCxaIrVu3irVr14qvfvWruW5SQWJ9zB7Wx+xifZwc1sjJY33MHtbH7GJ9nJypro/sKacp5/V6AQAulyvHLSlssizjiSeeQDAYxOrVq3PdnIJ011134YYbbsCHP/zhXDeFCADrY7awPmYHayTlE9bH7GB9zI6pro+6KXlXoiQhBDZu3IjLL78cy5Yty3VzCtKBAwewevVqRCIR2Gw2PPPMM1i6dGmum1VwnnjiCezduxe7du3KdVOIALA+ZgPrY/awRlI+YX2cPNbH7JmO+shQTlPq7rvvxv79+/Hmm2/muikFa9GiRdi3bx88Hg/++Mc/4vbbb8f27dtZWMehpaUFX/3qV/Hyyy/DZDLlujlEAFgfs4H1MTtYIynfsD5OHutjdkxXfZSEEGLK3r2ASZKEZ555BjfddFOum1Kw7rnnHjz77LN444030NDQkOvmzBgf/vCHMW/ePPz617/OdVMKxrPPPoubb74ZWq124Jgsy5AkCRqNBtFodNhrNDrWx8ljfZwarI8TwxqZPayPk8f6ODVYHydmuuoje8op64QQuOeee/DMM89g27ZtLKhZJoRANBrNdTMKytVXX40DBw4MO/bZz34Wixcvxje/+U3+sknThvVxarE+TgxrJOUD1sepxfo4MdNVHxnKhwgEAjhx4sTA88bGRuzbtw8ulwt1dXU5bFlhueuuu/D444/jT3/6E+x2Ozo6OgAATqcTZrM5x60rLN/61rewYcMG1NbWwu/344knnsC2bdvw4osv5rppBcVut58zJ81qtaKkpIRz1TLE+pgdrI/Zw/qYPayRk8P6mB2sj9nD+pg901UfGcqH2L17N6666qqB5xs3bgQA3H777XjkkUdy1KrC89BDDwEA1q1bN+z4li1bcMcdd0x/gwpYZ2cnPv3pT6O9vR1OpxMrVqzAiy++iGuuuSbXTaNZhvUxO1gfs4f1kfIF62N2sD5mD+tj4eGcciIiIiIiIqIc4T7lRERERERERDnCUE5ERERERESUIwzlRERERERERDnCUE5ERERERESUIwzlRERERERERDnCUE5ERERERESUIwzlRERERERERDnCUE5ERERERESUIwzlRERERERERDnCUE5ERERERESUIwzlNKvIsgxFUXLdDCKivMP6SESUHusjTTWGcspr69atw9133427774bRUVFKCkpwXe+8x0IIQAAsVgMmzZtQnV1NaxWK1atWoVt27YNXP/II4+gqKgIf/nLX7B06VIYjUY0Nzdj27ZtuOSSS2C1WlFUVITLLrsMzc3NA9c99NBDmDdvHgwGAxYtWoTf/e53w9olSRJ+85vf4Oabb4bFYsGCBQvw3HPPDbze39+PT37ykygrK4PZbMaCBQuwZcuWqf2wiGhWYX0kIkqP9ZEKjiDKY2vXrhU2m0189atfFUeOHBGPPfaYsFgs4uGHHxZCCHHbbbeJNWvWiDfeeEOcOHFC/PSnPxVGo1EcO3ZMCCHEli1bhF6vF2vWrBFvvfWWOHLkiPB4PMLpdIqvf/3r4sSJE+LQoUPikUceEc3NzUIIIZ5++mmh1+vFL3/5S3H06FHxs5/9TGi1WvHaa68NtAuAqKmpEY8//rg4fvy4+MpXviJsNpvo7e0VQghx1113ifPPP1/s2rVLNDY2iq1bt4rnnntumj89IprJWB+JiNJjfaRCw1BOeW3t2rViyZIlQlGUgWPf/OY3xZIlS8SJEyeEJEmitbV12DVXX321uO+++4QQalEFIPbt2zfwem9vrwAgtm3blvaea9asEf/0T/807Nitt94qrr/++oHnAMR3vvOdgeeBQEBIkiReeOEFIYQQN954o/jsZz87wZ+aiGhsrI9EROmxPlKh4fB1ynuXXnopJEkaeL569WocP34cu3fvhhACCxcuhM1mG3hs374dJ0+eHDjfYDBgxYoVA89dLhfuuOMOfOQjH8GNN96IX/ziF2hvbx94/fDhw7jsssuGteGyyy7D4cOHhx0b+p5WqxV2ux1dXV0AgC9/+ct44okncP7552PTpk3YsWNHdj4MIqIhWB+JiNJjfaRCwlBOBU2r1WLPnj3Yt2/fwOPw4cP4xS9+MXCO2WweVpQBYMuWLXj77bexZs0aPPnkk1i4cCF27tw58PrZ5wshzjmm1+uHPZckaWARkA0bNqC5uRn33nsv2tracPXVV+PrX/96Vn5mIqJMsD4SEaXH+kj5hqGc8t7QYpd6vmDBAlxwwQWQZRldXV2YP3/+sEdFRcWY73vBBRfgvvvuw44dO7Bs2TI8/vjjAIAlS5bgzTffHHbujh07sGTJknG1u6ysDHfccQcee+wxPPDAA3j44YfHdT0R0VhYH4mI0mN9pEKiy3UDiMbS0tKCjRs34ktf+hL27t2Lf//3f8fPfvYzLFy4EJ/85Cfxmc98Bj/72c9wwQUXoKenB6+99hqWL1+O66+/Pu37NTY24uGHH8bHPvYxVFVV4ejRozh27Bg+85nPAAC+8Y1v4B/+4R9w4YUX4uqrr8af//xnPP3003jllVcybvN3v/tdXHTRRTjvvPMQjUbxl7/8ZdxFmYhoLKyPRETpsT5SIWEop7z3mc98BuFwGJdccgm0Wi3uuecefPGLXwSgDiP64Q9/iK997WtobW1FSUkJVq9ePWJBBQCLxYIjR47g0UcfRW9vLyorK3H33XfjS1/6EgDgpptuwi9+8Qv89Kc/xVe+8hU0NDRgy5YtWLduXcZtNhgMuO+++9DU1ASz2YwrrrgCTzzxxKQ+ByKis7E+EhGlx/pIhUQSIrlhH1EeWrduHc4//3w88MADuW4KEVFeYX0kIkqP9ZEKDeeUExEREREREeUIQzkRERERERFRjnD4OhEREREREVGOsKeciIiIiIiIKEcYyomIiIiIiIhyhKGciIiIiIiIKEcYyomIiIiIiIhyhKGciIiIiIiIKEcYyomIiIiIiIhyhKGciIiIiIiIKEcYyomIiIiIiIhy5P8HbVnxqrBQ1DkAAAAASUVORK5CYII=", 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" + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have fitted the model, we can visualize how a model parameter varies as a function of (some) interpolated covariate. For this example, we will visualize how the mean response `mpg` varies as a function of the covariate `hp`. \n", + "\n", + "The Bambi model, ArviZ inference data object (containing the posterior samples and the data used to fit the model), and a list or dictionary of covariates, in this example only `hp`, are passed to the `plot_predictions` function. The `plot_predictions` function then computes the conditional adjusted predictions for each `covariate` in the list or dictionary using the method described above. The `plot_predictions` function returns a `matplotlib` figure object that can be further customized. " ] }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plot_comparisons(\n", - " model=fish_model,\n", - " idata=fish_idata,\n", - " contrast=\"livebait\",\n", - " conditional=[\"persons\", \"child\"],\n", - " subplot_kwargs={\"main\": \"persons\", \"group\": \"child\", \"panel\": \"child\"},\n", - " fig_kwargs={\"figsize\":(12, 3), \"sharey\": True},\n", - " legend=False\n", - ") " - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Unit level contrasts\n", - "\n", - "Evaluating average predictive comparisons at central values for the conditional covariates $c$ can be problematic when the inputs have a large variance since no single central value (mean, median, etc.) is representative of the covariate. This is especially true when $c$ exhibits bi or multimodality. Thus, it may be desireable to use the empirical distribution of $c$ to compute the predictive comparisons, and then average over a specific or set of covariates to obtain the average predictive comparisons. To achieve unit level contrasts, do not pass a parameter into `conditional` and or specify `None`." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "True\n" - ] - }, - { - "data": { - "text/html": [ - "
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termestimate_typevaluecamperchildpersonsestimatelower_3.0%upper_97.0%
0livebaitdiff(0.0, 1.0)0.00.01.00.8644080.6270631.116105
1livebaitdiff(0.0, 1.0)1.00.01.01.6946461.2528032.081207
2livebaitdiff(0.0, 1.0)0.00.01.00.8644080.6270631.116105
3livebaitdiff(0.0, 1.0)1.01.02.01.0090940.7554491.249551
4livebaitdiff(0.0, 1.0)0.00.01.00.8644080.6270631.116105
5livebaitdiff(0.0, 1.0)1.02.04.01.4532350.9646741.956434
6livebaitdiff(0.0, 1.0)0.01.03.01.2332470.9002951.569891
7livebaitdiff(0.0, 1.0)0.03.04.00.1880190.0903280.289560
8livebaitdiff(0.0, 1.0)1.02.03.00.6063610.3905710.818549
9livebaitdiff(0.0, 1.0)1.00.01.01.6946461.2528032.081207
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" + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } ], - "text/plain": [ - " term estimate_type value ... estimate lower_3.0% upper_97.0%\n", - "0 livebait diff (0.0, 1.0) ... 0.864408 0.627063 1.116105\n", - "1 livebait diff (0.0, 1.0) ... 1.694646 1.252803 2.081207\n", - "2 livebait diff (0.0, 1.0) ... 0.864408 0.627063 1.116105\n", - "3 livebait diff (0.0, 1.0) ... 1.009094 0.755449 1.249551\n", - "4 livebait diff (0.0, 1.0) ... 0.864408 0.627063 1.116105\n", - "5 livebait diff (0.0, 1.0) ... 1.453235 0.964674 1.956434\n", - "6 livebait diff (0.0, 1.0) ... 1.233247 0.900295 1.569891\n", - "7 livebait diff (0.0, 1.0) ... 0.188019 0.090328 0.289560\n", - "8 livebait diff (0.0, 1.0) ... 0.606361 0.390571 0.818549\n", - "9 livebait diff (0.0, 1.0) ... 1.694646 1.252803 2.081207\n", - "\n", - "[10 rows x 9 columns]" + "source": [ + "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n", + "bmb.interpret.plot_predictions(model, idata, \"hp\", ax=ax);" ] }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "unit_level = comparisons(\n", - " model=fish_model,\n", - " idata=fish_idata,\n", - " contrast=\"livebait\",\n", - " conditional=None,\n", - ")\n", - "\n", - "# empirical distribution\n", - "print(unit_level.shape[0] == fish_model.data.shape[0])\n", - "unit_level.head(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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countlivebaitcamperpersonschild
00.00.00.01.00.0
10.01.01.01.00.0
20.01.00.01.00.0
30.01.01.02.01.0
41.01.00.01.00.0
50.01.01.04.02.0
60.01.00.03.01.0
70.01.00.04.03.0
80.00.01.03.02.0
91.01.01.01.00.0
\n", - "
" + "`plot_predictions` uses the posterior distribution by default to visualize some mean outcome parameter . However, the posterior predictive distribution can also be plotted by specifying `pps=True` where `pps` stands for posterior predictive samples of the response variable." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } ], - "text/plain": [ - " count livebait camper persons child\n", - "0 0.0 0.0 0.0 1.0 0.0\n", - "1 0.0 1.0 1.0 1.0 0.0\n", - "2 0.0 1.0 0.0 1.0 0.0\n", - "3 0.0 1.0 1.0 2.0 1.0\n", - "4 1.0 1.0 0.0 1.0 0.0\n", - "5 0.0 1.0 1.0 4.0 2.0\n", - "6 0.0 1.0 0.0 3.0 1.0\n", - "7 0.0 1.0 0.0 4.0 3.0\n", - "8 0.0 0.0 1.0 3.0 2.0\n", - "9 1.0 1.0 1.0 1.0 0.0" + "source": [ + "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n", + "bmb.interpret.plot_predictions(model, idata, \"hp\", pps=True, ax=ax);" ] }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# empirical (observed) data used to fit the model\n", - "fish_model.data.head(10)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Above, `unit_level` is the comparisons summary dataframe and `fish_model.data` is the empirical data. Notice how the values for $c$ are identical in both dataframes. However, for $w$, the values are different. However, these unit level contrasts are difficult to interpret as each row corresponds to _that_ unit's contrast. Therefore, it is useful to average over (marginalize) the estimates to summarize the unit level predictive comparisons." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Marginalizing over covariates" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since the empirical distrubution is used for computing the average predictive comparisons, the same number of rows (250) is returned as the data used to fit the model. To average over a covariate, use the `average_by` argument. If `True` is passed, then `comparisons` averages over all covariates. Else, if a single or list of covariates are passed, then `comparisons` averages by the covariates passed." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" + "`plot_predictions` allows up to three covariates to be plotted simultaneously where the first element in the list represents the main (x-axis) covariate, the second element the group (hue / color), and the third element the facet (panel). However, when plotting more than one covariate, it can be useful to pass specific `group` and `panel` arguments to aid in the interpretation of the plot. Therefore, `subplot_kwargs` allows the user to manipulate the plotting by passing a dictionary where the keys are `{\"main\": ..., \"group\": ..., \"panel\": ...}` and the values are the names of the covariates to be plotted. For example, passing two covariates `hp` and `wt` and specifying `subplot_kwargs={\"main\": \"hp\", \"group\": \"wt\", \"panel\": \"wt\"}`. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } ], - "text/plain": [ - " term estimate_type value estimate lower_3.0% upper_97.0%\n", - "0 livebait diff (0.0, 1.0) 3.649691 2.956185 4.333621" + "source": [ + "bmb.interpret.plot_predictions(\n", + " model=model, \n", + " idata=idata, \n", + " covariates=[\"hp\", \"wt\"],\n", + " pps=False,\n", + " legend=False,\n", + " subplot_kwargs={\"main\": \"hp\", \"group\": \"wt\", \"panel\": \"wt\"},\n", + " fig_kwargs={\"figsize\": (20, 8), \"sharey\": True}\n", + ")\n", + "plt.tight_layout();" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Furthermore, categorical covariates can also be plotted. We plot the the mean `mpg` as a function of the two categorical covariates `gear` and `cyl` below. The `plot_predictions` function automatically plots the conditional adjusted predictions for each level of the categorical covariate. Furthermore, when passing a list of covariates into the `plot_predictions` function, the list will be converted into a dictionary object where the key is taken from (\"horizontal\", \"color\", \"panel\") and the values are the names of the variables. By default, the first element of the list is specified as the \"horizontal\" covariate, the second element of the list is specified as the \"color\" covariate, and the third element of the list is mapped to different plot panels." ] }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# marginalize over all covariates\n", - "comparisons(\n", - " model=fish_model,\n", - " idata=fish_idata,\n", - " contrast=\"livebait\",\n", - " conditional=None,\n", - " average_by=True\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Passing `True` to `average_by` averages over all covariates and is equivalent to taking the mean of the `estimate` and uncertainty columns. For example:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "estimate 3.649691\n", - "lower_3.0% 2.956185\n", - "upper_97.0% 4.333621\n", - "dtype: float64" + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n", + "bmb.interpret.plot_predictions(model, idata, [\"gear\", \"cyl\"], ax=ax);" ] }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "unit_level = comparisons(\n", - " model=fish_model,\n", - " idata=fish_idata,\n", - " contrast=\"livebait\",\n", - " conditional=None,\n", - ")\n", - "\n", - "unit_level[[\"estimate\", \"lower_3.0%\", \"upper_97.0%\"]].mean()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Average by subgroups\n", - "\n", - "Averaging over all covariates may not be desired, and you would rather average by a group or specific covariate. To perform averaging by subgroups, users can pass a single or list of covariates to `average_by` to average over specific covariates. For example, if we wanted to average by `persons`:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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termestimate_typevaluepersonsestimatelower_3.0%upper_97.0%
0livebaitdiff(0.0, 1.0)1.01.3742031.0112901.708711
1livebaitdiff(0.0, 1.0)2.01.9633621.5433302.376636
2livebaitdiff(0.0, 1.0)3.03.7015103.0565864.357385
3livebaitdiff(0.0, 1.0)4.07.3586626.0476428.655654
\n", - "
" + "- daysabs: The number of days of absence. It is our response variable.\n", + "- progr: The type of program. Can be one of ‘General’, ‘Academic’, or ‘Vocational’.\n", + "- math: Score in a standardized math test." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [daysabs_alpha, prog, scale(math), prog:scale(math)]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " 100.00% [8000/8000 00:02<00:00 Sampling 4 chains, 0 divergences]\n", + "
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Also note that this model also contains an interaction `prog:sale(math)`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " Formula: daysabs ~ 0 + prog + scale(math) + prog:scale(math)\n", + " Family: negativebinomial\n", + " Link: mu = log\n", + " Observations: 314\n", + " Priors: \n", + " target = mu\n", + " Common-level effects\n", + " prog ~ Normal(mu: [0. 0. 0.], sigma: [5.0102 7.4983 5.2746])\n", + " scale(math) ~ Normal(mu: 0.0, sigma: 2.5)\n", + " prog:scale(math) ~ Normal(mu: [0. 0.], sigma: [6.1735 4.847 ])\n", + " \n", + " Auxiliary parameters\n", + " alpha ~ HalfCauchy(beta: 1.0)\n", + "------\n", + "* To see a plot of the priors call the .plot_priors() method.\n", + "* To see a summary or plot of the posterior pass the object returned by .fit() to az.summary() or az.plot_trace()" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_interaction" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n", + "bmb.interpret.plot_predictions(\n", + " model_interaction, \n", + " idata_interaction, \n", + " \"math\", \n", + " ax=ax, \n", + " pps=False\n", + ");" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The plot above shows that as `math` increases, the mean `daysabs` decreases. However, as the model contains an interaction term, the effect of `math` on `daysabs` depends on the value of `prog`. Therefore, we will use `plot_predictions` to plot the conditional adjusted predictions for each level of `prog`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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FFVew33770dHRwe9//3u6urqYOXMmlUqFzs5OPvnJT7LTTjth2zYPPPAAZ555JrfccgvnnHMOAEEQcPrpp/OXv/yFJUuWcPDBB/PnP/+ZE088ccjYnn76aQ477DDmzZvH9ddfz6xZs/j973/Pxz72MTZu3Mg111xTc/wVV1zB0Ucfza233srKlSv55Cc/ybve9S5M02T//ffntttu4x//+AdXXHEFDQ0N3HjjjcO+L319fRx++OGsXLmSyy67jEMOOYT+/n7++Mc/snbtWvbaa6+ter/HEhHZwtiRFNu9a1WJ9o3PV8X2tIWgaRM9SkEQBEGoy0DF5aWO4uYPHCGd/fZWP/bOO++kp6eH8847D4B3vOMdfPzjH+eHP/xhLLKXLFnCxo0befLJJ9l7773jx7797W+P15uamrjlllvibc/zOPbYY+nq6uKGG26IRfbvf/97HnroIb7+9a/zsY99DIDjjjuOVCrFlVdeWTO2//zP/6ShoYE//elPsV/6uOOOo1Kp8MUvfpGPfexjtLS0xMfvt99+NWN49tlnueGGG/jYxz7GV77ylfjxjz76KD/72c82KbJvuOEGli9fzv3338+b3vSmuP/MM8/ckrd1myAiWxh7dBOa50KjiG1BEARh6pBPm+zSmhvTcw5UXKYVUlv9+B/+8Idks1ne+c53AlAoFHjb297GLbfcwvPPP8/uu+/Ovffey9FHH10jsOvxy1/+khtuuIEnn3ySgYGBuD+TycTrDz30EADvec97ah777ne/u0Zkl8tl/vCHP/DhD3+YXC6H61ZzjJ900kl885vf5LHHHquJgJ9yyik154zGG3nNk/2//vWv6e/vp1Ao1H0t9957L3vssUeNwJ5siMgWxo9IbDfMhr41qmLkxuehdTeYuQhadwdz6794BEEQBGEsWTijwGkH7DSm53xqdQ+vXzh9qx67YsUK/vjHP/LWt76VIAjo7u4G4KyzzuKWW27h5ptv5r/+679ob29n55133uS57rrrLt7+9rfztre9jU996lPMmjUL0zT59re/HWcuAejo6MA0TVpbW2seP2vWrJrtjo4OXNflG9/4Bt/4xjfqPufGjbXWm2nTptVsp1KpTfaXy+VhRXZ7ezvz5s0b7uVOCkRkC+OPYULzPGiYE4rtP8OGZ6BxJyW22/aCbMvmzyMIgiAIOxA333wzQRBw5513cueddw7Z/6Mf/YjPfe5zzJgxg1dffXWT5/rpT3/KLrvswu23346WuJtcqVRqjmttbcV1XTo6OmqE9rp162qOa2lpwTAM3vve93LRRRfVfc5ddtlls69xa9mS1zzRiMgWth2R2G7aGQY2wobl0PE8rJoNM/aCtr2Vp1smSQqCIAg7OJ7n8aMf/Yhdd92VH/zgB0P233333Vx//fXce++9nHjiifzkJz/hueeeY88996x7Pk3TSKVSNQJ73bp1Q7KLHH300Xz5y1/mZz/7WezJBvj5z39ec1wul+Poo4/mH//4B/vtt18cfd5WnHjiiSxZsoQHH3yQY445Zps+95YiIlvY9mg6FNpUq/SpjCRdK2HNP1Qmkpn7wPQ9VDl3QRAEQdgBuffee1mzZg1f+tKXOOqoo4bsX7x4Md/85jf54Q9/yDe/+U3uvfde3vjGN3LFFVew77770t3dzX333cd//ud/stdee3HKKadw1113ceGFF3LWWWexatUqPvvZzzJ79myef/75+LzHH388b3zjG7n00ksZGBjgta99LX/+85/5yU9+MmQMX//61zn88MM54ogj+PCHP8yCBQvo6+tjxYoV/O53v+PBBx8ct/fn4x//OLfffjunnXYal19+Oa973esolUo88sgjnHLKKRx99NHj9txbiohsYWJJN6jm2dC3Hl75C7RHVpJ9YMbekG/d/HkEQRAEYTvihz/8IalUive///1190+fPp0zzjiDO++8k+985zs8/vjjXHPNNXzxi1+ko6ODGTNmcPjhh8d+5/e///1s2LCB73znO9x8880sXLiQyy+/nFdffZWlS5fG59V1nd/+9rf853/+J1/+8pexbZs3vOEN/Pd///eQtHj77LMPTzzxBJ/97Ge56qqr2LBhA83Nzey+++6cdNJJ4/fmQJzV5Nprr+V73/seS5cupaWlhYMPPpgPfehD4/rcW4oWBEEw0YOIePDBB/npT3/KX/7yF1atWkVzczOvfe1rWbJkCa95zWtqjn3iiSe49NJLeeyxxzBNk2OOOYbrrruOhQsXbtVzL1++nMWLF7Ns2TIWLVo0Fi9n85S64a8/AHtA2SgECHwodkDfOjVxsmGWimq37gYtCyS6LQiCIGw1L774IkBdrXDPv9bypxUb2XenpjF9zqdW93D4btO3Ok+2sG3Y1GcjYqRacVJFsr/97W/T0dHBxRdfzD777EN7ezvXX389hx56KL///e9jz82zzz7LUUcdxQEHHMAdd9xBuVxmyZIlHHHEEfzzn/9kxowZE/xKhK1G0yE/QzW7X6UA7HoZVv9d2Uum7w7TdlUXJbox0aMVBEEQtiPKjsdTq3vG/JzCjsmkEtnf+ta3aGtrq+k74YQT2G233fjCF74Qi+wlS5aQTqe5++674+Tnr3nNa9h999257rrr+NKXvrTNxy6MA6mCEtW+B8VO6HgR2v8NhekqU8n0PVTO7YZZkndbEARBGBWFjMmb9p45bucWdjwm1f/6YIENKun6Pvvsw6pVqwBwXZe7776bc845JxbYAPPnz+foo4/mV7/6lYjs7Q3dgMIM1TxbZSZZ9y/Y8LSKeDfNU2K8daGkAhQEQRC2iiP3kLvgwtgyqUR2PXp6enjiiSfiKPYLL7xAqVRiv/32G3Lsfvvtx/3330+5XK6pXiRsRxgpaJyjml2EgXY1WXLdk0pwT1sYCm4pdCMIgiAIwsQx6UX2RRddxMDAQFzKs6OjAxhaHSjqC4KArq4uZs8efoLBhg0baG9vr+lbsWLFGI5a2CakcpCar/zZlV4V4e5aCWufVH2z9oW2fdRxgiAIgiAI25BJLbKvvvpqfvazn/GNb3xjSHYRbRMe3E3tA7jppptq0tUIUxxNg0yTaoGvottr/wkdK+DVvyqxPXORWEkEQRAEQdhmTFqRvXTpUj73uc/x+c9/no985CNxf1TiM4poJ+ns7ETTNJqbmzd57gsvvJC3ve1tNX0rVqzg9NNPH/W4hQlG06EwE/JtUOqCjSug6yWVnaRtkRLcDeMzsUUQBEEQBCFiUorspUuXcu2113LttddyxRVX1OzbddddyWazPPXUU0Me99RTT7Hbbrtt1o/d1tZWd5KlsB2haZCbplqlD3pXq1SAa/8JM/ZUYrt5vmQlEQRBEARhXNAnegCD+exnP8u1117LVVddxTXXXDNkv2manHrqqdx111309fXF/a+88goPPfQQZ5555rYcrjAVSDfAjL2gdVdVAGjFA/DkL2DZndD+HHjuRI9QEARBEITtjEkVyb7++utZsmQJJ5xwAieffDKPPfZYzf5DDz0UUJHugw8+mFNOOYXLL788LkYzffp0LrnkkokYujAVsHJKaHu2qij50p9gwzOquM3CI6Fp54keoSAIgjBRrHhA3fkcD9INsNubxufcwqRlUons3/3udwDcd9993HfffUP2RxXg99prLx5++GEuu+wyzjrrrJqy6lLtUdgsRkplH2ncCfrXw6rH1WTJ+W+AnQ6SSpKCIAg7IpU+eO5eMLNje163BHueOLbnFKYEk0pkP/zww1t87Gte8xoeeOCB8RuMsP2jGyrfdm6aykTy3H9D7xoV1c42T/ToBEEQhG2NmYU5B4ztOdf8c2zPJ0wZJp0nWxC2OWZG5dMGeOkReOqXsPH5iR2TIAiCICT417/+xXnnnRcngMhms+y+++6cf/75/O1vf5vo4Y0Zt956K5qmsXLlyokeyqiZVJFsQZgwNF1ZSMo9sG6ZSv8393Uw7zCpHCkIgiBMKN/97nf5yEc+wp577snFF1/MokWL0DSNZ555httuu42DDz6YFStWsOuuu070UIUEIrIFIUmmCWYuhs4V8Pz90LsWdjsWCpLyURAEQdj2/PnPf+bCCy/k5JNP5s477ySVqgZ+jjnmGC666CJ++ctfks2OsZd8jCgWi+RyO2blZbGLCMJgDFOl/EsVYNVj8K9fqlLt4cRbQRAEQdhWfOELX8AwDL773e/WCOwkb3vb25gzZ068/be//Y23vOUtTJs2jUwmw4EHHsgdd9xR85jIlvHQQw/x4Q9/mOnTp9Pa2sqZZ57JmjVrhjzH7bffzutf/3ry+TyFQoE3v/nN/OMf/6g55txzz6VQKPDUU09x/PHH09DQwLHHHgvA/fffz2mnncbOO+9MJpNht9124/zzz2fjxo2jfYsmLRLJFoThaJgF6UboeB7K3SqqvcsRkMpP9MgEQRCE8aBjBaz809ie0+4H3YRFZ4z4oZ7n8dBDD/Ha176W2bNnb9FjHnroIU444QQOOeQQvvOd79DU1MQvfvEL3vGOd1AsFjn33HNrjv/gBz/IySefzM9//nNWrVrFpz71Kc4++2wefPDB+JgvfOELXHXVVbz//e/nqquuwrZtvvKVr3DEEUfw+OOPs88++1Rfrm3zlre8hfPPP5/LL78c11W1KF544QVe//rX88EPfpCmpiZWrlzJV7/6VQ4//HCeeuopLMsa8fsz2RGRLQibIpVT9pHul+GFP0D/Olh4NLTMn+iRCYIgCGNNpV/ZBcea4tZFazdu3EipVGL+/KG/OZ7nxamNAQzDQNM0LrzwQhYtWsSDDz6IaSqZ9+Y3v5mNGzdyxRVXcM4556DrVSPDCSecwI033hhvd3Z2cumll7Ju3TpmzZrFqlWruOaaa/jIRz5Sc9xxxx3H7rvvztKlS7n99tvjfsdxWLJkCe9///trxnvBBRfE60EQcNhhh3HUUUcxf/587r33Xt7ylrds1Xs0mRGRLQibQzdg2kIodsKaf8DARpWNZNa+0Dx3okcnCIIgjBXpAkzbbWzPafdDbvrYnhOVyvjJJ5+Mt7/yla9w+umn8+yzz3LdddcBxFFkgJNOOom7776b5557jr333jvuHyxu99tvPwBefvllZs2axe9//3tc1+Wcc86pOV8mk+HII4/koYceGjK2t771rUP6NmzYwJIlS7jnnntYs2YNvu/H+5555hkR2YKwQ5ObpqwiPavg+f9RWUim7w6z94OWXUCXKQ6CIAhTmtbdYN+zxvaca/4Juxy+VQ+dPn062WyWl19+eci+n//85xSLRdauXRsL1PXr1wPwyU9+kk9+8pN1zznYA93a2lqznU6nASiVSjXnPPjgg+ueTx/025fL5WhsbKzp832f448/njVr1nD11Vez7777ks/n8X2fQw89NH6u7Q0R2YIwEsy0+hJ2K9C3Fl58GNrD0uyz91f7DPmzEgRBEEaPYRgcc8wx/M///A9r166t8WVHPuhkPunp01XE/NOf/jRnnnlm3XPuueeeIxpDdM4777yzrm1lMJqmDelbtmwZTz75JLfeeivve9/74v4VK8bBmjOJEDUgCFuDmYaWBdDoKJ/2y3+Bjc+piPbs/VV2EsmvLQiCIIyST3/609x7771ccMEF3HnnnZucILjnnnuy++678+STT/KFL3xhTJ7/zW9+M6Zp8sILL9S1gWwJkfCOouQR3/3ud0c9vsmMiGxBGA2GBU1zoWEO9G+A1X9Ts9NbFsCs/aBtbzV5UhAEQRC2gje84Q1861vf4qMf/SgHHXQQH/rQh1i0aBG6rrN27Vr+3//7fwCxReO73/0uJ554Im9+85s599xz2Wmnnejs7OSZZ57hiSee4Je//OWInn/BggV85jOf4corr+TFF1/khBNOoKWlhfXr1/P444+Tz+dZunTpJs+x1157seuuu3L55ZcTBAHTpk3jd7/7Hffff//WvSlTBBHZgjAW6AY0zoaGmWpi5Np/QscLSnTP2k9Ft0VsC4IgCFvBBRdcwOtf/3q+/vWv87WvfY01a9agaRo777wzhx12GH/4wx845phjADj66KN5/PHH+fznP8/HP/5xurq6aG1tZZ999uHtb3/7Vj3/pz/9afbZZx++/vWvc9ttt1GpVJg1axYHH3xwTdaQ4bAsi9/97ndcfPHFnH/++ZimyZve9CYeeOAB5s2bt1VjmgpoQSAVNgCWL1/O4sWLWbZsGYsWLdo2T1rqhr/+AOwBVdJb2H4IAih1Qu8aZS2ZthB2PlilAxTPtiAIwoTw4osvArBw4cKhO5f/Cp67F8wxrpzolmDPE7cqT7aw7djkZyNkpFpRfu0FYTzQNMi1qlbqgvXLlODe8AzMO0SJbkEQBGHykG5QYni8zi3scIjIFoTxJtsCmSboWw+vPAbdr8DMRTD3ECjMmOjRCYIgCAC7vWmiRyBsZ4jIFoRtgaYrz3Z+usqzveIP0PkCzDkQdnqNlGoXBEEQhO0MEdmCsC0xLGUVsYvQtRJ610L7c+LXFgRBEITtDPlFF4SJIJWDmfuEfu3l0Lsa1j9d9WvXSeYvCIIgCMLUQUS2IEwkSb/2qv+DntCvPfsAaNpZxLYgCIIgTFFEZAvCRBP7tWdU/drrn4bWXZWFREq1C4IgCMKUQ365BWGyYJgwbRdwK9C3Fl76I2x4WuVQn7kvtO0lEyQFQRAEYYogIlsQJhtmWpVlb5oLAxtgzT9g4/OwaidlJZm5GPKtEz1KQRAEQRA2gYhsQZis6AY0zIbCLDVBsvNF6HoJVv8dZuylBHfzPPFtC4IgjAF/Xv1n+p3+cTl3wSrwhp3eMC7nFiYvIrIFYbKjaZCbppo9oCpHdv8e1v0LWhbCrMUqI4mVmeiRCoIgTFn6nX4eXvUwGXNsv0vLbpmj5h41pucUpgYisgVhKpHKw/TdwbOhbx28/Gdof0YVuZm2q7KZNM9TKQIFQRCEEZExM+zTus+YnvPpjqe36nFnnHEG9913H2vXrqW5ubnuMe95z3u44447ePXVV5k5c+YoRjlybrrpJnK5HOeee25N/8qVK9lll1245ZZbhuybCK699lqWLl1KEATb/Ln1bf6MgiCMHiOlxPSsfcHMQNfL8Ny98K/b4W8/hGf/W+XfrozPrU9BEARhfDnvvPMol8v8/Oc/r7u/p6eHX/3qV5xyyinbXGCDEtm33nrrkP7Zs2fz6KOPcvLJJ2/zMU02JJItCFMZ3YBCm2q+p7zbPath4wqVfzvfCi27qAh3y3zVJwiCIEx6TjzxRObMmcPNN9/MhRdeOGT/bbfdRqlU4rzzzpuA0Q1POp3m0EMPnehhTAokki0I2wu6oWwjM/ZUEe5Mo7KUrPgDPPVL+NstsOxXKltJsXOiRysIgiBsAsMweN/73sff//53nnrqqSH7b7nlFmbPns2JJ57IsmXLOO2002hpaSGTyXDAAQfwox/9aMhjuru7ueSSS1i4cCHpdJq2tjZOOukknn322fiYpUuXcsghhzBt2jQaGxs56KCD+OEPf1hjt1iwYAHLly/nkUceQdM0NE1jwYIFgLKLaJo2JMr9pz/9iWOPPZaGhgZyuRyHHXYY99xzT80xt956K5qm8dBDD/HhD3+Y6dOn09rayplnnsmaNWtqjr399ts5/vjjmT17Ntlslr333pvLL7+cgYGBkb7V44ZEsgVhe0Q3INeqWuBDuQcGNkLnS7C6oPqb56qJky3zlTgXBEHYwVnZs5K/rv3rmJ5zwBnA0AzevODNI37sBz7wAb74xS9y880387WvfS3uf/rpp3n88ce5/PLLWbFiBYcddhhtbW3ceOONtLa28tOf/pRzzz2X9evXc+mllwLQ19fH4YcfzsqVK7nssss45JBD6O/v549//CNr165lr732ApRIPv/885k3bx4Ajz32GB/96EdZvXo1S5YsAeBXv/oVZ511Fk1NTdx0002AimAPxyOPPMJxxx3Hfvvtxw9/+EPS6TQ33XQTp556KrfddhvveMc7ao7/4Ac/yMknn8zPf/5zVq1axac+9SnOPvtsHnzwwfiY559/npNOOomPf/zj5PN5nn32Wb70pS/x+OOP1xw3kYjIFoTtHU1X5duzLUpwV/qg2AFdK2H1E0pwN+4MrQuheb6ynkhaQEEQdkCKTpGX+14e8/N2lbu26nG77bYbb3zjG/npT3/Kl7/8ZSzLAuDmm28GlAhfsmQJtm3z0EMPMXfuXABOOukkuru7Wbp0Keeffz5NTU3ccMMNLF++nPvvv583velN8XOceeaZNc95yy23xOu+73PUUUcRBAFf//rXufrqq9E0jQMPPJBsNktjY+MWWUMuv/xyWlpaePjhhykUCgCccsopHHDAAXzyk5/k7W9/O1rid+eEE07gxhtvjLc7Ozu59NJLWbduHbNmzQLgqquuivcHQcAb3vAG9t57b4488kj+9a9/sd9++23ZmzyOiMgWhB0JTVe+7EwTBAHY/Upwv/IXWPuPUHDvpFICtsxXObp1cZUJgrBjkLNyzG+YP6bnHHAGaMm0bPXjzzvvPM455xx++9vf8ta3vhXXdfnpT3/KEUccwe67786DDz7IscceGwvsiHPPPZd7772XRx99lBNOOIF7772XPfbYo0Zg1+PBBx/kC1/4An/961/p7e2t2bdhw4YRT7IcGBjg//7v//jwhz8cC2xQdpj3vve9XHbZZTz33HNxJB3gLW95S805IsH88ssvxyL7xRdf5KqrruLBBx9kw4YNNXaWZ555RkS2IAgTiKZBukG1lgWh4O6EVx+HtU8qwd0wC1p3hRl7S5VJQRC2exY0LeCkhSeN6Tmf7niag2cdvNWPP+uss/joRz/KLbfcwlvf+lb++7//m/Xr1/OlL30JgI6ODmbPnj3kcXPmzIn3A7S3t8cWkOF4/PHHOf744znqqKP4/ve/z84770wqleLXv/41n//85ymVSiMef1dXF0EQbNEYI1pba39vIitK9Pz9/f0cccQRZDIZPve5z7HHHnuQy+VYtWoVZ5555laNczwQkS0IgiJVUK15HthFKHWoSZLrl0HT32Gn18CcgyQHtyAIwjYkm83yrne9i+9///usXbuWm2++mYaGBt72trcBSpCuXbt2yOOiiYLTp6s5NzNmzODVV1/d5HP94he/wLIs7r77bjKZalGeX//611s9/paWFnRd36IxbikPPvgga9as4eGHH+bII4+M+7u7u7d6nOOB3AcWBGEoqRw0zYXZ+yufdvcqePYeePI2WLcMfH+iRygIgrDDcN555+F5Hl/5ylf47//+b975zneSy6mAx7HHHhuLziQ//vGPyeVysWf6xBNP5N///vcmJwVqmoZpmhiGEfeVSiV+8pOfDDk2nU5vUcQ4n89zyCGHcNddd9Uc7/s+P/3pT9l5553ZY489NnueweOMxpDku9/97ojOM96IyBYEYdOk8jBzkbKPrP0XLL8Llv8/JbwFQRCEcee1r30t++23HzfccAOO49Tkxr7mmmuwLIujjz6an/3sZ9x7772cffbZ3HPPPVx77bU0Nan6CB//+MdZtGgRp512Gp///Oe5//77+e1vf8sll1zCQw89BMDJJ59Mf38/7373u7n//vv5xS9+wRFHHFE3c8i+++7Lk08+ye23385f//rXumkGI/7rv/6Ljo4Ojj76aO68805++9vfctJJJ7Fs2TKuu+66mkmPW8Jhhx1GS0sLF1xwAb/61a+4++67ede73sWTTz45ovOMNyKyBUHYMnKtMHs/QIOVf4J//QKevx9K3RM9MkEQhO2e8847jyAI2GeffTjkkEPi/j333JO//OUv7Lnnnlx00UWcfvrpLFu2jFtuuYVPfepT8XENDQ386U9/4rzzzuN73/seJ598Mv/xH//Bc889F3ujjznmGG6++WaeeuopTj31VK688krOOussLr/88iHjWbp0KUceeST/8R//wete9zpOPfXUYcd+5JFH8uCDD5LP5zn33HN55zvfSU9PD7/97W+HpO/bElpbW7nnnnvI5XKcffbZfOADH6BQKHD77beP+FzjiRZMRDH3Scjy5ctZvHgxy5YtY9GiRdvmSUvd8NcfgD2gfLCCMFVwK9D9Cni2ykSy82th9gFgpiZ6ZIIgCMPy4osvArBw4cIh+36/8vc8tvYx9mndZ0yf8+mOpzl09qFblSdb2HZs6rMRMVKtKBMfBUEYOWYapu+ucm53PA99a2HDszDvUNUvebYFQZiClN0yT3c8PebnFHZMRGQLgrD1pBtg5r4w0A6r/w69r0LbPqqse36GKu0uCIIwBShYBY6ae9S4nVvY8RCRLQjC6NA0VSUyNw16XoUXH1J5ttONqr9xNuTboDBT+bqluI0gCJOQN+z0hokegrCdISJbEISxQTdVURvPhnIPlDqhOyxPHBW9yTarEu6FmUqA52eAldnUWQVBEARhSiIiWxCEscVIKfGcn6G2fRcq/cq/3f+8yrNt5SDTAKlGaJwDhRmQaYZsixLiqYL4ugVBEIRtRhAEI04luDnGXGSXy2VWrlzJ7rvvXpPMfEvp6+vjs5/9LP/85z/5xz/+wcaNG7nmmmu49tpra44799xz+dGPfjTk8XvuuSfPPvvs1g5fEISxRjeVcM42q+0gAKeoRHffGjVxUjeU8I5aplFFu7PNSnhHAlyqTQqCMAo0TcPzvIkehjAJ8X0f0xxbWTyqs33jG9+gu7ubq6++GoC///3vnHDCCXR2drJgwQIefvhh5s6dO6JzdnR08L3vfY/999+f008/nR/84AfDHpvNZodULspmsyN/IYIgbDs0TRW4SeWhYZbq811Vyt0pQqkLelcrX7duQSpbFd/ZlmqUvGW+WkrEWxCELSSdTtPT04Nt26RSknJUULiui+u6Y64hRyWyf/CDH/DBD34w3r7sssuYNm0aV199NTfccAOf+9znRlzicv78+XR1daFpGhs3btykyNZ1PS4XOmXZ+Dy4ZchNlyidsOOimyp6PTgbieco4e0Uodihqkx6thLo+VZo3Enl6W6eryLfMqlSEIRN0NjYSE9PD2vXrmX27NkitAWCIGDDhg2A+nyMJaMS2a+88gp77bUXoGwef/zjH/nFL37BmWeeSUtLC0uWLBnxOcfaDzPpeemP6pb507+BhtlKMLQuhGm7QqZpokcnCBOLYYHRNPRvodKnRPeq/4M1/1QXqY2z1N9Ny3xomCOCWxCEIeTzeaZNm0ZnZycvvPAClmWhadqOpz0EQAlsz/PwPI9cLkdDQ8OYnn9UIrtSqWBZFgCPPvoovu/zpje9CYAFCxawbt260Y9wE5RKJWbNmkV7ezuzZ8/m9NNP5zOf+QzTpk0b1+cdM9wy9K8PNwIltvvWwMt/Ul256Up0TwtFd3663BoXBKhmKwFVMbXYAaufgHVPQXaasqFMW6iynTTuBIbM8RYEQQXy2trayOfz9Pb2UqlUkMLXOy6apmFZFi0tLbS0tEyuiY/z5s3jf//3fznqqKP4zW9+wwEHHBCH2tvb28c87J5k//33Z//992fx4sUAPPLII3zta1/jD3/4A3/9618pFIZP/L5hwwba29tr+lasWDFuYx0WMwNHXKImflX6ofMFVarad9X+4kbVXn1cbacbQ8G9C7QsVFkZ9JFPLhWE7YrI3908D5ySEtzr/qWymOSnQX4mTFsQernb1MWqYU30qAVBmCA0TaNQKGxSJwjCWDAqkX322WezdOlSfv3rX/Pkk09y3XXXxfv+9re/sccee4x6gMPxiU98omb7uOOO48ADD+Sss87i+9///pD9SW666SaWLl06bmMbEWYaWnZRAgGUB7X7Feh8UYnurpfArah9lV5Y+0/VQKVKa1mgHh9F7cz0tn8NgjBZsLLQtLNqbkUJ7g3PwPplSoinG1R6wKadqxMo89PVhEq5SyQIgiCMIaMS2VdeeSWmafKXv/yFM844g49+9KPxvmXLlvHWt7511AMcCWeccQb5fJ7HHntsk8ddeOGFvO1tb6vpW7FiBaeffvo4jm4LMSxo3VU1joPAh941SnB3vAhdLyo/KqgJYBv/rRqApqvodsvCasRbfN3CjoqZVn8PjXPA95StxO5XVSk3/lv9rUWiOztNHReJ7kKbEuyCIAiCsJWMSmRrmsbll19ed99vf/vb0Zx6qwmCAH0zE57a2tpoa2vbRiMaJZpejcztcqTKMVzsUJHurpfUMvJ1B74SED2vwso/qr5cayLavYuaXKnJhDBhB0M3hmYv8Wx1wVrph7516g6RlQv93gX1t9IwS2Utyc+QaLcgCIIwIsZkNlC5XOaJJ56go6OD1tZWDjroIDKZbV8q+c4776RYLE79tH6bQtNUpC0/Hea+TvXZA1XB3fkS9LyiInegBHmxA1b/XW0baZV9oWWXUHwvkIidsGNipNRFaK5VbQe+8nRX+qBvvbpzpBuh6G6E3DQ1ibLQFgpv8XYLgiAIwzNqkf3Vr36Vz372s/T29sYlKRsaGrj66qu55JJLtuqc9957LwMDA/T1KVvE008/zZ133gnASSedRHt7O+9+97t55zvfyW677YamaTzyyCPccMMNLFq0qCZ39w5BKg8zF6sGytfds6oqurtWgjMQ7qvUWkzQVLSuZRc1OaxlF5XVRCJ2wo6Gpg8tkuM5ai5EpVfZttb8Q0W5041KfDfNVYK70KYi39a2Dy4IgiAIk5NRV3z85Cc/yXHHHce73/1uZs2axbp16/jZz37GpZdeimVZfOxjHxvxeT/84Q/z8ssvx9u//OUv+eUvfwnASy+9RFNTEzNnzuSrX/0q69evx/M85s+fz8c+9jGuuOIK8vn8aF7W1Mewqqn/QFlMBtrDaHcouvuj9IoB9K1V7ZW/qK5UQUW7mxeoSHfzPJlQKeyYGNbQaLc9oER39yuw4Vl1JyjdqOY/tMxT0e6G2Up8S+pAQRCEHRYtGEWCyF133ZU3vOEN/PjHPx6y7+yzz+bRRx/lhRdeGNUAtxXLly9n8eLFLFu2jEWLFm2bJy11w19/oH60o+wi2wq7CN0rleDufAm6X1Ye1bpo0Dg7FNwL1FLKWQuCwikp0V3uUX/LVk4J7tw0VYkymnyZa5W/GUEQhCnMSLXiqMIsa9as4T3veU/dfe9973v5f//v/43m9MJ4kspB2z6qgfJw962BzpVV8V3sCA8O1K3y3jXwchjttnJVT3fzfHWRIN5uYUfEyqpWmKnuGjlFKHfDxhWwfrmKcmebVI7u5rmhr3umumMkkW5BEITtllF9w++xxx6sX7++7r61a9ey2267jeb0wrZEN5S/tGkucITqq/RB18vKZtK1Uvm8o2i3U4QNT6sGgKZ8qc3zQ6vJfHXLXIrlCDsSmlb1dTfupOwllT4V5e75p/J0Z5qU8DbT6rhMc/iYnLp4tXLhethnZiQCLgiCMAUZlcheunQpn/jEJzjooIPiyosA//rXv1i6dClf/epXRz1AYQJJN8CsxapBGO1eqwR31Iobw4MDlUqwf321QqWRUqkHmxdUhXe2eVu/CkGYODRdiepMk7rb47lQ6VHCu7hRTaz0HHWsbqi/GSMFZrRMK5GdbQlTCoZpBdNSqU4QBGGyM2KR/Za3vKVm23VdDjjgABYtWhRPfFy+fDlz5szh1ltv5YwzzhizwQoTjG5Uc3YvOFz1VfqVnztqXS+DW1b7PDvMcPJi9RzpplBwz1OtaZ5kZBB2HAyzdiJlEt8F11Z/N15F/R2Ve9W2WwHdDHN9N4U+752U4G6YpSLhgiAIwqRixCL7X//6F1ri1qVpmsydO5fe3l56e3sBmDt3LgBPPfXUGA1TmLSkCzBzkWqgbo8PtCuxHYnuvjWqH1QUb92/VAOqNpN5VW934xwlKARhR0I3IWUCufr7PVvZTgY2KguXblaj5I07qb+bhllhKkGZHyEIgjDRjFjJrFy5chyGIWw3aHqYN3hmtViOZ6sqlF0vq0mV3a9AqSt8QNJm8lfVpRtKNDQnIt75GVKpUtixMVJh2fcZatutqEh3/wZVOMdMhakEG9Xfj5UL7Scm6Fa4bqmmh8u4L6XEuni/BUEQxgwJFwrjj5GqzdsNypPa/UpoM3lFNaeo9vletS/CTIcTM0PR3TwXstNEFAg7LmYaCjNUg9Be0qOqVXa+CFFyVg3QDBX51nS11A3VtHCph/nAW+ZBwxyVsjPTNFGvTBAEYbtgzER2e3s7pVJpSP+8eds4/7MwNUg3DLKZBGoiWFJ497yqfKqgonYdK1SLsPJKbEfe7ua5IgyEHRczA4WMuouUJPDB9yHw1N+T74XriaVTgt5XYe0/qxaUhjnQvLOynzTOEQuKIAjCCBm1yP7c5z7HjTfeSEdHR939nueN9imEHQFNq94K3+k1qi/K3d29KhTdr0Dfuqq/2xmA9mdVi0g3KWEQpSNsmqtunwvCjoqmg6Gjvu43U7k1yn5S6lL2rtWR77ulmuO7cTYUZkmOb0EQhM0wqm/Jm2++mS9+8YtcfvnlLFmyhCuvvJIgCPjJT35CNpvlsssuG6txCjsiydzd8w9TfZ4NvasTwnuV8qRG98YrPbC+RxUBicg0hecJxXfzPBVJFwShlsHZT9yKKqzTtwY6nlfR7EyzSsVZmKWK7GSaQyEeLkV8C4IgAKMU2d/61re44ooruOyyy1iyZAlnnHEGBx10EFdeeSVvfOMb2bhx4+ZPIggjwUhByy6qRThldas78nH3vJrI343yqZZ7YP2yal+N8J6nlhLxFoRazHR1IjOosvHlHuh8KbyDpCmbSlT10spBbroqKZ9pUmI8sp+I3UQQhB2MUYnsFStWcOihh6LrKuuDbatqgNlslksuuYSrr76aT33qU6MfpSBsCisDrbupFuEUldjuXqWi3VsivNON1TzgUdQ70yyTKwUhIq5mOUdtB77yczslcEvKZtKxQvXXiO+s2k41qLSfZkb93ZrZ6tJMV4+zstVsQkGQGEBQvy8IQu95WNzHj/znyW1H9XmuWmp6IsNKmGXFTNdmXDFS4/v37yfGHFULFQRhu2FUIts01cM1TaOxsZFXX3013jd9+nRWr149utEJwtZi5WD6HqpF2MUw4h0J71VQTMwlqPQOKhWPmlxZI7x3VrfSJZ2gIKi/g0h4JwkCVVAnEuD97crq5TtKWALoeiKVYCKtYLTUtEFiejBBzQL8xGROPxTd4XbgJyZ7hvvQqllWdCuRacVIZGAxQ9GfUwI8mZFFM9RrSG5H2Vui74foNXu2EvdeWGzIKalsMG6lOi5NVza2wszEXYBmtUwV5GJfEKYgoxLZu+++O6tWrQLg4IMP5vvf/z6nnXYauq7zve99jwULFozFGAVhbEhtQnj3JNpAO/EvtzMAG59TLcJMhxPAdoamnaqV96SAjiAotNBGEpWEr0cUXY5Ky/uOEp1+v9qOBHZdcTmoT9OUSNX0WvFrWKBnhvZHIth3qxlXfA8CV4nfaDve54bCPBj0/EE4vuTzh+sEifOGop5ALXUzvMgwqyI98NX3z9onVX9kv7Gy6i5boa1WfGcaq3cARIALwqRkVKrgpJNO4o9//CPve9/7+PSnP82b3/xmmpubMU2T/v5+br755rEapyCMD/WEt1uG3jVVm0nPq6pYTpTVxK0MLRevG1CYXRXdTTurW+qmlIwXhLpEUeMJ/RvZTLaVLSEIUJaVMEIeWVegGg0f6Z0vz1WWN7ekJp72rlVVcnUjtNTklM0msrjENpzsMDacnCpWVBOFN7ZcnLuVavS9ZllRY3TKKkJv5dQ4UoVw2RDe6RimiumOTBBQY2tKXtB5TvXCbpMtYZUKfPV/Gt1ZShXU0hyDz/h4EwTV152845S8wI2XidSjuqFsorox0a9gWEYlspcsWRKvH3PMMfzlL3/htttuQ9d1Tj75ZI4++uhRD1AQtjlmZmjxHM9WP3Rx1Hs19K1VX46g/uB7X1UtRoP89DDqvVO19LVU1hOE7YdkJHusMEwwGoFBk7F9N7TgFNUkVM+pWlICPwyUa/Wre0ZiP2pQtcrEFpno2FC02ANg9ykxnbzjULMe1jJIRumtNBjp8G5GWons3HRlh4kFeF7tjz3xYdPH2YrnucrK5FbUe5dc6oYagxmOP/bppzc/rijffPT/E7dEn12qWoa8SlUcx8IxqLU3xRdwYcMfpi98/zUtfN/T1XGn8qpwW7ohIcBDEW5l1f9X/PnYit+lIBj6Pkav0a1UPyOxdcqpfm6dMnhltayxdg26YI3XPfU6fV+97mwL7Pt2lV50kjKm97cPPvhgDj744LE8pSBMDowUtMxXLcL3YGCDEty9r1aXTlSUKVDWk4F2VeQjIpWvFd2NOykf5iS+GhcEYRKgm0osbSoF6bA2nIGhwiWOwAdD+6EqwiPRrlsqOp5uDMVZaqg485yq39yrQLFfpV31lqvTRsLVTIfCPuF/14zaTDVGSkXgIwEeW25I+PWD4dc9G9xQ0NpFZf9z7UT02AsnwobrmpYYU9iMcFxmRn13W9nqBFnfU+K53Kvm9Hh2VUDGS7taVE3TwzsICdtSvJ20OxmgpRLWIy1xMTdoHb3a53vV990ugdddFb2QuIBILOOxaOGFWXL/oPci+v+Nzu+GFw/JKHzUPLdaACuKRPte4v9QC+1bZvU9iD5LNe9HOHcCvXZfpVddBAaTuxbLqER2uVzGtm0aG6tX23fccQdPPPEExx13HMcee+yoBygIkxbdUNXwGmYDr1V9QQClTvWjkhTf5e7q4+wB2Phv1ZLnKoSV9SLh3ThHsg0IgjAyJtqGE0XQh7sQiCKcbkUJL7cySKCF3vhogixBNRNMRBTNJVoMk4Emsu1E4ky36ojolIq0a4Z6bFIoOqWqUIyy0gSRUEw8n5G4EDFSYQS5UL1A2FbzdQyGT5UZBNULrkh4Oz2DoulR1DicPFwzB2HQeiSKB18o6aa6EEuZ1c9idAEVieaxwHeVyJ/kjOp//r3vfS/5fJ5bb70VgBtvvJGPf/zjAHzlK1/hd7/7HSeddNJoxygIUwdNqxbzmLVftd8eUD7v3tXV1re+ehVe126CspY07lQtbd24k6qKKVFvQRCmIlFUeiQFwSIrwRDCiC4kdG9CxMn3ZJXYRjTC914YFaMS2Y8//jhf+tKX4u0bb7yRs88+m29+85ucd955XHfddSKyBQFURHr67qpF+K6aUBlHvUMR7hSrx0T5vJNpBXVTZTNpmFONfDfMUT5HQRCE7Q3dQIVpBWFqMSqR3d7ezk477QTASy+9xIsvvshtt91GY2Mj5513Huecc86YDFIQtkt0s+rN3jnsCwIlqnvXQF8kvNfUlo733WrWkyTphlB4hxaWhjnQMDP0MgqCIAiCsC0ZlcjO5XL09PQA8L//+78UCgVe+1rlTc1kMvT3949+hNs5JcfH8ANEBgmAuqWXbVZt5j7Vfs9W9pK+NVXhPTjqXemDyqCc3mjKXpIU3o2zpaCOIAiCIIwzoxLZ++67L9/61reYP38+N910E0cffTRaaGp/5ZVXmDVr1pgMcnul5Pg8vbYHzRmA/kaashaFjElD2iKbMgaXWxB2ZIyUSlOUTFUURb371oaR77VKhCe93gQqA8rABlXkInm+wsyE+A5bulHSCwqCIAjCGDAqkX311VdzyimncMABB5BKpXjggQfifffccw8HHXTQqAe4PWN7PrbjUyq69LhFNA3SlkHGNMilDJqyFg0Zk4aMST5tYoj4EZIko95te1f7o9SCvWsTAnwNlLqqx3h2tbR8EiuXEN2zqutSTEIQBEEQRsSoRPYxxxzDM888w9///ncOOOAAFi5cWLPvgAMOGO34dgiylk6+OYsXBFQcn4rr0VtyWN1dIm3qpC2drGnQlEvRkDFozKZoyljoormFetSkFkzglMJo91roWxcK8LUqd2x8TBE6X1AtSboxFN2zVKrBaH24dFGCIAiCsIMzKpFt2zbz589n/vz5Q/adf/75ozn1DomhaeRSKooNapqb7XqUHZ/Oos36vgqWoZG1DAoZi9Z8ipZciqaciTXeVbKEqY+VHVrJMgiUl7tGfK9Ry6iAAajE/5Xe2tzeAJnmhPieVV2XcvKCIAjCDs6oRPZOO+3EBz/4QT784Q8zb968sRqTEKIBadMgbRqASsTveD5F22NdT5l1PSVyKZNcyqC1oAR3Sy5F2hTBLWwhmgaZRtVm7FntD3xlL6kR3+tUysGoehmoIjvlbmh/tva82Rbl+Y7F90y1bYntRBAEQdgxGJXIPvXUU7nxxhu57rrrOOWUU/jIRz4iVR7HGcvQacrqNGUtvCCgaLt0FW029FXIpgzyKUOJ7TDKnU9JblFhK9D0alGdmYur/YEPAxtDwb2uKsAHNoQlc0NKXaoNFt/pplBwz0osZ0llS0EQBGG7Y1Qi++abb+b666/n+9//Pt/5znc4/vjj2WOPPbjooot43/veR0ODVBUaTwxNoyFt0ZC2VDVvx6Ov7LKx3yZt6uRSJk1Zk+ZcSmUtyZhkTRHdwijQdCi0qUaioqXvQXFj1evdv76++K70qDbYdpIqqEh31BpmqWWmSbKdCIIgCFOSUYlsgJaWFi699FI+9alP8bvf/Y5vfvObXHzxxVxxxRWcc845fOQjH2GvvfYai7EKm0DTiP3cAVBxPYoVj66ijdFZIm3pZCyDfNqgKZuikDYpZAwKKZlAKYwBulEVyLP3r/b7HhQ7wqj3+sRyPfhO9Ti7Hzr7h064NNOQn1m1m0Qt1yolkwVBEIRJzahFdoSmabzlLW9h7ty5XHLJJTz88MPcdNNNfPvb3+b000/n29/+Nm1tbWP1dMIm0ICMqVIBAnh+QMX1Kdke3UWbVzsTWUusMGtJ2gxzdJtYhni6hTFCN6qR72Ta/MCHYmcY8Q4j31FzK9Xj3Ar0vKJaEs1QRXYKbaHwDpf5NrBk0qUgCIIw8YyJyHZdl1/+8pd861vf4tFHH2Xu3Ll86Utf4h3veAe/+c1v+OxnP8s555zDfffdNxZPJ4wQQx+UtSRQke6y67Ox32Zdr8pakjYNMpZOczZFU86K83RLfm5hzNF0yE9Xbeaian9UYCcpuqNW6Usc56moeP+6oedONyXE98yqyM80SZVLQRAEYZsxKpG9evVqvvvd7/L973+f9evXc8QRR3DHHXdwxhlnoIcp5T760Y+y0047cfbZZ4/JgIXRo2mQsQwylgFZlbXE9nwqjkdv6OmOUgXm0gbTcmmaskp052QipTCeJAvsJLOdANjFhOjeUF0vdqASXoZEvu+O52sfb6Sq0e98W+3STI/zCxMEQRB2NEYlshcsWIBpmrzzne/k4osvHrb4zMKFC5k5c+ZonkoYZ1KGTsrQiaaq2p6yl7T32azrqZANRXlj1lS5ubMWjVnJzy1sQ1I5mLaLakk8F4rtCeGdWHoJ64lnQ+9q1QYTR7+TAnwG5KZJ9FsQBEHYKkYlsq+55hrOP/98ZsyYscnjDjjgAF566aXRPJWwjUkZOqkwVWAQQNnxKDmhp7urpKLcKYOWfIrGjLKVFNImpsyiFLY1hlm/wmXSejKwQYnuaFnqZoui37oBuelKcOfboBAu8zMg3SCZTwRBEIRhGZXIvuqqq8ZqHMIkRtMgmzLIhlYR11dR7ig/d8rUyZgqe0lz1qIha1JIK+GdkkmUwkSxKeuJZ6t831HUOynCkxMvfa9qSxmMmamK7/z0hBVluhTdEQRBEMZm4mNPTw///ve/KZVKQ/a98Y1vHIunECYRpq7TkNFpyFgEgOP6lN2q6DYNTWU3sdQxUaS7ISt5uoVJgpGCxjmqJQkCVT5+oB3625XoHgitKMWNKitKhFuGnlWqDSaVDwV4nSb+b0EQhB2CUYls13W54IIL+PGPf4zneXWPGa5f2D7QgJSpkzJ1GjNqEqXr+ZRdPy6MY2hanKe7kDZpDCdQ5lMGubRJWqLdwmRB01QWkkwTtO5Wu8/3oNRZK74jAV7urj3WHlCta+XQ50g3hoI7jH7nplfXRYALgiBsN4xKZH/ta1/jd7/7HTfffDPnnHMO3/rWt7Asi+9///v09PRw4403jtU4hSmEaegUDJ1CWn28Bufp9jtDYW7oqjJl2qAhbZFPmyrVYFrl+Ba3qzCp0I1qNJp9avdF9pNIeMdto4qMJ6n0qja48A6EAjwU3bkZCTE+XdlTBEEQhCnDqET2T37yE6688kre9a53cc4553DIIYdw0EEH8cEPfpA3v/nNPPTQQxx//PFjNVZhijIkTzdguz626zEQVqX0g1KcqztlqiI5DRmTfNogl1Il4TNiNREmK8PZT0DZSpKiO7m0+2uPjQX4i0PPkyqE4nt6YtmqxHgqL5MwBUEQJhmjEtkvvvgi+++/f5wTu1wux/suuOACLr74Yv7rv/5rdCMUtjs0UBUnzapNJEDZTKKId0/RYXV3gKlrYXXKqtUkqk6ZT5lSEl6Y/JgZaJqr2mCckhLcxS0Q4Ha/avUsKGY6jHy3JkR4uJ5tljSEgiAIE8CoRHY+n8e2bTRNY9q0abz88sscdthhAGSzWTo6OsZkkML2jwZYhq5KuidsqUnh3V20CbogbRikLY1syqA5m6IgJeGFqYqVhea5qg3GKavJlpHwLm6sLss9tce6Feh9VbXBaIbK9x2J7kiI51pVEx+4IAjCuDAqkb3XXnvF+a8PO+wwvvrVr3LEEUeQSqX48pe/zJ577rmZMwjCpjENHdPQyYc6oKYkfFgoJ1kSvilrxekDCxmTnCUWE2GKYmWgaWfVBuPZqtLlQEcYBU8sS521WVACr2pXqUe6sSq4B7dMo0TBBUEQtpJRiex3vOMd/Pvf/wZg6dKlvPGNb2T+/PkAWJbFXXfdNeJz9vX18dnPfpZ//vOf/OMf/2Djxo1cc801XHvttUOOfeKJJ7j00kt57LHHME2TY445huuuu46FCxeO5mUJk5gtKQkfZTNJmwaFtEFTLkUhbVDImBRSllhMhKmPkapfgAfCLChdSoQXN4YCfGN1PVkFE6o+8K46BcN0A7L1BHgYGbey4/P6BEEQtgNGJbIvvPDCeP3AAw9k+fLl/PrXv0bXdY477ritimR3dHTwve99j/3335/TTz+dH/zgB3WPe/bZZznqqKM44IADuOOOOyiXyyxZsoQjjjiCf/7zn5utQilsPwwuCR9lMyk7Hj0lVaFS+brVhMqmXIqGtBlXqRSLibBdoRvVjCQM+g4OApVasLgxjIRvrK4XO0MbSqISpu+F6Qo31H8uK6cEdzYhvCMRnp0GhjVer1IQBGHSMybFaCLmzZvHxz72sVGdY/78+XR1daFpGhs3bhxWZC9ZsoR0Os3dd99NY2MjAK95zWvYfffdue666/jSl740qnEIU5ch2UxCi0nF9dnYb7Oud7DFJEVT1qQhI1Uqhe0cTYN0QbWWBUP3e46ym0Siu9hRjYgXO2qrYQI4RegpQk8dLzhAuikhvkPhHS2zLeqCQBAEYTtlxCJb13W0EaSKGmkxmi05t+u63H333ZxzzjmxwAYl0I8++mh+9atficgWYpIWk6bhLCa6Rtaqiu7GrElD2qIxK6Jb2IEwLCjMVG0wQaBE9cBGKCVFeLgsdSn/d5JKj2r1rCiEhX/qCfDcNMg0iwgXBGFKM2KRvWTJkhohfMstt9Df38+pp57KrFmzWLt2LXfffTf5fJ4PfOADYzrYiBdeeIFSqcR+++03ZN9+++3H/fffT7lcJpOpX7xhw4YNtLfXTgJasWLFuIxVmJwMtpgMqVJZI7qtann4rFSoFHZQNE3l407loWX+0P2Br+wmyQh4Mio+2IpCoCpllrvrF+aJRXgi8p1cz7aIHUUQhEnNiEV2cgLi9ddfz6xZs3jggQcoFApxf19fH29605vI5XJjMsjBRKkBp02bNmTftGnTCIKArq4uZs+uMykIuOmmm1i6dOm4jE2YmgyuUrkp0d2QsSikTbIpg6xlkE2pCpUyoVLYodH0qvht3XXoft+FUncovDtrBXipE8q9DCvCqVOcB1RmlFh8h88di/BpKkOLIAjCBDEqT/ZNN93EV77ylRqBDdDQ0MCll17KJz/5ST71qU+NaoCbYlPWkk3tu/DCC3nb295W07dixQpOP/30sRqaMMUZIrp9n7KjRHdHvw2ovN4pU+X2TpsaDRmLXEqJ7mzKVKLclKi3IACgm4kJmXXwXCh3hZHwUHiXuqqifEgknGpmlO6X65/TzIT2kxbIDBbjLZBukBSFgiCMG6MS2atXr8Y065/CNE3WrVs3mtMPS2trK0DdYjednZ1omkZzc/Owj29ra6OtrW1cxiZsn5i6TiFdFd0B4Li+8na7Hv0Vn3W9FXStWlQnZepkwvLwmXA9amlTx5TQtyBUMUzIz1CtHr6notpxFDwhwktdoSfcr32MW4a+NarVQzNURcyk8M601PZJsR5BELaSUYnsvffem69+9auceOKJWFbVG2fbNtdffz177bXXqAdYj1133ZVsNstTTz01ZN9TTz3FbrvtNqwfWxDGAg1ImUpIJ/GDAMdT4jsqD7+mO0DXVHTc0nVMQ8MyVMXKXMokE2Y5iQW4pWOOYHKxIOwQ6EY1RWA9Al9ZTiLBnRTf0fbg7CiBV/WPD4eVCwV3c60AzzSHfc0yQVMQhLqMSmR/7nOf4/TTT2fhwoWceeaZzJo1i3Xr1nHXXXexbt06fv3rX4/RMGsxTZNTTz2Vu+66iy9/+cs0NKjpa6+88goPPfQQn/jEJ8bleceDh3vaKDses/U8M9MObSkbcRhMXXRNpQZMm0ZNeXg/CHA9JcAdP6DseHQOOPhBVYCnDBXdtkydfMoglzZjz3fOMsikDBHfgjAcmh4K4GZgl6H7gwDcUliop0tZU2IB3q2W9SwpTlG13tXDPXHoDW+uiu7BIlwqZwrCDsmoRPbJJ5/Mfffdx5VXXsm3vvUtfN9H0zRe97rXccstt/CmN71pq8577733MjAwQF9fHwBPP/00d955JwAnnXQSuVyOpUuXcvDBB3PKKadw+eWXx8Vopk+fziWXXDKal7VN+X3XHFZWChAGUjQCpqdcZqZtZqZtZqUdZqVtZqYdZqZtMkaw6RMKkxJd00iZ2pDIN0TR7wDX83G8gKLjsLGvQgBYhlZjPymkzTAHeFWAZywdQ8S3IGwaTVNRaSsHjTvVP8b3lNCOxHd5sAjvBqc06EFBNVXhcN5wTVeZUmqEd2I92yz+cEHYDtGCIBgT1VYsFunq6qKlpWXUWUUWLFjAyy/X/7J66aWXWLBgAQB///vfueyyy3j00Udryqrvumudme2bYfny5SxevJhly5axaNGi0Qx/i+np2siR1/0v3V5qix/TbEYC3KEtFOHRdqPpIVpr+yEAPM/H9gJsz8dxfRzfJwiU+E4Z0aRLneZ8iuasRXMuRS5lIB8DQRgn3HJVdJe6QyHeXRXhpW7wnZGfNxbioRjPNNUK8mifWFMEQeXrd0tw0Dn1C2uNEyPVimNW8TGXy41Zyr6VK1du0XGvec1reOCBB8bkOSeKm3Z9nO6yw4bMQtZXUqyrWDXLAa/2C7XbNel2TZ4bGHqujO6FEe+q8I6WrSkHU5TXlEJD2UhMA3JUPwcBxFFv2/UZsG3W91VImzq5lCoX35JL0ZxTxXQsXaJjgjBmmBlomKVaPYIAnIFQgHfXLiNLSrlbpTSseZxfjaAPi6Yi3kkxnh0kzDPNMllTECYJY1pWXRg5mgYtpk1rQ4m9GwbfhoR+V2ddJcX6ihUu1fr6ikWXU1uIoewbvFwyeLk0dNKnTsD0lENbQni3pR1mpmza0g4F0x/yGGFyohFlMCEsHW8RABXHo+R49HTZrO4qhRMrDablUzSFUe58SqJggjCuaBqkCqo17Vz/mCAAeyCMgvdUo+HlnqoIrxsRD6ppC3tWDT8GMzM0Kj64iT1FEMYdEdmTnILps5tZZrd8eci+iq+xIYx4r69ZWmywU3hBNXTto7HBTrHBTrGsLz/kXHnDY2ZaCe62hBhvSzlMTzkyGXOSo1EtHQ8qr3fJ9ugq2mzoq5AxdbIpk8asinKnTZXlxNDCpa6aqWuYui6FdQRhPNE0SBdUa5pb/5iojH25pxoNj1opIcbdob8NuGXoL0P/+k2MQVdCO11HgMetEcws4kMUhK1DRPYUJq0HzM3azM3aQ/b5AXTYJusrKTbYFhvCSPiGilrv82r/6wc8gxeLWV4sZoecSyOgNeXSFka9IyEeifIm8YJPOkxdpyGjqlMGQDmMcnd32LzaWcIwNAxNQ9dU4SZDB13T0XU1SdPQlec7ZeoYukbGNMinDXJpNelSspwIwjiTLGPfOGf449xKKL6TUfCexHaPinwPzpoS+NX9PZsYh24psZ1pUllU4kh4Y1WIZ5pU9FwQhBpEZG+n6BrMSLvMSLt19xc9vSYKvqFisd5W2xsrFl5i6lyAxkbbYqNt8XT/0HOlNJ8ZiQj4jDAVYSTG82JFmVA0UJlILANy4PkBnh/gBwF+AF7g4/ngBl64HRAEAZ6vMp+A+jylTIOUqVIUNmZM8mmTXMpU6QZTBpYhtzsEYZtjpqEwU7Xh8D2o9IWCe5AAL/eo/OLDRcV9Z/O5xAGMVEJ8N1ZFeM22RMaFHQsR2TsoOcNnQa7CglxlyL4oCr7Brka+19sWG8JIeI9b+7GxA53V5TSry/Un2+QNjxmhAG9L2/H6jJRqkpZw2xJZQxI9m32MF6hJlrbr01N0aO9T1S2jaHeUXrCQscinotSCBhnTEOuJIEw0upHIIb4J3EpVcFd61XolKcTD9XrZUzwbBtpV2+RYrFrRnW4cKsTTjSqCL55xYYojIlsYQjIKvqhh6P6yp9Eeim7l/7ZoD5cbKhZlv1a0DXgGAyWDlXUmZAI0mm6N8I4FeLhM6SLCJxpD06rR8JCoumXF9ekru3QM2BCAZeqJ9IIahYwV5/SOzpGxRHwLwqTDTENhhmrDEQQq4h3ZUIaI8t6qIK8nxrc0Mq7pkGoIRXfDIFHeUF1mGlUUXRAmISKyhRGTMYb3ggcB9HlGjeiuWbct3KA2OtHrmvS6Ji/U8YODyg0+PRLgofienqoK8bSI8AmhprplSBCgcnp7PmXXo7/is663ojKimKqsfMrUsQxNFdaJqlpaBoWMSUZm2ArC5EbTwMqqNlwaQ6iK8UpvNRIeZUap2e6rb1MJ/GqRn81hZmqFd7oRMuEy1aDWUw1qn+QZF7YhIrKFMUXToNH0aDQ9dq2TEcUPoNsxw0i4VbNst60hfnCo5gZfMVBfhDeZrhLdCQuK2lb9OUM84dsKTYO0qQrkJAkCVEl5z8cOxXd7oqpl2jRImTr5tEFjxqKQVp7vQtrElJC3IEw9kmJ8U35xUDaVSl9tNLzeuj3AkAmcoES6W968VQXAyidEd2OY5SUS54mWKoggF0aNiGxhm6JrMC3lMi3lsmdhaF5wP4BOx6yJfkeTLtvD9cEivMc16dlEJDxveDWRb7Xuxn0Nkh1l3NE0Yu92kqiwTsVVKQe7izarghIpUydj6qStquguZAwKaWU9Ed0tCNsRZlq1/PRNH+d7YPdXBXmNMB/U5w6dbwSoQkHOALBu8+Oy8gnhXUgI8MGCvAEMkVPCUORTIUwqdA2mp1ymp1z2ZngRvjHhA4/Wo2h4Mj84VD3h9Yr0AKQ1JcJb024svqM2I+UwLeViiKgbF6qFdXQI580GgO16VJzaSZZpyyBt6mQtg8asRcY0yKb0OD+4TLIUhO0c3aimENwcUXTc7oNyUoD3DRLkffW941AV5P1bIMhjy0oYBU8ua/oLkmFlB0JEtjClSIrwvQZFwnWvguH001fx2ehYbLDTtDtpNtgZ1rs5Nrg51jk5KkHtx74SGKyuGKweJvChETDNcpkRlqdXdhQlxlvDdbGkjB0aDPF6e0FAxVFWk76yy9qeMrpWFeiWoZMyNRXpTkWTK5UAz1qGWE4EYUdjS6PjQVAryCv1Wm81gu4NnYsEjMyyohlKbKfCCHmNKB/cX5CJnVMYEdnC1CQIMLwyKbcfy+3D9Er4egrHzNOUzdGQN5mvgWcYuKaJZ+h4uo+n2fR4Gu22SYdtsrFi0FXy6B6o0F326ChrQwr1BGh0OBYdg8rYJ8mFlpTpKZdWy2aGZTM9ZdNmVWhNVWhOgS63E7caQ9PIhfm4I1R2kyjDifJ5r+9VV0qWoYXiWy2j7CYp0yAV9qXDvN9RNhRDIkuCsOOhaWBlVGMTWVUiYv94JMr7ExaWqD/cdor1zxF41XSIW4KRUoI7FuGbWBdRPqmQX31hahD4WG4Ry+3Hcvsx/QqukcG2CpQyMxnIzqacnkEpPZ1KqhlPz+Dr1rC35BrDtsugfs13odhBsa+T/r5e+vr76Sq5dFR0NtoWG5wMG90sPrXe4qJn8ErJ4JWhDhcAdAJazRIzzBIzrDLTLZvWlEtr2mVaKmBaBvKmhjaeEdcgQAs89MBFC3y0wEPDD9eH3ybw8YwsFasJ18hNmtucKruJNnSSJcrnHQnwkuPSNeDghYV1akvIaxi6Ki0fWVFU9pNqFhRT17FMVQHTNHSpdikIOzJbGiGHQR7yPjVxMxbm4TK5PpxtxbOh1KnalmCkwmqhheoyKchTBUgn9ot9ZdwQkS1MDgIf3XfRAwfdd6pL38X0SuiBh2vmcMwC/fm5DGTnUE5Pp5SeQTnVij9GV+6BbkJhJtnCTLKzVVzDdAfIVDrI2J1kKh1Y5Zfp9DJssDO0Oxk2OlYcFe+oGHTaOkW39gvLR6PdzdHu5qBOtiqAlOYxwywywyozwywz3arErdVymG7Z5MyAQNMJNIMAPVzX0QMPzXfRg7D5Dlq87gEBaBqBZuBrpnq8pifOobZ9zQr71Daajo+G5ZXIlddjeiUcs0DFasK2GidlsYiqzxvqFdrx/AA3rHrp+SoKPmAHeF5QFeKahq6jBLgerms6hq4K8KSTYtzQMBNL1aeW4lIRhB2YkXjIg0CJ6ViUhxHyaDsW6OHS7lcivh6eDSUbSl1bNk5NrxXkNcth1o3h7+wKVURkC+OOFnjono3hV8KWENKBq0KPmoavmfi6ha9beLqFb2RxLIsBM08xM4tSHKluUWJ4G+GaefrNPP35eTX9aWDnsA2m7Hj0lBy6izbdJYeeoqO2Sw49RZuesoM3yMZtBwarnQZWO3UqAIVkdZfpVoUZZjkU4yo63pqyabUCWtOQMix8qxFfM/DMPI6RxzPSeHoaz0jh62k83QqFtIGvWfi6Ga4b4bqFH+4PNJ2M3Um+tIZcaQ2F0hpSdjeF0ho83cK2mqlYTWN2oQOEF11OfMEweAmEnxeTIFyqi4fq+nAXALUVL+un6PKCAD8W4qpVAq9mO0okZmgahqHOaWgaRijODV0L0xkqW0oUCY9EuBnaVkxdwzQ0sasIwo6MplWj5LnWzR8f5SGPhXj/oPWBodvDRcoDv5oqcUsZEi0fJMKtfG2/ld8hM7DseK9YGHuCAN23MfyEkPYqGL6NFngEmoGnp/BDoWdbWQLdxDFyOGYDrpnFMzJ4ehrXyOIZaVw9g2dkcY0Mvp6acreyoowXMxvrZzTxg4CBihsKcadWkIetv+wOyQhb8k1WVUxWVfLDPnfW0mnMmDTlUjRlrbg1Zi2aLLWenFS4JZTT0ymnp9PRvB+m00++vJZ8aS2F4iuk7W6aiivRAo+K1agEt5YKrSde1X6CN6w1RYlnFy1w0QBf0/F1KxTNlhLURhpXyysbECjbSxi9N70KWjBQcx498AnQ4mh/PQKG+VxpeniRYYZiPlqv9gW6ugjxgmoUPBLgtpsQ5GF0XAMlxjUtFvp6Yj2ZL1xZWUK7SijK05YukzgFQVAk85Dnt8BLnoyU2wNKeDv9YWQ8KcoTy+E85TDyaDmAkYZUrlZ416znhvYbU+/3P4mI7B2dIED3HUy/jOGVMfxybC9QftwAjaC6JEALohCsRmRD8PQUvp4KhXKOSmoattWIbTXimAVcM48TRlVdM4dnZAi0HTfRv65pNGQsGjIWO7fUP8b1ffpKbo3w7inZcVS8p+QwYA+9XVhyfEqOzfq+YWbBAxlLpzEzSIAn1zMWGUtHq/Pl5loFeqzd6WnYHd13yJbXh4J7FdlKO7lKO7rv1thQau0tYbQ8YXfxdQvHKOBa+fAzlAkj75kwCp8JL75UHxBezKkLOz2xHl/seRV038Z0SxiBrX5khly2BGixCK7u02rEuocWuBheKeFp92IrTgCg6fHYXCODZ6llMpoeAJ7n4wWE4tsPBblfEzmPRqFrxEI8inanDJ1c2iQXlahPqdSFmZRB2ph81h1BECYJI42Ug7KjOMVBAnyg/rYT9g2XnxzAq0CpMjJhrhtV0W0lBDoa7PzaLT/PBCEiewdBCzwVXfbKCUFdQQt8fD2lhIGRwU41q0gdGsR2gkQUTzdim0EkkqKsHqoVcMwcrpHfppaO7RFT12nJp2jJD2/DcDyf3hoRXm29oT2lWEeIlx2fslNhQ9/wX4gpQ6cxa8aiuzEhwKP+QtpkILczA7md2TDttaTtTvLldei+E0agjYSFw1A2lNiakrB7aOaIoxWekcUz6hcgGgvUnAB1h0b3IptTuB3fuVHrljtA2u7E9MqYXom03Y3pKfO9p6di8a0uHrIEpsFwVpUIPxLeoSh3PJ8ex6VjwMYPlABPRRlUTJ2MaZAPy9RnLD0sYx/5xDWZtCkIwsjQjWqO7y3Fc6uC2x4Auxhu12lRvzvMRCVQQn84K8u8Q0b+mrYxooKmOkGgomxJ32py4mDgxFFFL44OZqmkWqhYLdipJmyrAcdsiJeDI3DC5MUydFoLaVoL6WGPqSfEe8sOPSU37u+vuEMeZ3s+G/ttNvYPHxHXNWgII+KNmVCQZ9tojER52GdNwShrND/AZXhrThLNd7HcflJOLym3D8vpJeX0kql0YHpFTK9MrrIRwy+j+X54IyiKW2vVRRDE20EoigN0XCOLa+VwMzkl1gMNOyxVP1D26PZUBhVdA9PQh3jEo/5MWHnTNGpFeDLnuGloWLpM3BQEYYQYJhhbONkzwncTYrxYK8CTIj2Oqg8oH/kU0CkisicBWmjZUP5Vr+pZDTz0xLqGV5NFQgv8MGOEiRdGCH3dwjcsXC2nBIKRo5JqxrEasM2kmC5IpHkHYUuEeGRNUeK7GgmvinKXvrKDP8ht4QfEx2wKVaXRTIhvFQ2PIuQNGZN82kSfwpHWQDexU83YqeZBO3wsd4CU04vl9pFy+jC9Ymi7iuxYULViRX0+GgGa76P7Nmm3G9MtkauoLC8BmhLeRg43ncM1s+oOQZg/3PerPnHb82I7ih8E8f9j0pKStKYYuoY+aOJmMu+4GU7s1DTQNA0d9Xuno86BpixRuqZuUOiaTOwUBGEYdBMyjaptKf3t4G0iAj5JEJU1wQS6QcbrJzfwUiLbg5GYeGWoCWBklFUDHc9I45gNOGYu9KpmYruHuh2dib2sO7LvWdhytsSa4gcB/RUV/e5NiO+emm0Hxxvse4aS41FyvLhYTD2iqHgU/W4M1xsS641Zi7RZ3ys+adF0HKsBxxrBLdc6mG6RlNNN2u4iY3eTrnSSsTdieiWy9kbMUgkIcPUMrpGN//YDXQM0AiNUvEq6q4w+gaaEeKBT0TK4gOsHVFy//sTNUHwbeiisw/+H8CnQwj6NSIBX+0xdIzMo9WE1el4bSZcIuiAIm2SK/AaIyJ5I0o2sbTwQO7M3rU0FFZHWLZWrWDerKe3i1HZmvE8QtjW6poVi14JhJmsGQUDZ8ekpK+HdF9lSwm21dBmoDM2cUhMV7xqmqg/KK94QCu6GjFlXjDdkLFLm5L+VOBJcM4dr5ihm58R9ulchbXeRdrpJ292k7U5ylXYMr5yIivtoUaTcDycvh5OZo3Ut8DG9IgE6jlnAtgpD7nYFgOf7eD744QTNIEgsfVDPqKLwfvigIACfoRM7o6wq9VIfZswwF3lSfJs6lq6TMqsZVyTTiiAIkxlRaxOJprGuaX8GbJegJTfRoxGEUaNpGtmUynoxa5j0haAm8vWFwrsntKL0ltSyp+zE1pWK6w95rO35dAzYdAwM7xUHlUEljoxnlCBvyFSFecN2IMZ9I00pO4tSdlbcp/kOKacvTpEY204CH/DRgiCxT4ls3XfI2J1kS+vI2J2k3H7ylXVogY9j5LFNJbo1I81YvV1x6sOkrSVMfdiZiKBHlpZk6kMlxmsLA0UVPPWE7UUf9LhqUSENQ6t61QVBEMYDEdmCIGxzDF2jOZeiObfpAjYVx6Ov7CrhHQpxte7SF0bG+8ou7mCzONUMKu2byKACVTE+WHyrpUljWq2nralhvQp0i0p62lY+OMBye8lWOshW2smWNpC127GcfrLFV9B9G9fIqrScRjZO3bk1t24NTcMwN/+4IAgj6EGA56v5AxW3fmEgjdALrie94hp6aF/R9cgrHvnFlRiPouWGrmEmIurJSLuuR9NRQytMtBXNWY2XasXUNclrLgg7OCKyBUGYtKQtg7RlML1h+EmbQRBQcjw1OTMxSbM3ER3vK7v0ld04OppkS8V4ZFOpFeFWbV+Yw3pKecaTaBqO1YRjNdFbWAgoL3imspFsZSOZSjv50lpMb4CM0xWnMiQAXzdV9iI9HeY2V3nzR2tv08KsKNWzDH+xE6DmDgR+oKwrQYDvh1aXwCfwwSHAD9fVJFA1EdQf9NnQtaGCPJkEJrmMhHeSKNKeT5txasVsyiCXMslYukwEFYQdABHZgiBMaTRNI5cyyaXMTVpU/CCgZHtx9DsS39F2b8mhr+LSV6ovxrfUpmJoGoVIeKdNCkkhnq6uF9Im5hRIbeiaOfrNefTn5wGgezYZuxPL7cNyBzDdIpbbT9ruwvSKGH5F5Qp3esJCQW5YwTOVEOHRempM03BpqPcfo56AHdmdCD8IlJ88FOJBNQkMiUVM5E+P8PygJq+5ZUR5zZW/vCFt1hQWSpt6IssLse1FpLggTF1EZAuCsEOga1ocVZy9iRSuQSTGK7VR8L5B4ryv7GJ7Qz3jXhBsUVpDUKkNGzKmEuVpFQ0vpKuR8ag/mzImTXpD30hRzM4CZg3Zp3sVLLcfKxTepjcQpi/sI+V0x0WwUk6vKubjqbsHvm5UBXgYCffDtKQTlQs3SkVojIHMDQDX83E8VeGzaDts7KsQQCy8TQN0TQ9THlazshh6Na1i5EWPI+yhINdQE0e1ROQ9ts2gxeuRiDcSYn6kvvTobkFUKClKFRkEAYaucqybuqRsFAQQkT0pKNkeG/sq+ITRkjB6Es/aD6pREj9QX8CmrmMYGqamqaUefRlXvYQjoZoloJrXVhB2RDRNUxHG9KYj41D1jPdWHPpjMR4K8YpLfxgpr1d1E6qpDTdVeRPU33whIcILcZTcrOlvyJgTmuLQN9JUjDSVdJ2yzYGvot6h8FZR8P6wWmY3ltuH4duYXoW02xvWDgjrAaCp6rJx9VCVcSmqFhpVEVWpCXWVM0XTAY3JkOpLg9D3DblUNaIeoIpFOa6P6we4gadsLr7K0hL/FgRqu87Ug1CMU5s2MYyAa1roRScS4NH3e0LMa2oSqBkJ5NCbroMS0UGA61VTOrq+EtTKjhP9XvkEVMV/9BuUNqLCR9Xfptj7Hkb2U4aOZariR4KwvSEiewIxdY1MSpVCdnw//DLU0HQwNS2cuBMVdqh+cfqJCUC27+O5apa+6wd4vvqy9oIgTNulvsijn5l4XatuA/HEoAjPD9C0quhWkZJqRMRIfJnqWvXLPVqPvmw11G3P6IcgPjbpZkw8b3JsyYlF0XMJwmRiSzzjoP6e+hOR8f6yS1+lGhFP7qs3idMPoLfs0lseWplzMKauxeI7Et7ReiES46FA36aCXNNxrQKuVaBegkbds7G8SHgXMb0Sum9jemW1DIW54dtoUXXbwFHHBS5a4KEK+qjsKaoablRZE2q+CbUAXzPx9EG1BbZyEufWokEsNLeW6KWpII3ytPihtSVI2FyC0Pbi+eAGvjrerz7O9zct5Id8z4e/VXpC3Dt+gB9OUvUHFT4C4gmkURTe0CLRrXz3udA2E1UkTUc51U31HuladSKr/BwIUwER2RNIPm1y6v6zqTh+fNvPiMR1mLYqWo9EpqYRprrysT1fLd3qbUjb87DdQJVbDo8JgqpIr7mNOOjWYhT5CAIl2F3fxw3Fu+2qGf3qOaIv0ui2YdW3SBT58Ktf4H4iGh994UaRmVp/Y+LfRH8UzfGCQP1EJq4UDK36hV3vAqCaGSB5e7R6cRAtBWE8MXSNpqwqP78pgkAVgukfFB1XItylP+qrqFzj9QSR6wd0Fx26i5u3qwwW5IVEdHzwetYa3wmdvpGiYqSopIZJwh6i+w66V8HwK6H/W4lw3XfDyrjBoMq5PhrRth/3p5ye0NJSJuX2Ytjtyr6i6bh6WhX3mgKFvaLAhDFJovabYrDNxA38MG0juL4TX2BqgGloGJqKgkc51CNhXY3UV7/vo9/QGhtNGKXXdT2+UIhsNcbgY6LoflRoKVHBtF62GhH5wpYgInuCaWvY9O3o4dhM5rNxJQiqtw0dz8f3ifPc+kEirVYwfD9Ub4eq9dqJQ0FCafuBuqUaPafrqYi94wWJCw1PlZIORX+Ue9f3A5wo2h+NxVeZBqLjklkFolumSR+kqascvElbjkb1CzpKOhDfmt3ED110oTH4VnAU8YkiT1C97Qvqdi/JW8KDtq0pMIFO2DyapqoiZrYgOu4HAUXboz8RCa+K8bCFgrxYp/gPjEyQG5pGPm0khLdFIW1QCH3uUX8+bZJPmeN250kV5rJwKYz6XIZXxnL6SLl9cdn7tN1D2u7E8MqYXpm024PhldEDH08zw8wpYfYUI42vp/H1TV88CQpd09ANbbOZYqK0jW5QvUtru14cnIl+HqLv0cG/KfU+6zW2Gr36nR0JZ43q9+lwFUyTIj8KilXv8NYK9+pSifjBfUm7TrykVtDruoZlVH+DhKmHiGxhxGiaFnr3IDOJcgf7fmSZUcLa9dRFgOsHoedRCXPXq+53veodgYrrK4+s7WJ71S/3wbacSLwnv9BrvZNQ72teBeCjiUqhjSa0CEWWmujLN3kLmMRzVC9Map/b9ZVvNWPpZEyDtKUrsWYaW11sxfX9+D3Qo//z8O6KMPHomhZHmTeH5wcU7Vrh3T9IjPdXNi3IvSDYYssKKO9xLMDT5qB1I17PpyfOR66i1BnKzKjp13w39I33hpM2e9UETrublNsXRtArpEMfueY7oGlx2sJIhEcXBL5mTfoo82RC0xJpG8dQpUTVR6tznaoWmUio+0F1fxz4wK85Phgk8AcHiYa8Hur45iMrKNX0kPE6kQCvRvAj+0zaUIEgy6h63ePt8Pt5NBNbhbFFRLaw3aDrGqnw2yQ7wnRdg0mK76Qtx/b8MJJem19X+Q+rkerI3hJ9WSc9iJGlxRhUnU4LJx/pWq1w9+v8IER9oC4airZHd8mhs9+m7HiUXY+O/gqVcNx6GCFNmzppSw8jRdGdAT++OFFiPXw/NRVFMXQ9nPwUhLdz1U9JdCvXNNSkpeQXfpRdoFppT2w5E4mha2FObws2kVkFhkbII4uKWvfUdsVloKKOqZfuEKBoe2rC52YmdUJoW0mI8HxCiMd9KZN82Dfed24C3cRONWGnmhgYtE/3HWUzcasTOFVGlT7SdiKDijeA7jjK3uI7QJCYvBmJ7xReuD4WOcWFTaNNkK1mSNAkkWhgOLEehP552/Nif7vnVy2XGsTfrbEtMvbM60Mi5dWJp7XR9zgPfJ0MNTUW0+Gi7zXzsEisS/pJEJEtCHUxDTXxZiJtOVvDkAl24Xp30aFzwKbi+JRdD13TSJk6uVAQpwydbMqgkFZlxqPJR9GEIz/0Clcc5c2vuD4DoS/Yju4WeD6u51NORPw938cLbTnJOI+u1YrwaHJT8nlNyTYwIYwkQh4EAWXHr0bEw89EMkKe3K64Q1MeQmhbKTl0b0HaQ4C0qdcI8nwqGRk3QkEeNWNMP0u+blFJtdT3jgc+plcKvd6l2HJiekVlTXGLKouKV44nbkZi3PBKgE4l1UTFasYzts5KKEw+an3zcc+oCAjTJyaskdWJrdUsNfHE1qAauKk3viGRdi1hoYnsNTWR+ORjtEF9aiUW3VTnl0U2x8FiPXps0u5THXftRQkBWJVu0oHNTmWXxlG/m+OHiGxB2I7Y1AQ7zw9URNJ20TVIh1aSaAb/1hJNiq2ElpuKo/zxjleN/A9eLzseFcen5Hp4no/jB1Qcn76yQ8UNqLhe+GOgqei7GYlwIxb+piHR8YlG0zSyKVXJcMZmPOSg5lYMRFHwhCCPlwlLy4CtSqfXQ33WbDo3UxgoIm3qQwT50PUxiJRrOq6ZxzXzmzxM92wMvxQK8DKGVyZtd9Iw8DIZu5OG4ir0wKViNVKxmjd7PmHHI4pkj8Xch3qR9ihFI/XsNOFxqkxAQBCmcKxnqYHqY5NzsJKWx829TohuPlSj4xmnTKPp0SQiWxCEyYChazTlLJpyYztJK4o8N2zl411PeeEHKh4DthuLsKKt/L/dxWoEvmi7dBX9MGoexBEOU1f5fWuXVZ9idKs0Th8m4nxCsAyd5lyK5tzmj40yrVSFuKfW7VpxHgn2ol0/2wpURfnmqnVGqHLoRo34zqUGCfOUQS7cn7FG5in3jRS+kcKxar07G1pfR7a8gXxpNQ3FVWq9vA7DK2NbDVSsZhyzQfzdwpgyHpH28cbvt8CtX39gMiEiWxCECcU0dBoMXfmF6xAEASUnFFKhEI8i4WVXLQdsFf2MrCtRdb2K68ae8+jWauRpr/6WRNYVqr75hGd+iI8+4a+PhHuUelMYO5KZVloLm4+S+0FAObxYiwV44qItipIXt0CU256PXfTp2oKsK6B8qvmUSS60quQSYjyXqtpXcom+etHyQDMoZmdTzM6mveU1ZOwO8sXVFEqvkiutJeN00VBchWvkqKSasc0G8XELwiRG/joFQZjUaJpGLmWSS5lsLlzueMqyUna82LoSTf5M5n2PIuGuX80sE7UoB7wXCfNw8mmUOjK5LzkZKfacB8k8vInJn1ptjvZqqWyZJDoW6InPyZZYVyI/eT0xrvq8eN5BtF6vUBCoLBR9YXYW2PxET6hGy3PJSHkYHY+EeS6VIZfek/y0fWjSBmgqryVfWk2huIq000OuvA7QsM0GbKsBxywwUWXoBUEYiohsQRC2G1Tpan2LJu0Nh1szkTPMsx7mZ3cG7XPjVJGD0kK6YZQ9FPhxNpowR7vnKwtDPDG0ziTRIKjO1Dd1jbRphJFdlZ4xZeoixkdB0k8+nS0T5bbnx3dTIkFeTApy2wu31f6SM/zt7JFGywEylk4uNZeCNY8G06VJL9NMPy1an2r0UjB9cmmLbDpNNmVhGvIZEYSJQkS2IAhCApVZZuxywEf526M87U5iAqjj+TWTRFU+92rFVscLlBc9FHBl26On7LChrxxn6kibBhlTJ20ZZEN7hWWIfWWs0TR1oZM2DVryW5Z2yPODcL6BS9GuLouJaHnRrs5BGLBVVd3hKDs+ZcemM+6xgJaw1SerOzQYHgXTp8HyKZheuO3RkGhRf4PpkdYDsX0LwhggIlsQBGEcifK3pxjdbfyirao59pUdesNy671Fh40DFUqOR9nx6CyqPOm268fV6iyjOhnUSk4OrclvLqJ8PDD0LU+HGOF4fizEk8JcRc3DZbS/onKR297wwrzkW5R8iw0OUNqyMViaEuMFw6PB9GNBXu2rivRCKNgLpocpHyFBqEFEtiAIwhQg8hvPbKzNn+z7AQOxAA9zpFdcKo4Xi7Sy68eTQV3fp2i7tZVPfVXRzghzpqfCtI5RusRoe7xKpQtVLEOnKavXTcM5HElhHkXFYyFetqmUByiXbYqOx4Ad0O9qFP3hf/6dQKfL0elyRpaJKKsnxfdQcR5t5xNCPW/4UpFQ2G6ZsiL74Ycf5uijj66779FHH+XQQw/dxiMSBEHY9ujJao7D4HrJyZ1eWFSoOkG07Cj/cF/ZobfkhtVNVUS8v6K2Hc+Pc5enwkqgUWaVZDYWXauficUyNSkwNE6MVJgbXgmj0oNT7MEp9lEuF7FLfcqW5AQMuBp9rkGfZ9Hrpej1U/R6afo8C28Td2RKvkHJNmjfskyJMXmjNiJeCO0thVCQD+4rGB55iZwLU4ApK7IjvvCFLwwR24sXL56g0QiCIEw+ogqm+c3P78P3Qx+47VEKS6OXHOULLzmqlHpv2QknbqoJmtHETi/MtuIG1UwsUYYW2/XD1ImJAkOWUVNsSET4tsEzsni5LORmYQIFgCCgzR0g7fSQcnqw3H4Mv4Lh2Rh+N6ZbxHBLVDyffkej39XjZZ9rMOAZ9LkG3UGe3iBLr5emP+wrejrBJnIvD3jq8etHKM5V5NyPhXgsyE0/FuJq6VdFvCGec2HbMeVF9u677y5Ra0EQhDFC1xMpEzdBNKHT82ttJyrDSrieyMQSWVd6yw7dRYey62E7Pv1lh45Elc9YhCcEuGRT2QZoGq5VwLUKDLBT/WMCH8O30f0Khu/Q7FVo9Svovo3lFsmXVpMtv0La6cFy+vCMDBWriZLZRH+Qpt9VorvfMxhIrPe7Bv2ePqRvc+I8jpwzMluLoQU1IrxWqNeK9qRAzxs+lr65GoWCUGXKi2xBEARh2xNN6ATIMrJMLL4fKH/woKqNg0V4xfHDbCoVlU0lCJRX3NLJmEaNGN/qUujClqPpeEYGz8hQL/FgOwdhOT3kS+vIl9ZQKL5K2u5mWvFFpgUBttVIJdOEa+S2qGqlH0DRU5HyKFo+4Bkqip4Q44P7BlwDbxPi3As0elyTHnfkEiil+UMEeD4U6bV9gwW7CPQdkSkvsi+66CLe+c53ksvleP3rX8/VV1/N4YcfvsnHbNiwgfb29pq+FStWjOcwBUEQhBB9M1k3osmcSnw79JWr650DNn1lN/SS+/SUlHXF8aoZVZT1JMymYmhx/nQrkVlFouLjg2M10W010d24J7pnkyuvI19eS37gVbL2RnLlDZheEcfM45gFHDOPp2fqim5dI/Rh+8yuK+vrEwRQ8nUGXD0U4AkhHorw/nB78P6Sv+kLRjvQ6XR0Okc4KRSqWVvyhk8uEUHPGVXhHq3njFrhntV9sbhMQaasyG5qauLiiy/mqKOOorW1lRUrVvCVr3yFo446invuuYc3v/nNwz72pptuYunSpdtwtIIgCMKWUjuZMzNkf8VV3vCBikdfxaG/7IZpDP041WHF9ao5yF2fgSijSphlBarWlJRZm0klyqYiQnx0+EaK/vw8+vPzYNrBZOxO8qW15EpryJfWYLn9FEprMLwyvm4p4W3kccw8gT5yERuhaZAzfHKGzwzcET3WC0KPeI0Ar42SR9tFr2pzGfB0ypsR6NWsLVvxmgjiyHhuUOQ8Euf1tiPRbmniQ58ItCAItpv7F93d3ey7775MmzaNJ598ctjjhotkn3766SxbtoxFixaN91AFQRCEccTxVNYUVcDFo+JW1yMx3l9x6S46VFyVa9p2gjiriu35BICpRaJbRcENXcPSVfaUKP+4oUuu8ZFiuEUydqdqlQ5y5fVYTi+W24/lFdF8D9fIhhHvPK6RnfQl490AipG/PCHIB4aI86qAj9Y35z8fLabmxxH0vOGTS0TUo2XO8OuK9NwknCzq97eDW2b+my5gzi57bbPnXb58OYsXL95irThlI9n1aG5u5pRTTuE73/kOpVKJbDZb97i2tjba2tq28ejq4/gOGhqmvl39VwiCIEwokUWkYWggvIYom8rg/NIDYaGX3pJDb9lREzj9AM8LqLh+PMnTiyt6qug4EEfICxllicmnTYmKD8IzcwyYOQZyO6uOwCft9JCuRMJ7I7nyekxvgFxlA5ZbxNd0XCObaDmCSfTbaWrQaHk0Wh6MwN4CVf95Mjpe9EKRnhTrnk6xjnh3gk1fgLiBTo+rb5UPHcAgIGcOFt/1RHr9fVnDx9gB/wQmz6dzjIgC81MholDxKvxmxW8ouSUaU400phrJWllyZo6clatZZszN/FIIgiAII6Y2m0r9HIeer7Kf2K6ynkQ5x6OId7XfiwvDdBWVd3xDX4ViRxEz9KE3hMLblImatWg6lVQLlVQLvewKgO7ZpO1OMnYHGbuLTGUjabsL0yuRcboxy2vRfRdXT8Wi2zWyeEZ9j/dkJuk/35oQoO1rsUgfiEV6fWFerLPf3YxI99Doc036Rua+qSGt10bHB4vx7KC+wevZKVi4aLsS2V1dXdx9990ccMABZDKTX5RWvArdlW7W9K/B1E1c31VlkHWLlJ4iZaiWNtJkzAxNqSYKqQJZM0vGyKilqZbRuj7Jb6cJgiBMNYxYiG/5YxzPp6Pfpr2vwsb+Cmu6S3QXHXrKDqu7SwRBQD5t0ZA2KWRMMtbIMrTsCPhGilJ2FqXsrLhP9yphLu9u0rbK6Z0tb8ByB0Lx3YXplfE1HV+38DULXzPxdRNfMwn05LY15cT4cKT0gJTu0Wx5W/X4TYn05LLoJtYT+yr+5rVHxdep+DqdW+FJj8jqoeDW5vL5nR7b+hNtI6asyH73u9/NvHnzeO1rX8v06dN5/vnnuf7661m/fj233nrrRA9vRLRkWphTmAOoSLzjO9ierZpvM+AMxOtBEGBoBpZuYRlWvEzpKSzdIp/K05hqjEV41sxi6ia6ptdvDO0zdVMJfD01Je4ICIIgTDYsQ2dWU4ZZTSrgEwQB3UWH9v4KG/sqrOsts6GvQn/Z5eXOASqOyh5h6mFJ+9ADnjJ0rNATboUe8B35e9k30pSMNkqZRLw38LHcftJ2D2mnm5TTQ7rSheX2YfgVdN9FD1xM30HziuiBG/cR+OoUmolrpHGNpAd8x3mfRyvS3QBKNWLcqBHtRU+nFK9H+2qFuhds/v0u+QYl30DHxNL8rRrrtmTKiuz99tuP22+/ne985zv09/czbdo0Dj/8cH7yk59w8MEHT/TwthpN0+II9nB4vofjO6p5DrZvU3JLOJ6D0+fgBR5BEGDqJpZuxeJZQ305R0sdvWY76ouEtqEZsX0lY2bi6HraSGMZFmk9HY81Y2RIm2nSRlqi6YIgCIPQNI2WfIqWfIo9ZjYAULTdONLdXXTC0vYqX7jKgqLsKAMlF8cNC/v4akKmTpiq0NBIhRU9U1HKQl3fscrYazqO1YhjNdLP3JpdumerypV+BcOLlmUM3w6Xqt9y+8NKlwOhF7xEoOk4Zi4U3rlhUw0Kyo/eYPo0mFsnfIMAnECrEd6bWvecypT4r5iyIvvyyy/n8ssvn+hhTAiGbmDoBpk6qa2SuL6L4zv4gU8QBGpJQBAE8dLHj7d938fFxfM93MDF9V28wMP13djrHgnwSISbuompmXGfpVvkrBwFq0DaSJM207EAzxiZqkjXrZpovAhzQRB2NHIpk/mtJvNb8zX9nh9QdlQ5+5LtxRlRSmFfOSzk0xvmCI9SE5ZsD8f3ccK84a6v8lVEmVCSKQqTKQu350mZvpHCN1I4NGz2WMMrkba7yNhdpO1OsuV20nYXllskXVqD6ZXxdAvHyOGaeTw9RaAZYdMJNLH8bC2aBilty6PpKrvINhjYKJmyIlvYPJHwHUv8wFfiOyHEXd+l4lXod/rVdthPAAEBlm7FAjwp0A1NXSwYmhFHwzNmpmqDCVskzAcvI7G+I986FQRh+8PQNfJhVpLNUZuasCrGy4nWX3HpLbvhBE01gbO/4obrKuASFfGJhbihx5HC6DtWC/+JvnGT/ZpG4nFT8zvZM7IUs1mK2Tlxn+kOqMmXlU4ydhfZygZSdg+mVyTt9KAFHlrgqyWqKqlCI9B0/IQA9zULX0/hGSk8PY2npyZ9WkJhdIjIFkaErunKyrKFF+xBEMRC3PGdWIBX/Aqe7+EFYfM9/MCPrS6g0mBFIjwS53HTqpHzrJUlb+arwjsU6UkRb+nWkHNF+03NjPdLRF0QhKlE2jRImwZN2U0XbwmCgLKjivKUbJWeMLneV3bi6pl2mCUl0otBGDCBMElhoLbiIhsB+EEQZ17RNDWuTKLsfcYypmTU3DXzuGaegVxoQwkCLLeXjN2F4ZVjb7fuO+iBE2476J4T+sGVPUUP/PAYm5TTq+wqvg2BT6CZeHpKNSOFHwpwT09v9/YUL/Bx8LBxcQIPnwAPv9oCte0n+twgwKMbNIc2rzTRL2GTiMgWxhVN05ToNSyy1M9bPhyRxSUZHY8j50GlZtvzw9tLGhiaUZ3Uqdef3Bkdo2lavG5ohppEaqTI6NWIelLcJ6Pygy0vyQi85D0XBGEyoWka2ZRBNjV8hCQS4kVbVdAcLLLVktBiGAVtVb/rB3G1zd6SQ+eATdHxqDg+PWWHDX0VbFdZFi1DJ22qsWQt1VLmFAlwaBqO1YRjNW35Y4IAPXAxvDKmO4DlFVU2FHcAyxsgZfeScnvRPRs9qJByekIRXoEAPN3CM9J4ekZFwI1MGAWffALcj0WzhxN4OKhmB264rsR0JXAp4WDj4gVVAe0TEBpba9cDta4+gRoeRRo1gx67d6Jf8iYRJSBMWiIBbOgG6WHy1w4mEuZRVDxar2moZeRXj48PI+vJvngsaOh6KM7R46j4kGW4bulWbH+JbC1DRLphqSi6USvcoyi7pmnxxUL0XkzV27CCIEx+tkSIbylBEIQRcpf+ihMuXfrLLh0DNv0Vl7Lt0V20WesoC4up62RTBhlLJ5cyyVhKjE95NC20ilg4VgP1Yq+a72J6RSy3GItvy+3HcvrI2F2Y3gCGVyHl9mLY7SoKDnhaKhTgaVwjh2Pmx0R8+4GPm4we48dR56RodkMxXQlcyqFodhPRZ7cmKu2HMhp0TcNAr22ajolGmKYBPbmuhevha+t1wKYy6tc53ojIFrYrYmGOgcWmb5+OhMHiPbK4JNftwI7X3cCNLTCR/SUgiCPmhmbEoj3Zor44A0xoX0lmh0mKeVMzMXSDnJUja2TVMkzdmGyGvh38UAmCMGXQtKSvfOgk/ZLt0RtaVHpKDj1Fh46BapaV7pJNqScS3xoZy1ApDA2VNUUtq+uGrk05K0qSQDdxdJUhZejOAMMrkXL7Y+Gdcvsx7F5MeyOaVwS/hG6vQSsOYOspymaespnHMSyC0ILhx+I3smQEsfj18HECV0Wg8fDCGLJ6TBRZropmP2HlAPX/bar7xqFo1jA0nTQmZkJEGzuYJVNEtiBsAWMl3qOIeY1YT3jSHd+hHJRrM8BEWWGizDBBULMdif+okFE0WTRZ1CjKnx6Jbl3T4wuAyGtZk31m0HOkjBQ5MxTwYVrHrJklbaQlui4IwoiJIuYzG2sFeNlRpex7Eq2j36a7ZOO4AW7g43o+ZTfADTOoROXto+8yXQsFuKFyjlthWkMzzDUeifWJFOVBEOAFLm5g4wVOdek7ie1Q0gYufuDhBS5e4OBg4xkOXtYlyGTRPB3NMzA8DcMNsJxeNL8TrVKBwMXVTVUVU7fwNQ0/sv/UOOs1NI1QJmuDmgryGJpOKpTSJjpGIhAk1EdEtiBsQ3RNRx+ncspRIaOomJHjO/Q5fTgVB7vPxvNVPMLUTTS0xESmIM4EE0Xd0aiuh+NO6anYsx6tZ4wMjalG8la+JoqeMlIq3WMYaR+cTSbZJyJdEISIjGWQsQzaBolv2/Uph5lRKq5PxfHikvZq28f2lAe8FKY4LEepDMOc40XHx/GCcLsqyquRcR1dd9E1H0330HQXTfPQNA90DzQXz3eU2MUlSFgKAwKGEtSsBYGH69tU/BJOUI7Fsx+Ehopw6YV9ASr4ksjtEta20NE0FTOOtnXTRDNb0NKt6EGA5ZVIu0XSbh85Z4CUX8F0SuiBhm+kCfQcnp5G21FyqU8QIrIFYTshWcgob+WHPS7Kex4VIooeC8SWlMH4gR8Ld9uzqfgqZaPt2zieE58viqLHk08jT7leLXQUF0cKix9ZhhVbZHRNV5NXw/Q1uq6H3jzVH50ryhiTzDYTedujdUsLve4J73v0GIm+CMLUQX2/eKRMD9PwyaXBD8AHgkAjCPTqOgF+YOAHJrbr0G/bDNgOA3aFouNQtCuU3Aolx6HkVCi5Nv2VEl12Gdsr4zouju/iJi2BYc0Iz/fwNZ8g8NC1AE0jtPWp6LmuhdFgPfIPg574Og0Ij8dA18KGGa9bWvTdGe0z0EbxXRUAZcDxbGynl5TbR9ruxnT7sfwShtMNaPiaGWY1SeFpFoHYC8cMEdmCsIOxNZlPdE0nY2Y2WQDJD/y4Aqnne/EEUz/w8X0fJ3BqJp5GLWlNAWo87MNtRxcHQOxdT2aJ0TW9Jloee+Hr5WWPJqEOyhpTk4lGH3r+SKzHk1QHVVCNffSJSqvRxYUgbM8kLXGDJ6In+6LvC9uz4wv2aOn4DmW3TMktUXJLKr1r4m5bVEwtWo+/Q4LqdvI5a+bRhHf1ADRTI2VpzNAMAjS0wCBAB99Sy0BX4t038AONwNfxfA3HA9vz8fwA31ep6PyAcDtQfmU/wEsEuA1deccNTauu61roX9bQdW1cEoZ4RgrPmE6Z6RCA6RVJOb3VDCfuQFgBs0TK70HzPXzdxNctPE2Jb1+3QL66RoyIbEEQxgRd00mb6S3OBDNWKG9jeIvV94f8sEY/5vU88Mm87EAswuNoO1rtclDGl+T6kLsCWiyxwwIe1WOiSaumYcZ52qOsMskLhMFifvD64Amwg48RW872R1wQLKj9DA+bTSmZQSl5DOriN7rojYRn9HcU9cfzPvBr5oAMHkMkYF3fraksHJ+DQcs6j0sK4Hii96CL5eH+rpIXuFF/Wk8PnWCuj+HfQwCuX/WFOwmPuOtXrSmuH+C4PpXQ3uL5AZ6vJg7aTrStxHn0bRQJcFPXYg+5pesYujY6sauBa+ZwzVy1y/cwvRKmV1RLt4Tl9oVl6G0sp4jhOwRo+LpBoJlhkZ1q8zWTQKwnQxCRLQjClEbTNCVYMbe4SFI9kj/8cZSdqqgYLBAClAc+jqglJhMlo+816wn/++DniCP0CT98PXE/XO732EqDUfuY8CIhjtZr1pCCTfXsM8O9hmgZjSGZXjIeYx1bUL1xx3nqqXOeQZl1ou3h7hokJwvHk4KpZvepmdgbLpMCNRaqofiMIqfJdJ7xGNFqLsYG59yPxlo3mps4d70LQ8d3VIaiUHQmo72O79SI2+Rnz48yCvtBdb3OBOrBE5yj9yh5XPQZ1DQtaStW4k6lKR4ibOP/i8T/TfL/X9Nq9xu6QSq0RyQvGKfUBaEGpqFjGrCl8+H9UITbXuQXj8S5j+2qdccLqLgeFTcU6r5PpRIGC/zIR66Fz6285Jauqcmc+sjfu0A3cPQCjlVIdAYYfkUJbq+IEYpvwy+HRXU8tKgIj++h4aH7HkFY6TLQjFCQG6EgN5UQ14wdKiIuIlsQBIHaDDKThWQ0cFM53yPB5fneJm05g6OTSdtNgBJVSYEfM2gibLW7OoE22h4spmoijUnxVUekDRbWNZHKOncEIjGma/oQK0H0mmom9g6yJUXiOSk8h4hxAnxfHRfNEahnAarpIxTZ1F5EDT530i6VFOLJ9xONuoW0ku/h4IuOZBrQeu93csw150msC+ODrmukdI2Utfmor+erKpplx4srcZYdP67c6YRC3XY9imE0PBLhmlbHnqJpGEZ1e5OCXNNU0RsjQ4WW2l2+i+E76L6D4dtogRNu2+i+g+lVMPwyWuCh+y6G76JRQfPVNgS1QpwwMq4bBFTL0G8PYlxEtiAIwiRlsPic7AyOJteLHg8Rs4moaixqg2pUtp5QrlknEflPTuAdLMq16oVDUlTWCNB6kdiE6NzU6xocEQZqJvRu6tzR+nB3FYQdE0PfRHGgABxPZVypuAG2E0W+VfTb9jzKtocTecZD+0rZ9ZVVxQ/wq8mkYtFdFeBgaLpa6lXPOKic3q5uwqaqOAdBVXgHDoZnx+u672B4FUyvhB64aIGH4XtoXgXN99Hw0MILWzQI0GPhrZa6qoY5BRCRLQiCIIwJsTd9O4hACcKkRgPL1LFMnYZNHOaF1pQobaFTs1R+8SgNohNP5FQTNyuehxeE2wlRDqEo1wg979QI9CiK7msWupka/vsgQIluz8YIxbeKiruhFcUNy9Hb6H4FLfDQAh898GIbCpM8E4qI7AnG9V3lsfO92okbgiAIgiAIo8AwNLKGwZZMWPF9lBj3g9qlV932Ep7xwdlVvCDAcfw4s0oQZluJxxIL82pkXBW0yaDrGQxT+//t3X1wVNXdB/DvvfuaF0kgBOPia0VF3mTacZq2VmjxBSkpIU6sTjsyMIUppK1ULVBtJ0HAdmw7lepo6ygFBqFKCTjS2DcDdjqTcUrRqVqkpZYWdRBIBPLCbrJ7z/PH7rk592WTXbib3c3z/czsZPfcc+89m5Ps/s6555xrLouoIdmbbhJIBt6p4Lvr3EnEEv1IlE3w/pfmIQbZefbi4RfRGe10pKcbr5huskkm2x3jI7P56cE29dKtvUGRyTEApD2n4xhDHNtMczmu/RxuZR4uj/2cjuPb8qn7pyvLkM9dzmseG4OX0ImIKHfclhu1D2myD3ly5FPnFtifDzFsaqh90m6zlc9xXJmmCcCfzKMHgKAQCKS2x1OrpCQSyZ9xIzkcJS5XUEkNTUkIw5zgaaR6x424nKsgH4MTyA2zrDJIT5ZFAwBNIG7EMLFkKlDgw6sYZOeZOmnInm5ONHLPQpQVe9BtbySYeeyvbfu4vvbyOMknjmOlyzdUOSx5lfNlnO5y7mHTFfYGTtbbbe/TLY9regb5h9uWrozD5s/gmCMl3edr2vwukzszPaYlXaRJHya/Pa/jtbjA7YMnynib5bktiLSf07Gf27Fcfjdquj3NrSzDrqVvD2LdXg9zHLd9hjsvjSQN14VvQFmIw0VoCDOrZ+Js/1lcFLzI/Cd2TAjK4Ge6CUZy9nza/WGd4T5ka1bZllFamjyW8ijbKbfsXwzKBiIiooJmv1o9LVKBMeEM107MEwbZeTZ1/FSci59DpDyS76LkndtlrEwDdNfGga3nwT7z3x7gu54rXRmyeG4/vlu+dGV27Jfu8h6cvSrDrXPsdv6MXw9zvgs5Ttq0NPu7pg1zXNdj245lT0+7n3Cmu+2bdrtL2dy2u+UZKq9repr3kU7aPGmTC6/FlrZnPW1yBr37WV41SJvucpXgQq92nPf2NHkyvTqUzZWhTI8/1LFzchVNHbpnv0pmG6JnyZvm+BldPcxw6J9b+cxwM4N91HMNt/+Q5x5uSGaWeYcbTqruq+qKdiEWL/wVRhhkU8Gwj1MmIiIiKlaFPWKciIiIiKgIMcgmIiIiIvIYg2wiIiIiIo9xTDYREWVFrkgEDDPBN8P1f932kzKd1JlNeRyv00x0lhNhBQQ0WG+PLm/J7niktmWy9KH9PQw3udAt3XWiWIaTx4gotxhkExEVqHTLYVq2pQsSMwgcZR5DGOYjIRJIiIT5XKbL/czATV1NAEOsvjDUjZgA1yBQPa46CVoNMO3U4FJP3SpOT12szfYmVJqWDKh9ms8Mrg1hYMAYMH8vAgKGYcCA8rszEhgQA+aSqvbyZUKtF3u6+dyl8eK66pJtm708AsJsNMg6lc/VhoNP8zkaE25pOnT4dB+DeqIUBtlEROdBCGEJQs3A1HAGqZafxmCQpgbEMuiR1OBPT93VzLFUVmq7WyArX5v7poIeHbp1uSxo8Gk+BPRAMnDSkwGUT/Mh5Ash6Asi5A8hoAcQ0APwa374dJ95h1nZa2svm64NnkfmleWxL91lBnlD9LxaAng1XclnP7bltUua+dz+eogAUdZxXMTNuk6IhPk8bsTN+nYr/3DUnnbZsAFsPfXK3456nwP5t2h5LoSlISDLL/8eZXkHjIFkI8EYsPxdm42K1N+72pCwN8TUh2yQyfLLQFzTkn9vjkaZS31a6j3VAPDpPjPAl8/Nhz4Y+BMVAgbZRFR07IGF202Z7DdoSnfDJvWngcFjyYDYgGEGxvILXwYQ6pe6/KJXe/n8ut+RHvANBqt+Pflw6xmUgYiargagcliCPUhVg191mwyuZY+j5ViabimPfJCTrunQfToCKOybYFwIM6BOBdVxEU8G40YyKJcNDBmgq2lq4K42RgaMAQwkBtBv9JsBvdobn3ri6HGX2+X/tHrFIBqPDl55MQavwqgNEvWKSLorGfZGn6MR6/Y89f/v1/2W/52AHoBPL+y7ENLI4acoEQ1J9sh5cQlY/aK094QN9yVtCMPRe+sWLDp6MJEmr27rxVQumcveXb/uT/bk+kLJ4FgLWIJQmSegBxwBqv2LVw2oiQqZpmnJv1/4gRzFi/a7EQPOmzvZh0bFjTgGjAHz50BiwPLaTE9tk41mQyQby2YPvpFAAoOBudpbbzm37UZX9huiySFEMSOWbGikGhtxIz74e1Q+A9SrSObv2tYIUNPsn2lmY1t5bZ8jwGE6hYVBNlGRkV8a6peDWyBp7/m0HyMhEuaXlOORGOxpkh/qhjCS42OVLx71C0INUs2eZJexpbqeGruZGpqgBrQ+zYegHkSJr8S8FBz2h1HiL0HYH0ZQD5oBq9sYUXvPr9uYUfVytTlRTbdOWJPlYUBMlBvm/1aBxYTqlTF1bLv6eSavig0YA+hP9CMajyKWiCWfJ6Lmz2g8it6BXkTjUXM8P2DrrXe5y6zasy8/h82hP7KDAoY1TWlQqCzj64easJturL3L5yPgvHMmuWOQTZQj6ge120QydQyvI82WT+3dcRumID+EIWAZ8mBfBULtQfHpPnOcrRzCUOovRUAPoCxQhvJgOUr8JQj5QtA13XJJ1jFRznZZWNd1hPRk76/s1ZVBs70XWB1CEdSDCPqSgbT8yQ9xIhopcpiVlxJGArFELPlZDufkVteJrMp3h/zsjYt48qqfETc/f9U0NZ96VbDf6Ed/ot/Sw28Iw3Xibr/od3TkqIG9fZ6AOvbePpxOPpdc02yTknU9/fA7dTx+NB7NeJ5DPjHIpv9X7GMN3YLa4Sb0qGN71Q8Xt9n75uU8Zea9vbfAp/sQwOCkM7k9qAfNiWdBX9B1KIIc/2cZrzjEpCWZFvQFEfaHkw/f4E8ZVHNMIRGRN3y6D6V6ab6LAcD6HSiHuJhD9GwTet3G3wOwfAfKYTVq5448j9pzrw6/ccyFUSboyisC5ko+6vd0qhEgn1eEKgr+aiODbCoobsGiDBLVy2GWy2cu6fZZ/5LsBZYz0dXWsnwE9aDrsAO1l1UdrqDOercH1nIJMDUgdptgxklnRESUa+p4+5AvlO/ipCUD/oHEgGWsuxzSGDfi0KBhQumEfBd1SPwmJwDJ4Fb+IZ/Pyg32Vqo8JuB+Ewp7b6skLxf5NJ+l59cSvCqrJeh6cpa/fX1Xn+ZL9tb6wggHwoPDImzDI+RDDp2Qwbc6xEH+JCIiotzz6T744CvohkAmGGQXgDOxMxgwBqyrIdhWRrAvIWQfx2SfmaxuV1drMGdAK5eBZD6/Pjhu1m3inBnsqr22tjV3ZRnVskqynDr0ZI+wL4CwHjYDXhnomr2+qbG6jrFZum24hcuyaZy0RkRERPnEIDuPwr4wqsJVCOpB6zglOYNZmWRgQFnuyDbWabgeZBk4B/UgSv2l8OnJ1mGpv9QcgyuXKQv5QvDrftfhEunu+pWuYQAoN4dQGgK8WQARERGNdgyy8yjoC2LhNQstS/RYhmTAumSPffgGAMcNNNyWHvJrfksQLSfREREREVFuMNLKM3n3MCIiIiIaPRjdERERERF5jEE2EREREZHHGGQTEREREXmMQTYRERERkccYZBMREREReYxBNhERERGRxxhkExERERF5jEE2EREREZHHGGQTEREREXmMd3xMicViAIAjR47kuSREREREVGhkjChjxuEwyE45duwYAKC+vj6/BSEiIiKignXs2DF88pOfHDafJoQQI1Cegnf69Gm89tpruOyyyxAKhTw99pEjR1BfX489e/Zg0qRJnh6bCgPrePRjHY9urN/Rj3U8+uW6jmOxGI4dO4ZZs2ahsrJy2PzsyU6prKzEggULcnqOSZMmYerUqTk9B+UX63j0Yx2Pbqzf0Y91PPrlso4z6cGWOPGRiIiIiMhjDLKJiIiIiDzGIJuIiIiIyGMMskdAdXU1mpubUV1dne+iUI6wjkc/1vHoxvod/VjHo1+h1TFXFyEiIiIi8hh7somIiIiIPMYgm4iIiIjIYwyyiYiIiIg8xiCbiIiIiMhjDLJzqKenBytXrkQkEkE4HMbMmTPx61//Ot/FovPQ3t6OJUuWYPLkySgrK8PEiROxYMEC/O1vf3PkPXjwIG655RaUl5ejsrISDQ0NeO+99/JQaroQzz77LDRNQ3l5uWMb67h4/eUvf8G8efMwduxYlJSU4JprrsG6desseVi/xeuNN95AfX09IpEISktLMXnyZDzyyCPo6+uz5GMdF77u7m6sWrUKt912G6qrq6FpGlpaWlzzZlOfTzzxBCZPnoxQKISrrroKa9euxcDAQE7eA4PsHGpoaMCWLVvQ3NyMV155BTfeeCPuuecebN++Pd9Foyw9/fTTOHr0KO677z60tbVh48aNOHHiBGpra9He3m7me/fddzF79mz09/fjxRdfxKZNm/DPf/4Tn//853Hy5Mk8vgPKxgcffIAHH3wQkUjEsY11XLy2b9+OWbNmoaKiAlu3bkVbWxtWr14NdZEt1m/x+sc//oHPfvazOHr0KB5//HHs3bsXd999Nx555BHcc889Zj7WcXHo7OzEM888g1gshvr6+rT5sqnPDRs24L777kNDQwN+//vfY8WKFXj00UfR1NSUmzchKCd++9vfCgBi+/btlvRbb71VRCIREY/H81QyOh8fffSRI627u1tcfPHFYs6cOWZaY2OjGD9+vDhz5oyZdvToUREIBMSqVatGpKx04ebPny/q6urEokWLRFlZmWUb67g4vf/++6KsrEwsX758yHys3+L18MMPCwDiyJEjlvRly5YJAKKrq0sIwTouFoZhCMMwhBBCnDx5UgAQzc3NjnyZ1uepU6dEOBwWy5Yts+y/YcMGoWmaeOeddzx/D+zJzpHdu3ejvLwcjY2NlvTFixfjww8/xOuvv56nktH5mDBhgiOtvLwcU6ZMwbFjxwAA8Xgce/fuxZ133okxY8aY+a644gp84QtfwO7du0esvHT+tm3bhtdeew1PPfWUYxvruHg9++yz6O3txerVq9PmYf0Wt0AgAACoqKiwpFdWVkLXdQSDQdZxEdE0DZqmDZknm/r83e9+h2g0isWLF1uOsXjxYgghsGfPHk/LD3C4SM68/fbbuP766+H3+y3pM2bMMLdTcTtz5gwOHjyIqVOnAgD+/e9/49y5c2Ydq2bMmIEjR44gGo2OdDEpCydOnMDKlSvxox/9CJdeeqljO+u4eP35z3/GuHHj8O6772LmzJnw+/2YMGECvvGNb+Ds2bMAWL/FbtGiRaisrMTy5cvx3nvvobu7G3v37sUvf/lLNDU1oaysjHU8ymRTnzLumj59uiXfJZdcgvHjx+ckLmOQnSOdnZ0YN26cI12mdXZ2jnSRyGNNTU3o7e3Fww8/DGCwTtPVuxACH3/88YiWkbKzYsUKXHfddVi+fLnrdtZx8frggw/Q19eHxsZGfOUrX8Gf/vQnfPe738XWrVsxb948CCFYv0XuyiuvREdHB95++21cffXVGDNmDOrq6rBo0SJs3LgRAP+HR5ts6rOzsxOhUAhlZWWueXMRl/mHz0Lna6jLHMNdAqHC9oMf/ADPP/88nnjiCXzqU5+ybGO9F6ddu3bh5ZdfxhtvvDFsPbGOi49hGIhGo2hubsaaNWsAALNnz0YwGMTKlSvx6quvorS0FADrt1gdPXoUdXV1uPjii/Gb3/wG1dXVeP3117F+/Xr09PTgueeeM/OyjkeXTOtzpOudQXaOVFVVubaKurq6ALi3uqg4rF27FuvXr8eGDRvwzW9+00yvqqoC4H6VoqurC5qmobKycqSKSVno6elBU1MTvvWtbyESieD06dMAgP7+fgDA6dOnEQgEWMdFrKqqCv/6179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termestimate_typevaluepersonscamperestimatelower_3.0%upper_97.0%
0livebaitdiff(0.0, 1.0)1.00.00.8644080.6270631.116105
1livebaitdiff(0.0, 1.0)1.01.01.6946461.2528032.081207
2livebaitdiff(0.0, 1.0)2.00.01.4245981.0783891.777154
3livebaitdiff(0.0, 1.0)2.01.02.3444391.8721912.800661
4livebaitdiff(0.0, 1.0)3.00.02.4294591.8715782.964242
5livebaitdiff(0.0, 1.0)3.01.04.4435403.7478405.170052
6livebaitdiff(0.0, 1.0)4.00.03.5419212.6864454.391176
7livebaitdiff(0.0, 1.0)4.01.010.7392049.02470212.432764
\n", - "
" + "data[\"style\"] = \"Other\"\n", + "data.loc[data[\"Action\"] == 1, \"style\"] = \"Action\"\n", + "data.loc[data[\"Comedy\"] == 1, \"style\"] = \"Comedy\"\n", + "data.loc[data[\"Drama\"] == 1, \"style\"] = \"Drama\"\n", + "data[\"certified_fresh\"] = (data[\"rating\"] >= 8) * 1\n", + "data = data[data[\"length\"] < 240]\n", + "\n", + "priors = {\"style\": bmb.Prior(\"Normal\", mu=0, sigma=2)}\n", + "model = bmb.Model(\"certified_fresh ~ 0 + length * style\", data=data, priors=priors, family=\"bernoulli\")\n", + "idata = model.fit(random_seed=1234, target_accept=0.9, init=\"adapt_diag\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The logistic regression model uses a logit link function and a Bernoulli likelihood. Therefore, the link scale is the log-odds of a successful response and the response scale is the probability of a successful response." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " Formula: certified_fresh ~ 0 + length * style\n", + " Family: bernoulli\n", + " Link: p = logit\n", + " Observations: 58662\n", + " Priors: \n", + " target = p\n", + " Common-level effects\n", + " length ~ Normal(mu: 0.0, sigma: 0.0708)\n", + " style ~ Normal(mu: 0.0, sigma: 2.0)\n", + " length:style ~ Normal(mu: [0. 0. 0.], sigma: [0.0702 0.0509 0.0611])\n", + "------\n", + "* To see a plot of the priors call the .plot_priors() method.\n", + "* To see a summary or plot of the posterior pass the object returned by .fit() to az.summary() or az.plot_trace()" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } ], - "text/plain": [ - " term estimate_type value ... estimate lower_3.0% upper_97.0%\n", - "0 livebait diff (0.0, 1.0) ... 0.864408 0.627063 1.116105\n", - "1 livebait diff (0.0, 1.0) ... 1.694646 1.252803 2.081207\n", - "2 livebait diff (0.0, 1.0) ... 1.424598 1.078389 1.777154\n", - "3 livebait diff (0.0, 1.0) ... 2.344439 1.872191 2.800661\n", - "4 livebait diff (0.0, 1.0) ... 2.429459 1.871578 2.964242\n", - "5 livebait diff (0.0, 1.0) ... 4.443540 3.747840 5.170052\n", - "6 livebait diff (0.0, 1.0) ... 3.541921 2.686445 4.391176\n", - "7 livebait diff (0.0, 1.0) ... 10.739204 9.024702 12.432764\n", + "source": [ + "model" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, by default, the `plot_predictions` function plots the mean outcome on the response scale. Therefore, the plot below shows the probability of a successful response `certified_fresh` as a function of `length`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n", + "bmb.interpret.plot_predictions(model, idata, \"length\", ax=ax);" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Additionally, we can see how the probability of `certified_fresh` varies as a function of categorical covariates. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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XAqAIXHrg0bhx4xQVFeX4evTo0fr2228VFRWlxYsXq169eho7dqzLRQIAgOKLaVmzpEsAUAwuzaj/5z//0Z133ilJys7O1tKlSzV16lQtWbJEkyZNUlxcnFuKBAAAAEobl4J6RkaGbrvtNknS1q1b9dtvv+nhhx+WJLVq1UoHDx50uUAAAACgNHIpqNvtdu3du1eStHr1aoWEhCg4+I/1b2fPnpW3t7frFQIAAAClkEtr1Dt16qSXX35ZycnJmjt3rvr16+do2717t2rXru1qfQAAAECp5FJQ/9vf/qaDBw/qo48+UqtWrfTXv/7V0bZw4UK1adPG5QIBAACA0siloB4YGKhVq1YV2JaYmCg/Pz9Xdg8AAACUWm574NH58+d15MgRZWdnS5LKly8vHx8fd+0eAAAAKFVcDuqJiYmKiIhQuXLlFBISoh9//FGSNGzYMC1ZssTlAgEAAIDSyOUnk95///26cOGCXnjhBeXm5jraAgMDNXfuXFfrAwAAAEoll4L6uHHj1LlzZ23fvl2vvvqqU1uzZs20Y8cOV3YPAAAAlFouXUy6fft2LVq0SJJks9mc2qpUqaK0tDRXdg8AAACUWi7NqHt5eSkrK6vAtrS0NJUrV86V3QMAAACllktB/a677tKnn35aYNvixYsVERHhyu4BAACAUsulpS9jxozRAw88oG7duqlv376y2WzauHGjPvnkEy1evFiJiYnuqhMAAAAoVVwK6v/zP/+j2NhYjRgxQl9++aWkP27LeNttt2nu3Llq166dW4oEAAAASptiB/WcnBzt27dPXbp00aOPPqr169frxIkTCgwMVNu2bVW2bFl31gkAAACUKsUO6sYYNWrUSMuXL1d0dLQ6duzozroAAACAUq3YF5N6eXmpWrVqTg85AgAAAOAeLt31pVevXpo3b567agEAAADw/7l0MWnz5s31+eef67777lP37t1VvXr1fA8+6t69u0sFAgAAAKWRS0G9b9++kqQjR45o7dq1+dptNptycnJcOQQAAABQKrkU1LlPOgAAAHB9FDmoP//883ruuedUs2ZNhYaGqnr16vL29r4etQEAAAClVpEvJp0+fbqOHTsmSQoNDdX27dvdXhQAAABQ2hU5qFesWFEnTpyQ9Me91C+/eBQAAACA64q89OXuu+/WoEGD1KpVK0nSyJEjddtttxXY12az6csvv3SpQAAAAKA0KnJQnzVrlkaMGKHk5GTZbDalpKTI19e3wL7MtgMAAADFU+SgHhISoqVLl0qSPDw8tGzZMsfsOgAAAAD3cOnJpImJiWrUqFGh+hpjNGnSJB0/ftyVQwIAAAClgktBvX379goICChU39zcXE2cOFFHjx515ZAAAABAqeBSUC8qY8yNPBwAAABw07qhQR0AAABA4RDUAQAAAAsiqAMAAAAWRFAHAAAALIigDgAAAFgQQR0AAACwoBsW1D09PbV//341adLkmn0zMzM1YsQIBQUFyc/PT82bN9dnn31WqOOkpaWpf//+CgwMlL+/vyIiIpSQkFBg399++03jxo1T/fr15evrq8qVKysyMlJ79+4t0rkBAAAA7uZV1AGTJk0qdF+bzaaxY8c6vg4JCSnUuO7du2vz5s2aMmWK6tevr4ULF+qxxx5Tbm6uevfufcVxFy9eVMeOHXX69Gm98847stvtmjlzpjp16qTVq1erffv2jr6ZmZmKjIzU0aNHNWbMGDVt2lRnzpzR+vXrde7cuUKfIwAAAHA92EwRn0Lk4eE8CW+z2fI9yMhmszn+Oycnp0gFrVixQg8++KAjnOe5//77lZycrIMHD8rT07PAsbNmzdKwYcO0fv16RURESJKys7PVrFkzBQQEaOPGjY6+I0aM0Mcff6wff/xRt99+e5FqvJLk5GSFh4crKSlJjRs3dss+AQAAcGsoalYs8tKX3Nxcx2vPnj0KDQ3Va6+9pv379+v8+fPav3+/Jk+erNDQUO3evbvIJ7B06VIFBAQoJibGafuAAQN09OhRp7Bd0NgGDRo4QrokeXl56fHHH9emTZt05MgRSdK5c+f08ccfKyYmxm0hHQAAAHAnl9aoP/vss+rbt69eeuklhYSEyNfXVyEhIXr55ZfVp08fPfPMM0XeZ1JSksLCwuTl5bwqp2nTpo72q43N61fQ2OTkZEnS1q1b9dtvv6levXoaMmSIKlasKB8fH7Vs2VJff/11kWsGAAAA3M2loP7tt9+qbdu2Bba1bdtW//d//1fkfaanp6tSpUr5tudtS09Pd3ls3sz61KlTtXPnTs2bN09Lly5V+fLl9dBDD+mbb765Zp1paWlKTk52eqWkpFz7BAEAAIBCKPLFpJfy9fXVli1b1LFjx3xtW7ZskY+PT7H2e+ka96K0FXZsbm6uJMnHx0crV65UuXLlJEmRkZGqV6+eJk+erAceeOCqx5k1a5YmTpx41T4AAABAcbkU1Lt166aJEycqICBAvXv3VsWKFfXrr79qwYIFmjRpkv785z8XeZ+VK1cucNb81KlTklTgjHlRx1auXFmS1KZNG0dIlyR/f3+1b99ey5Ytu2adQ4cOzbeOPiUlRV27dr3mWAAAAOBaXArqb7/9tvbt26fhw4frmWeekZeXl7Kzs2WM0b333qu33367yPts0qSJ4uLilJ2d7bROfefOnZKk8PDwq47N63epy8cWtI49jzEm351tCmK322W326/ZDwAAACgOl9aolytXTmvWrNGKFSv0wgsvqG/fvho1apRWrlypxMREp9nqwurWrZsyMzP1xRdfOG2PjY1VUFCQWrdufdWxu3fvdrozTHZ2tubPn6/WrVsrKChIklS9enVFRETou+++U0ZGhqPvuXPntG7dOt19991FrhsAAABwJ5dm1PN06tRJnTp1cseuFB0draioKA0ZMkQZGRmqW7eu4uLitGrVKs2fP99xD/VBgwYpNjZW+/btczxIaeDAgZo5c6ZiYmI0ZcoU2e12zZo1S3v27NHq1audjvPmm28qMjJSDzzwgF588UXZbDa99dZb+uWXXzR58mS3nAsAAABQXC7NqOf55ptv9NJLL+mJJ57QwYMHJUmbN2/WyZMni7W/JUuWqE+fPho3bpw6deqkjRs3Ki4uzmnNe05OjnJycpwetuTr66uEhARFRkZq+PDheuihh3Ts2DGtXLnS6amk0h/r0xMSEuTr66s///nP6t27t7y9vbV27Vqn+7ADuPks2nJIf4//jxZtOVTSpQAAUGxFfjLppc6dO6dHHnlECQkJjjuqbN68WXfeead69uypmjVr6s0333RbsVbHk0kBa+j54QZt3H9KrUMr6fMn+cMbAGAN1/3JpJd65ZVXtGXLFn3xxRc6c+aM0+z2/fffn2+5CQAAAIDCcWmN+qJFizR58mR169ZNOTk5Tm21atVyLIMBAAAAUDQuzaifPHnyitP2Hh4eOn/+vCu7BwAAAEotl4J6jRo1CrxvuST9+OOPCg0NdWX3AAAAQKnlUlDv3r27XnvtNW3fvt2xzWaz6cCBA/r73/+e78mdAAAAAArHpaA+fvx4BQUFqVWrVmrZsqVsNpsGDBig8PBw2e12jRkzxl11AkChZOXkKuNCliQp40KWsnJyS7giAACKx+Unk65fv16TJ09WQECA6tSpI39/f7300kv697//rTJlyrirTgC4qqycXL2bsFcRrydo17GzkqRdx86qzetr9G7CXgI7AOCm4/KTScuUKaMxY8Ywew6gxGTl5GrwvC1K3HNStsvafsm8qLfj/6Mdh07rwz4t5O3plue8AQBw3fGJBeCm9/7afUrc88eTkC9/glve12t2p+mDtftuaF0AALiiyDPqAwcO1NixYxUaGqqBAwdeta/NZtPs2bOLXRwAXEtWTq7mbUiVTflD+qVskuZtOKCnOtRhVh0AcFMoclBPTEzUs88+K0las2aNbLbL/6H5v67WBgDusCX1V/2S+fs1+xlJJzMvakvqr4qoU/n6FwYAgIuKHNT379/v+O/U1FR31gIARXbm/LVDuiv9AQAoKUX+998777xTycnJkqR58+YpPT3d7UUBQGFVKONzXfsDAFBSihzUf/zxR2VmZkqSBgwYoH37uDgLQMlpWbuiAgN88t3t5XI2SVUCfNWydsUbURYAAC4rclC32+3atm2bJMkYwzp0ACXK29NDfSNqX/VCUumPNep9I0K4kBQAcNMo8ifWww8/rGHDhikgIEA2m02RkZEqX758ga8KFSpcj5oBwMmQDnV0X0O7JOWbWc/7+r6Gdj3Voc4NrQsAAFcU+WLSGTNmqFGjRtq5c6c++eQTdejQQVWqVLketQFAoXh7eujDPi30wdp9mrfhgE5mXnS0BQb4qm9ECLdlBADcdGzGmGv9i/EVeXh46Pvvv1erVq3cWdNNKzk5WeHh4UpKSlLjxo1LuhygVMrKydXD7/2fdh07q7Dq5fTV0+0I6AAASyhqVizyjPqlcnNzXRkOAG7n7emh8n7ekqTyft6EdADATYtPMAAAAMCCihzUPT09tWnTpj8Ge3jI09Pzii8vL5cm7AEAAIBSq8hJety4cQoODnb8N7dnBAAAANyvyEF9/Pjxjv+eMGGCO2sBAAAA8P+5tEZ90qRJOnr0aIFtx44d06RJk1zZPQAUS48WwXq2Yz31aBFc0qUAAFBsLgX1iRMn6vDhwwW2HT16VBMnTnRl9wBQLDEta+q5qPqKaVmzpEsBAKDYXArqV7sFe2Zmpry9vV3ZPQAAAFBqFXmN+o8//qgdO3Y4vl6xYoV2797t1Of8+fNasGCB6tThcd0AAABAcRQ5qC9dutSxpMVms11xHXqZMmU0Z84c16oDAAAASqkiB/XBgwerS5cuMsaoVatWmjNnjsLDw536+Pr6qk6dOipTpozbCgUAAABKkyIH9erVq6t69er6/ffftXLlStWoUSNfUAcAAADgmmJfTOrl5aWHHnpIJ06ccGc9AAAAAORCUPfw8FBwcLAyMjLcWQ8AAAAAuXh7xkGDBmnmzJnKyclxVz0AAAAAVIw16pfy8fHRnj17FBYWpocffljVq1eXzWZztNtsNj333HMuFwkAAACUNi4F9RdffNHx32+//Xa+doI6AAAAUDwuBfX9+/e7qw4AAAAAl3ApqIeEhLirDgAAAACXcCmo59m9e7fWrVunX375RYMGDVK1atV09OhRVaxYkYceAQAAAMXgUlDPycnR4MGDNXfuXBljZLPZFB0drWrVqunJJ5/UHXfcoUmTJrmrVgAAAKDUcOn2jK+99poWLlyoN954Q0lJSTLGONqio6O1atUqlwsEAAAASiOXZtTnzp2rsWPH6vnnn893L/XQ0FAuNgUAAACKyaUZ9SNHjigiIqLANj8/P509e9aV3QMAAACllktB3W636+effy6wbc+ePQoODnZl9wAAAECp5VJQ79y5s1577TUdOXLEsc1ms+nMmTN699139dBDD7lcIAAAAFAauRTUJ02apOzsbDVq1EiPPvqobDabXn75ZYWHh+vChQsaO3asu+oEAAAAShWXgnrVqlW1efNmPfbYY9q6das8PT31ww8/KDo6WuvXr1elSpXcVScAAABQqrh015esrCwFBATogw8+yNf222+/KSsrS97e3q4cAgAAACiVXJpRf+KJJ/SXv/ylwLbBgwdryJAhruweAAAAKLVcCuqJiYl6+OGHC2x76KGHlJCQ4MruAQAAgFLLpaB+4sQJVa9evcC2atWq6fjx467sHgAAACi1XArqt912m1JSUgpsS0lJUbly5VzZPQAAAFBquRTUIyMj9frrr+vUqVNO20+dOqUpU6bovvvuc6k4AAAAoLRy6a4vEyZM0F133aV69eqpZ8+eqlGjhg4fPqxFixYpKytLEydOdFedAAAAQKniUlBv0KCBvv32Wz3//PP66KOPlJOTI09PT7Vv315vv/22GjRo4K46AQAAgFLFpaUvktSsWTMlJCQoIyNDhw8f1tmzZ7V69Wo1bdq02PvMzMzUiBEjFBQUJD8/PzVv3lyfffZZocampaWpf//+CgwMlL+/vyIiIq5595nz58+rfv36stlsevPNN4tdNwAAAOAuLs2oX6pMmTIqU6aMW/bVvXt3bd68WVOmTFH9+vW1cOFCPfbYY8rNzVXv3r2vOO7ixYvq2LGjTp8+rXfeeUd2u10zZ85Up06dtHr1arVv377AcWPHjtVvv/3mltoBAAAAd3BbUHeXFStWKD4+3hHOpT8uWj1w4IBGjRqlnj17ytPTs8Cxs2fPVlJSktavX6+IiAjH2GbNmmn06NHauHFjvjGbNm3SjBkztGDBAsXExFy/EwMAAACKwOWlL+62dOlSBQQE5AvNAwYM0NGjRwsM25eObdCggSOkS5KXl5cef/xxbdq0SUeOHHHq//vvv2vgwIEaNmyYWrZs6d4TAQAAAFxguaCelJSksLAweXk5T/bnrXlPSkq66tiC1sbnbUtOTnbaPmnSJP3222+aPHmyq2UDAAAAbmW5pS/p6em6/fbb822vVKmSo/1qY/P6XWvsjh07NG3aNC1fvlxly5bVyZMni1RnWlpavjFXevgTAAAAUFSWC+qSZLPZitVW2LHZ2dkaOHCgevbsqQceeKBYNc6aNYv7xAMAAOC6sVxQr1y5coGz5nlPPy1oxryoY6dPn66ff/5Z//u//6vTp09LkjIyMiRJFy5c0OnTp1WuXLkrXrQqSUOHDs23jj4lJUVdu3a98skBAAAAhWS5oN6kSRPFxcUpOzvbaZ36zp07JUnh4eFXHZvX71KXj01KStKZM2dUr169fH3Hjh2rsWPHavv27WrevPkVj2W322W32wt1TgAAAEBRWe5i0m7duikzM1NffPGF0/bY2FgFBQWpdevWVx27e/dupzvDZGdna/78+WrdurWCgoIkSWPGjFFiYqLTKy4uTpL01FNPKTExUXXr1r0OZwcAAAAUjuVm1KOjoxUVFaUhQ4YoIyNDdevWVVxcnFatWqX58+c7lqMMGjRIsbGx2rdvn0JCQiRJAwcO1MyZMxUTE6MpU6bIbrdr1qxZ2rNnj1avXu04RsOGDdWwYUOn46ampkqS6tSpow4dOtyQcwUAAACuxHJBXZKWLFmiV155RePGjdOpU6fUsGFDxcXFqVevXo4+OTk5ysnJkTHGsc3X11cJCQkaPXq0hg8frnPnzql58+ZauXLlFZ9KCgAAAFiRzVyadOGS5ORkhYeHKykpSY0bNy7pcgAAAGAhRc2KllujDgAAAICgDgAAAFiSJdeoo/AWbTmkw7+eV3DFMoppWbOkywEAAICbMKN+k1u89bDeSdirxVsPl3QpAAAAcCOCOgAAAGBBBHUAAADAggjqAAAAgAUR1AEAAAALIqgDAAAAFkRQBwAAACyIoA4AAABYEEEdAAAAsCCCOgAAAGBBBHUAAADAggjqAAAAgAUR1AEAAAALIqgDAAAAFkRQBwAAACyIoA4AAABYEEEdAAAAsCCCOgAAAGBBBPWbWFZOrjIuZEmSMi5kKSsnt4QrAgAAgLsQ1G9CWTm5ejdhryJeT9CuY2clSbuOnVWb19fo3YS9BHYAAIBbgFdJF4CiycrJ1eB5W5S456Rsl7X9knlRb8f/RzsOndaHfVrI25O/wwAAAG5WJLmbzPtr9ylxz0lJkrmsLe/rNbvT9MHafTe0LgAAALgXQf0mkpWTq3kbUvPNpF/OJmnehgMsgQEAALiJEdRvIltSf9Uvmb/nm0m/nJF0MvOitqT+eiPKAgAAwHVAUL+JnDn/+3XtDwAAAOsgqN9EKpTxua79AQAAYB0E9ZtIy9oVFRjgU6g16lUCfNWydsUbURYAAACuA4L6TcTb00N9I2oXao1634gQbs8IAABwEyPJ3WSGdKij+xraJSnfzHre1/c1tOupDnVuaF0AAABwL4L6Tcbb00Mf9mmhkVH1FRjg69QWGOCrkVH1edgRAADALYAnk96EvD09NLxjPT3VoY4efu//tOvYWYVVL6evnm5HQAcAALhFkOpuYt6eHirv5y1JKu/nTUgHAAC4hZDsAAAAAAsiqAMAAAAWRFAHAAAALIigDgAAAFgQQR0AAACwIII6AAAAYEEEdQAAAMCCCOoAAACABRHUAQAAAAsiqAMAAAAWRFAHAAAALIigDgAAAFgQQR0AAACwIII6AAAAYEEEdQAAAMCCCOoAAACABRHUAQAAAAsiqAMAAAAWZMmgnpmZqREjRigoKEh+fn5q3ry5Pvvss0KNTUtLU//+/RUYGCh/f39FREQoISHBqU9GRoZee+01dejQQdWqVVNAQICaNGmiqVOn6sKFC9fjlK6bHi2C9WzHeurRIrikSwEAAIAbeZV0AQXp3r27Nm/erClTpqh+/fpauHChHnvsMeXm5qp3795XHHfx4kV17NhRp0+f1jvvvCO73a6ZM2eqU6dOWr16tdq3by9JOnjwoKZPn64+ffro+eefV0BAgL799ltNmDBB8fHxio+Pl81mu1Gn65KYljVLugQAAABcB5YL6itWrFB8fLwjnEtSZGSkDhw4oFGjRqlnz57y9PQscOzs2bOVlJSk9evXKyIiwjG2WbNmGj16tDZu3ChJCg0NVWpqqsqWLesYe99996ls2bIaNWqUvvvuO7Vr1+46nykAAABwZZZb+rJ06VIFBAQoJibGafuAAQN09OhRR9i+0tgGDRo4QrokeXl56fHHH9emTZt05MgRSVLZsmWdQnqeVq1aSZIOHTrkjlMBAAAAis1yQT0pKUlhYWHy8nKe7G/atKmj/Wpj8/oVNDY5Ofmqx16zZo0kqXHjxkWqGQAAAHA3yy19SU9P1+23355ve6VKlRztVxub16+oY3/88UdNmzZN3bp1KzDsXy4tLU0nT5502paSknLNcQAAAEBhWC6oS7rqhZzXusizOGNTU1PVpUsX1axZUx9//HGhapw1a5YmTpxYqL4AAABAUVkuqFeuXLnAme9Tp05JUoEz5q6MPXDggCIjI+Xl5aWEhISr7v9SQ4cOzbeOPiUlRV27di3UeAAAAOBqLBfUmzRpori4OGVnZzutU9+5c6ckKTw8/Kpj8/pd6kpjDxw4oA4dOsgYo7Vr1yo4uPD3Irfb7bLb7YXuDwAAABSF5S4m7datmzIzM/XFF184bY+NjVVQUJBat2591bG7d+92ujNMdna25s+fr9atWysoKMix/eDBg+rQoYNycnK0Zs0ahYSEuP9kAAAAgGKy3Ix6dHS0oqKiNGTIEGVkZKhu3bqKi4vTqlWrNH/+fMc91AcNGqTY2Fjt27fPEbIHDhyomTNnKiYmRlOmTJHdbtesWbO0Z88erV692nGMtLQ0RUZG6tixY5o9e7bS0tKUlpbmaA8ODi7S7DoAAADgbpYL6pK0ZMkSvfLKKxo3bpxOnTqlhg0bKi4uTr169XL0ycnJUU5Ojowxjm2+vr5KSEjQ6NGjNXz4cJ07d07NmzfXypUrHU8llaSffvpJP//8syTp8ccfz3f88ePHa8KECdfvBAEAAIBrsJlLky5csm3bNrVo0ULLli1T3bp1S7ocAAAAWEjejUe2bt2qO++885r9LTmjfrPKe6Ipd34BAADAlRw6dKhQQZ0ZdTc6ffq01q1bp5o1a8rX1/eGHDPvLzNm8YH/4ucCcMbPBOCspH4mLl68qEOHDql9+/a67bbbrtmfGXU3uu222/TII4+UyLHr1q2rxo0bl8ixAavi5wJwxs8E4KwkfiYKM5Oex3K3ZwQAAABAUAcAAAAsiaAOAAAAWBBB/SZXpUoVjR8/XlWqVCnpUgDL4OcCcMbPBODsZvmZ4K4vAAAAgAUxow4AAABYEEEdAAAAsCCCOgAAAGBBBPUb6N1335XNZlN4eHixxh89elQTJkzQjh078rVNmDBBNpvNxQoB9/rxxx81YMAAhYaGys/PTwEBAbrzzjs1bdo0nTp1qqTLK5K1a9fKZrNp7dq1JV0KbiFz586VzWZzvPz8/FStWjVFRkbq9ddfV1paWkmXCFjG999/r5iYGFWvXl0+Pj6qVq2aevTooQ0bNjj1W79+vSZMmKDTp0/n20ft2rXVpUuXG1Sx6wjqN9Ann3wiSUpOTtbGjRuLPP7o0aOaOHFigUH9L3/5S743KlCSPvroI7Vo0UKbN2/WqFGjtGrVKi1dulQxMTH64IMPNGjQoJIuEbCMOXPmaMOGDYqPj9fMmTPVvHlzTZ06VWFhYVq9enVJlweUuBkzZqht27Y6fPiwpk2bptWrV+vNN9/UkSNH1K5dO7333nuOvuvXr9fEiRMLDOo3G6+SLqC02LJli3744Qc9+OCD+vrrrzV79my1bt3abfsPDg5WcHCw2/YHuGLDhg0aMmSIoqKitGzZMvn6+jraoqKiNHLkSK1ataoEKwSsJTw8XC1btnR8/eijj+q5555Tu3bt1L17d+3du1dVq1YtcOy5c+fk7+9/o0oFbrjvvvtOI0aMUOfOnbV06VJ5ef03vvbq1UvdunXTs88+qzvuuENt27YtwUr/kJOTo+zsbKfPvuJiRv0GmT17tiRpypQpatOmjT777DOdO3fOqc+RI0c0ePBg1axZUz4+PgoKClKPHj104sQJrV27VnfddZckacCAAY5/Jp0wYYKkgpe+5Obmatq0aWrYsKF8fX1lt9vVt29fHT582Klfhw4dFB4ers2bN+uee+6Rv7+/br/9dk2ZMkW5ubnX6TuCW9nf/vY32Ww2/eMf/yjwF5WPj48efvhhSUV/n27YsEFt2rRRmTJlVLt2bc2ZM0eS9PXXX+vOO++Uv7+/mjRpUuAfAnv37lXv3r1lt9vl6+ursLAwzZw5M1+/3bt3q1OnTvL391dgYKCeeuopnT171qnP5MmT5eXlpUOHDuUbP3DgQFWuXFkXLlwo/DcNuEytWrX01ltv6ezZs/rwww8lSf3791dAQIB27typ+++/X+XKlVPHjh0lSfHx8XrkkUcUHBwsPz8/1a1bV08++aR++eUXp/3mfV78+OOPiomJUYUKFVSpUiU9//zzys7O1p49e9SpUyeVK1dOtWvX1rRp05zGX7hwQSNHjlTz5s0dYyMiIvTll1/emG8MSp3XX39dNptN77//vlNIlyQvLy/NmjVLNptNU6ZM0YQJEzRq1ChJUmhoqCMvXb5scdWqVbrzzjtVpkwZNWzY0LHq4VLHjx/Xk08+qeDgYPn4+Cg0NFQTJ05Udna2o09qaqpsNpumTZumV199VaGhofL19VViYqJ7Tt7gujt37pypUKGCueuuu4wxxnz88cdGkpk7d66jz+HDh0316tVNYGCgefvtt83q1avN559/bgYOHGh27dplzpw5Y+bMmWMkmb/+9a9mw4YNZsOGDebQoUPGGGPGjx9vLv+/c/DgwUaSefrpp82qVavMBx98YKpUqWJq1qxpTp486ejXvn17U7lyZVOvXj3zwQcfmPj4eDN06FAjycTGxt6A7xBuJdnZ2cbf39+0bt26UP2L+j5t0KCBmT17tvnmm29Mly5djCQzceJE06RJExMXF2dWrFhh7r77buPr62uOHDniGJ+cnGwqVKhgmjRpYubNm2f+9a9/mZEjRxoPDw8zYcIER7/jx48bu91uatSoYebMmWNWrFhh/vznP5tatWoZSSYxMdEYY8yJEyeMr6+veeWVV5zOJz093ZQpU8aMGjXKhe8iSou83+ubN28usD0zM9N4enqajh07GmOM6devn/H29ja1a9c2r7/+uklISDDffPONMcaY999/37z++uvmq6++MuvWrTOxsbGmWbNmpkGDBub333937DPv86JBgwZm8uTJJj4+3owePdrxc9iwYUPz7rvvmvj4eDNgwAAjyXzxxReO8adPnzb9+/c3n376qVmzZo1ZtWqVeeGFF4yHhwefGXC7wn6mtGrVyvj7+5vU1FQzfPhwI8ksWbLEkZfOnDljjDEmJCTEBAcHm0aNGpl58+aZb775xsTExBhJZt26dY79HTt2zNSsWdOEhISYDz/80KxevdpMnjzZ+Pr6mv79+zv67d+/30gyNWrUMJGRkWbx4sXmX//6l9m/f79bzp+gfgPMmzfPSDIffPCBMcaYs2fPmoCAAHPPPfc4+gwcONB4e3ubn3766Yr72bx5s5Fk5syZk6/t8qC+a9cuI8kMHTrUqd/GjRuNJPPyyy87trVv395IMhs3bnTq26hRI/PAAw8U6VyB48ePG0mmV69e1+xbnPfpli1bHNvS09ONp6enKVOmjFMo37Fjh5Fk3n33Xce2Bx54wAQHBzt+Wed5+umnjZ+fnzl16pQxxpgXX3zR2Gw2s2PHDqd+UVFRTkHdmD9Ck91uNxcvXnRsmzp1qvHw8HDbL2nc2q4V1I0xpmrVqiYsLMwY88d7TpL55JNPrrrf3Nxck5WVZQ4cOGAkmS+//NLRlvd58dZbbzmNad68uSPc5MnKyjJVqlQx3bt3v+KxsrOzTVZWlhk0aJC54447rloXUFSF/Uzp2bOnkWROnDhh3njjDSOpwN/DISEhxs/Pzxw4cMCx7fz586ZSpUrmySefdGx78sknTUBAgFM/Y4x58803jSSTnJxsjPlvUK9Tp47TH8TuwtKXG2D27NkqU6aMevXqJUkKCAhQTEyMvv32W+3du1eStHLlSkVGRiosLMwtx8z7J5f+/fs7bW/VqpXCwsKUkJDgtL1atWpq1aqV07amTZvqwIEDbqkHKEhR36fVq1dXixYtHF9XqlRJdrtdzZs3V1BQkGN73s9R3vv3woULSkhIULdu3eTv76/s7GzHq3Pnzrpw4YK+//57R02NGzdWs2bNnI7du3fvfPU/++yzSktL06JFiyT9sYzn/fff14MPPqjatWsX4zsC5GcKeID4o48+mm9bWlqannrqKdWsWVNeXl7y9vZWSEiIJGnXrl35+l9+54uwsDDZbDZFR0c7tnl5ealu3br5PgsWLVqktm3bKiAgwHGs2bNnF3gc4EbI+zkpzB3wmjdvrlq1ajm+9vPzU/369Z3e5//85z8VGRmpoKAgp8+MvJ+PdevWOe3z4Ycflre3tztOxQlB/TpLSUnRv//9bz344IMyxuj06dM6ffq0evToIem/d4I5efKkWy8GTU9Pl/RHsLlcUFCQoz1P5cqV8/Xz9fXV+fPn3VYTSofAwED5+/tr//791+xb1PdppUqV8vXz8fHJt93Hx0eSHGvE09PTlZ2drRkzZsjb29vp1blzZ0lyrONNT09XtWrV8h2noG133HGH7rnnHsc693/+859KTU3V008/ffUTBwrpt99+U3p6utMfov7+/ipfvrxTv9zcXN1///1asmSJRo8erYSEBG3atMnxB2hBv8sL+rnx9/eXn59fvu2XXm+xZMkS/elPf1KNGjU0f/58bdiwQZs3b9bAgQO5LgNuV9jPlNTUVPn7+xf4OXG5wmSeEydOaPny5fk+Mxo3bixJ+a79KOhzzB2468t19sknn8gYo8WLF2vx4sX52mNjY/Xqq6+qSpUq+S6ec0Xem/DYsWP5/gA4evSoAgMD3XYs4FKenp7q2LGjVq5cqcOHD1/1D9Ab9T6tWLGiPD091adPHw0bNqzAPqGhoY6ajh8/nq+9oG2S9MwzzygmJkbbtm3Te++9p/r16ysqKsotdQNff/21cnJy1KFDB8e2gmYMk5KS9MMPP2ju3Lnq16+fY3tKSorba5o/f75CQ0P1+eefO9Vy8eJFtx8L8PT0VGRkpFatWnXFz5TDhw9r69atio6Olqenp1uOGxgYqKZNm+q1114rsP3SP56lws3kFwcz6tdRTk6OYmNjVadOHSUmJuZ7jRw5UseOHdPKlSsVHR2txMRE7dmz54r7y7t7RmFmue+77z5Jf/xCvdTmzZu1a9cux10CgOvhpZdekjFGTzzxhH7//fd87VlZWVq+fPkNe5/6+/srMjJS27dvV9OmTdWyZct8r7w/GiIjI5WcnKwffvjBaR8LFy4scN/dunVTrVq1NHLkSK1evVpDhw7l4WNwi4MHD+qFF15QhQoV9OSTT161b9577vK7LOXdLcadbDabfHx8nN7nx48f564vuG7yPlOGDh2qnJwcp7acnBwNGTJExhi99NJLkoqWl66kS5cuSkpKUp06dQr8zLg8qF8vzKhfRytXrtTRo0c1depUp9mQPOHh4Xrvvfc0e/Zsvffee1q5cqXuvfdevfzyy2rSpIlOnz6tVatW6fnnn1fDhg1Vp04dlSlTRgsWLFBYWJgCAgIUFBRU4JulQYMGGjx4sGbMmCEPDw9FR0crNTVVY8eOVc2aNfXcc8/dgO8ASquIiAi9//77Gjp0qFq0aKEhQ4aocePGysrK0vbt2/WPf/xD4eHhWrp06Q17n77zzjtq166d7rnnHg0ZMkS1a9fW2bNnlZKSouXLl2vNmjWSpBEjRuiTTz7Rgw8+qFdffVVVq1bVggULtHv37gL36+npqWHDhunFF19U2bJl8623BwojKSnJsQY2LS1N3377rebMmSNPT08tXbpUVapUuer4vM+IMWPGyBijSpUqafny5YqPj3d7rV26dNGSJUs0dOhQ9ejRQ4cOHdLkyZNVvXp1x3VXgDu1bdtW06dP14gRI9SuXTs9/fTTqlWrlg4ePKiZM2dq48aNmj59utq0aSNJatKkiaQ/fu/369dP3t7eatCggcqVK1foY06aNEnx8fFq06aNnnnmGTVo0EAXLlxQamqqVqxYoQ8++ODGPL/G7ZenwqFr167Gx8fHpKWlXbFPr169jJeXlzl+/Lg5dOiQGThwoKlWrZrx9vY2QUFB5k9/+pM5ceKEo39cXJxp2LCh8fb2NpLM+PHjjTEF354xJyfHTJ061dSvX994e3ubwMBA8/jjjztu6Zinffv2pnHjxvlq69evnwkJCSn+NwCl3o4dO0y/fv1MrVq1jI+Pjylbtqy54447zLhx4xw/F66+T0NCQsyDDz6Yb7skM2zYMKdt+/fvNwMHDjQ1atQw3t7epkqVKqZNmzbm1Vdfder3008/maioKOPn52cqVapkBg0aZL788st8d33Jk5qaaiSZp556qqjfIpRyeXd9yXv5+PgYu91u2rdvb/72t7/l+/zo16+fKVu2bIH7ynvflitXzlSsWNHExMSYgwcPOn1WGPPfz4tLb396tX0X9LM3ZcoUU7t2bePr62vCwsLMRx99VODnEOBOGzZsMD169DBVq1Y1Xl5exm63m+7du5v169fn6/vSSy+ZoKAg4+Hh4fS7+0qfGe3btzft27d32nby5EnzzDPPmNDQUOPt7W0qVapkWrRoYV555RWTmZlpjPnvXV/eeOMNt5+vMcbYjCngcnIAQKHNmDFDzzzzjJKSkhwXGgEA4CqCOgAU0/bt27V//349+eSTatu2rZYtW1bSJQEAbiEEdQAoptq1a+v48eO655579OmnnxZ4C0cAAIqLoA4AAABYELdnBAAAACyIoA4AAABYEEEdAAAAsCCCOgAAAGBBBHUAAADAggjqAAAAgAUR1AEARbZw4UJNnz692ONTU1Nls9k0d+5ct9UEALcagjoAoMhcDeoAgGsjqAMAAAAWRFAHAORz8uRJDR48WDVr1pSvr6+qVKmitm3bavXq1erQoYO+/vprHThwQDabzfEyxqhevXp64IEH8u0vMzNTFSpU0LBhw6563L1796p3796y2+3y9fVVWFiYZs6ceb1OEwAszaukCwAAWE+fPn20bds2vfbaa6pfv75Onz6tbdu2KT09XbNmzdLgwYO1b98+LV261DHGZrNp+PDhGjFihPbu3at69eo52ubNm6eMjIyrBvWffvpJbdq0Ua1atfTWW2+pWrVq+uabb/TMM8/ol19+0fjx46/rOQOA1diMMaakiwAAWEu5cuX0l7/8RX//+98LbO/SpYuSkpKUmprqtP3s2bOqUaOGBg4c6LSGvXHjxqpatarWrFkj6Y+LSUNDQzVnzhz1799fktSpUyclJycrOTlZ5cuXd4wdPny4Pv74Yx09elQVK1Z063kCgJWx9AUAkE+rVq00d+5cvfrqq/r++++VlZVVqHHlypXTgAEDNHfuXP3222+SpDVr1uinn37S008/fcVxFy5cUEJCgrp16yZ/f39lZ2c7Xp07d9aFCxf0/fffu+XcAOBmQVAHAOTz+eefq1+/fvr4448VERGhSpUqqW/fvjp+/Pg1xw4fPlxnz57VggULJEnvvfeegoOD9cgjj1xxTHp6urKzszVjxgx5e3s7vTp37ixJ+uWXX9xzcgBwk2CNOgAgn8DAQE2fPl3Tp0/XwYMH9dVXX2nMmDFKS0vTqlWrrjq2bt26io6O1syZMxUdHa2vvvpKEydOlKen5xXHVKxYUZ6enurTp88V17GHhoa6dE4AcLMhqAMArqpWrVp6+umnlZCQoO+++06S5Ovrq/Pnz19xzLPPPqv7779f/fr1k6enp5544omrHsPf31+RkZHavn27mjZtKh8fH7eeAwDcjAjqAAAnZ86cUWRkpHr37q2GDRuqXLly2rx5s1atWqXu3btLkpo0aaIlS5bo/fffV4sWLeTh4aGWLVs69hEVFaVGjRopMTFRjz/+uOx2+zWP+84776hdu3a65557NGTIENWuXVtnz55VSkqKli9f7rgQFQBKC4I6AMCJn5+fWrdurU8//VSpqanKyspSrVq19OKLL2r06NGS/pgxT05O1ssvv6wzZ87IGKPLbyL2pz/9SRMmTLjqRaSXatSokbZt26bJkyfrr3/9q9LS0nTbbbepXr16jnXqAFCacHtGAMB10bJlS9lsNm3evLmkSwGAmxIz6gAAt8nIyFBSUpL++c9/auvWrU4PRAIAFA1BHQDgNtu2bVNkZKQqV66s8ePHq2vXriVdEgDctFj6AgAAAFgQDzwCAAAALIigDgAAAFgQQR0AAACwIII6AAAAYEEEdQAAAMCCCOoAAACABRHUAQAAAAsiqAMAAAAWRFAHAAAALIigDgAAAFgQQR0AAACwoP8HGs9hr87jtxEAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n", + "bmb.interpret.plot_predictions(model, idata, \"style\", ax=ax);" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting other model parameters\n", "\n", - "[8 rows x 8 columns]" + "`plot_predictions` also has the argument `target` where `target` determines what parameter of the response distribution is plotted as a function of the explanatory variables. This argument is useful in distributional models, i.e., when the response distribution contains a parameter for location, scale and or shape. The default of this argument is `mean` and passing a parameter into `target` only works when the argument `pps=False` because when `pps=True` the posterior predictive distribution is plotted and thus, can only refer to the outcome variable (instead of any of the parameters of the response distribution). For this example, we will simulate our own dataset." ] }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# average by number of persons and camper by passing a list\n", - "comparisons(\n", - " model=fish_model,\n", - " idata=fish_idata,\n", - " contrast=\"livebait\",\n", - " conditional=None,\n", - " average_by=[\"persons\", \"camper\"]\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is still possible to use `plot_comparisons` when passing an argument to `average_by`. In the plot below, the empirical distribution is used to compute unit level contrasts for `livebait` and then averaged over `persons` to obtain the average predictive comparisons. The plot below is similar to the second plot in this notebook. The differences being that: (1) a pairwise transition grid is defined for the second plot above, whereas the empirical distribution is used in the plot below, and (2) in the plot below, we marginalized over the other covariates in the model (thus the reason for not having a `camper` or `child` group and panel, and a reduction in the uncertainty interval)." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [Intercept, x, alpha_Intercept, alpha_x]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " 100.00% [8000/8000 00:02<00:00 Sampling 4 chains, 25 divergences]\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n", + "There were 25 divergences after tuning. Increase `target_accept` or reparameterize.\n" + ] + } + ], + "source": [ + "rng = np.random.default_rng(121195)\n", + "N = 200\n", + "a, b = 0.5, 1.1\n", + "x = rng.uniform(-1.5, 1.5, N)\n", + "shape = np.exp(0.3 + x * 0.5 + rng.normal(scale=0.1, size=N))\n", + "y = rng.gamma(shape, np.exp(a + b * x) / shape, N)\n", + "data_gamma = pd.DataFrame({\"x\": x, \"y\": y})\n", + "\n", + "formula = bmb.Formula(\"y ~ x\", \"alpha ~ x\")\n", + "model = bmb.Model(formula, data_gamma, family=\"gamma\")\n", + "idata = model.fit(random_seed=1234)" ] }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plot_comparisons(\n", - " model=fish_model,\n", - " idata=fish_idata,\n", - " contrast=\"livebait\",\n", - " conditional=None,\n", - " average_by=\"persons\"\n", - ")\n", - "fig.set_size_inches(7, 3)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Logistic Regression\n", - "\n", - "To showcase an additional functionality of `comparisons` and `plot_comparisons`, we fit a logistic regression model to the [titanic dataset](https://vincentarelbundock.github.io/Rdatasets/csv/Stat2Data/Titanic.csv) with interaction terms to model the probability of survival. The titanic dataset gives the values of four categorical attributes for each of the 2201 people on board the Titanic when it struck an iceberg and sank. The attributes are social class (first class, second class, third class, crewmember), age, sex (0 = female, 1 = male), and whether or not the person survived (0 = deceased, 1 = survived). " - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "dat = pd.read_csv(\"https://vincentarelbundock.github.io/Rdatasets/csv/Stat2Data/Titanic.csv\", index_col=0)\n", - "\n", - "dat[\"PClass\"] = dat[\"PClass\"].str.replace(\"[st, nd, rd]\", \"\", regex=True)\n", - "dat[\"PClass\"] = dat[\"PClass\"].str.replace(\"*\", \"0\").astype(int)\n", - "dat[\"PClass\"] = dat[\"PClass\"].replace(0, np.nan)\n", - "dat[\"PClass\"] = pd.Categorical(dat[\"PClass\"], ordered=True)\n", - "dat[\"SexCode\"] = pd.Categorical(dat[\"SexCode\"], ordered=True)\n", - "\n", - "dat = dat.dropna(axis=0, how=\"any\")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Modeling the probability that Survived==1\n", - "Auto-assigning NUTS sampler...\n", - "Initializing NUTS using jitter+adapt_diag...\n", - "Multiprocess sampling (4 chains in 4 jobs)\n", - "NUTS: [Intercept, PClass, SexCode, PClass:SexCode, Age, PClass:Age, SexCode:Age, PClass:SexCode:Age]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " |████████████████████████████████| 100.00% [8000/8000 00:15<00:00 Sampling 4 chains, 0 divergences]\r" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 16 seconds.\n" - ] - } - ], - "source": [ - "titanic_model = bmb.Model(\n", - " \"Survived ~ PClass * SexCode * Age\", \n", - " data=dat, \n", - " family=\"bernoulli\"\n", - ")\n", - "titanic_idata = titanic_model.fit(draws=1000, target_accept=0.95, random_seed=1234)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Comparison types\n", - "\n", - "`comparisons` and `plot_comparisons` also allow you to specify the type of comparison to be computed. By default, a difference is used. However, it is also possible to take the ratio where comparisons would then become _average predictive ratios_. To achieve this, pass `\"ratio\"` into the argument `comparison_type`. Using different comparison types offers a way to produce alternative insights; especially when there are interaction terms as the value of one covariate depends on the value of the other covariate." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " Formula: y ~ x\n", + " alpha ~ x\n", + " Family: gamma\n", + " Link: mu = inverse\n", + " alpha = log\n", + " Observations: 200\n", + " Priors: \n", + " target = mu\n", + " Common-level effects\n", + " Intercept ~ Normal(mu: 0.0, sigma: 2.5037)\n", + " x ~ Normal(mu: 0.0, sigma: 2.8025)\n", + " target = alpha\n", + " Common-level effects\n", + " alpha_Intercept ~ Normal(mu: 0.0, sigma: 1.0)\n", + " alpha_x ~ Normal(mu: 0.0, sigma: 1.0)\n", + "------\n", + "* To see a plot of the priors call the .plot_priors() method.\n", + "* To see a summary or plot of the posterior pass the object returned by .fit() to az.summary() or az.plot_trace()" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model we defined uses a `gamma` distribution parameterized by `alpha` and `mu` where `alpha` utilizes a log link and `mu` goes through an inverse link. Therefore, we can plot either: (1) the `mu` of the response distribution (which is the default), or (2) `alpha` of the response distribution as a function of the explanatory variable $x$. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Xq76+fsDjvPHGG/rUpz6liRMn6sUXX9Tjjz+u3bt3a+XKlfJ6vUP4igAAAJCsM62J1aM77VZVFOUM55RGhH20J3C+iooKNTc3y2KxqKGhQU8++WSf4x5++GE5nU79+te/Vn5+qFH9JZdcotmzZ+uxxx7TD37wg36P841vfENz5szRjh07ZLeH/gwzZszQlVdeqaefflr33Xff0L4wAAAAJCze1osfhVsvznblyma1DPe0hl3KraRbLBZZLP3/YQOBgH7961/r05/+dDSgS6GAf+2112rnzp397l9TU6MDBw7o85//fDSgS9IVV1yhOXPmDLg/AAAAhl+7x682d2DAcaeauuT2G5KkuWXpX48upWBIj8fRo0fldru1aNGiXo8tWrRI1dXV8ngufILBoUOHomP72j/yOAAAAEbPmZaBTxiVerZenD0GThqVUrDcJR6NjY2SpKKiol6PFRUVyTRNNTc3a9KkSUntH3m8L3V1db1q3qurq+OeOwAAAOITbz16pPVieWGm8jMzhnNKIyYtQ3pEf2UxA5XM9Demv303b96sjRs3Djw5AAAAJC1gBFUXR+vFNrdfZ1pD49L9KqOx0jKkFxcXS1KfK95NTU2yWCwqLCxMev++Vtgj7r//fq1bt67Hturq6n470QAAACAx59q9MoIDj/swptSFkD7KZs2apaysLB08eLDXYwcPHlRlZaUyMzMvuH9VVVV07E033dRr/8jjfXG5XHK5XEnOHAAAAPGI9yqjkXr0rAybphRlD+eURlRanjhqt9t166236oUXXlB7e/d/PX388cf67W9/q9tuu63f/SdPnqzLL79c27Ztk2EY0e379u3TkSNHBtwfAAAAwyuekG4ETVXXhVovzpmYK2sc5c7pIiVD+q5du7Rjxw699NJLkqT3339fO3bs0I4dO9TV1SVJ2rhxo7q6unTLLbdo165d2rlzp26++WaVlJToH/7hH3o8n91u18qVK3ts+8EPfqDDhw9r3bp12r17t5599ll95jOfUVVVle6+++6ReaEAAADopbXLr06vMeC4k42d8gZCNTFjpfViREqWu9x33306efJk9P727du1fft2SdLx48c1ffp0zZs3T6+//rq++c1v6q//+q9lt9t13XXX6bHHHlNpaWmP5zMMo8eKuSStWLFCL7/8sh5++GHdeuutys7O1i233KIf/ehHcjqdw/8iAQAA0KeaOEtd3vm4WZJkkTTbRUgfdidOnIhr3CWXXKLdu3cPOM40zT63r1q1SqtWrUpkagAAABhm8ZS6tLn9+vOpVknS/PJ85ThTMtYmLSXLXQAAADA++QJBNXR4Bxz3h2ONMsILsVdXlgz3tEYcIR0AAAApo6bFrWDfRRBRvkBQbx1vkiRNK8rWtOKcEZjZyCKkAwAAIGV8cLZtwDF/PNkktz90vuFVY3AVXSKkAwAAIEXUtLjV0uXvd0zQNLX3aOiClEU5Ds0vzx+JqY04QjoAAABSwvtnBl5Ff/9Mm5o6fZKkK2cVj6ne6LEI6QAAABh1de0e1bcPfMLo76sbJIWuMHpJRdFwT2vUENIBAAAw6uJZRf+4sVMfN4UubHn5jCI57GM3yo7dVwYAAIC00NLl05kWz4Dj/ju8im6zWLR8ZvFwT2tUEdIBAAAwqt6Po6NLU6cvutq+eGqB8rMyhntao4qQDgAAgFHT6Q3o48auAcftrW5QpH36lWO07WIsQjoAAABGzQdn2wa8eJHbZ+iPJ5slSZWuXE0qyBqBmY0uQjoAAABGhcdv6Fh954Dj3jreKJ8RlDR2L150PkI6AAAARsWH59oVGGAZPRAM6g/HQhcvmpjv1GxX7khMbdQNKqT/8Y9/HKp5AAAAYBzxG0F9eK5jwHF/Od2qNk9AknRVZaksY/TiRecbVEi/7LLLtHz5cv3sZz+T39//JVwBAACAiOq6DvkCwX7HmKap338UaruY57Rr8ZSCQR83XUL+oEL6li1bFAwG9fnPf15Tp07VQw89pNOnTw/V3AAAADAGBYOmjtS2DzjuaH2nattC/dOXzyqW3Tb4Su15ZXmDfo6RMKhX+oUvfEH79+/X/v37dcMNN+ixxx7TzJkz9elPf1qvv/76EE0RAAAAY8mxhk51+YwBx/2+ul6SlGGz6PIZRYM+rivPqTkTx0FIj7jsssv0b//2bzp16pQ2bNigt99+WytXrlRVVZX+3//7f/J4Br6CFAAAAMY+0zR1uHbgixfVtnmiNeuXVExQtsM+qOParRYtnTn4oD9ShrS7i8PhUHZ2thwOh0zTVFdXl+677z7Nnj1b+/btG8pDAQAAIA2dbnarzR0YcNzecC26RdKVswbfdnHR1ALlZabPVUqHJKT/5S9/0Ze//GVNnjxZ3/zmN7V06VLt379fx44d05/+9CdNnjxZX/7yl4fiUAAAAEhj750ZeBW93ePXn063SJIumpSv4lznoI5ZkuvQ3DQpc4kY1P83eP7557Vp0ybt3btXpaWl+l//63/pvvvuU1lZWXTMokWL9P3vf1833njjoCcLAACA9FXb6lFTp2/AcX841igj3D/96tmDW0W3WaWlM4vTpqtLxKBC+h133KElS5bo6aef1h133CGHw9HnuOnTp+vOO+8czKEAAACQ5t4/2zrgGF8gqP3HmiRJUydkaVpR9qCOuXByoQqy0qfMJWJQIf13v/udrrrqqgHHzZw5U88888xgDgUAAIA01tTpU22rd8Bx73zcLLc/1PnlqtmDu3hRca5DF01KrzKXiEHVpMcT0AEAAID346hFD5qm9laHThidkJ2h+ZPykz6ezSotm5F+ZS4RQ9rdBQAAADhfm8evU81dA457/0ybGsM161dWlshmTT5gLygvUEF2+pW5RBDSAQAAMKw+ONMm0+x/jDdg6OVDZyVJmRlWXVIxIenjFeUMbhU+FRDSAQAAMGzcPkPHGzoHHLf7/XNq6fJLkm6YXyan3ZbU8awWadnMYlkHsQqfCgjpAAAAGDZ/Od2i4ACr6KeauvTm0UZJUkVRti6fkfyVQReUF6gwu++Og+kkbUP6XXfdJYvFcsGf/q5wumXLlgvuV1tbO4KvAgAAYOw61+bR0fr+V9EDwaBeePe0TEk2q0Vrl0yWNcmTPSdkZ2hBeXqXuUQMqgXjaHrooYf0t3/7t72233rrrXI6nbrssssGfI5nnnlG8+bN67GtuLh4yOYIAAAwXgWMoPYfbxpw3O8+bNC5tlBrxmvnlsqVn5nU8ayW0EWL0r3MJSJtQ/qsWbM0a9asHtveeOMNNTQ06Nvf/rZstoHrmKqqqnTppZcO1xQBAADGrT+fblWHJ9DvmLp2j357pE6S5Mpz6pNzSpM+3kWT8lWUk/5lLhFpW+7Sl6eeekoWi0X33HPPaE8FAABg3Gro8OrDc+39jgmapna+WyMjaMoi6bZPTJHdmlw0LcjKUNXkgqT2TVVpu5J+vtbWVu3YsUMrV67UjBkz4trnlltuUX19vQoKCrRixQp997vfVVVVVb/71NXVqb6+vse26urqpOcNAAAwlgSDpvYfaxqw5eKBE0062Rjqnb5sVrGmFWUndTyLRVo6s2hQPdVT0ZgJ6c8995zcbrfuvffeAceWlZXpwQcf1LJly5Sfn6+DBw/q0Ucf1bJly7R3714tXrz4gvtu3rxZGzduHMqpAwAAjBmHzrSq1e3vd0yr269XDoWadRRmZeiGiyYmfbwF5fkqyXUmvX+qspjmQP+dkx4uu+wyHT9+XDU1NXI6E3+jTpw4oYULF+q6667Tiy++eMFxF1pJX7NmjQ4dOqQFCxYkfGwAAICxoKXLp1cO1fbbctE0TW3bd1If1IbKYb64fLrmluUldTxXnlMrL3LJkmQ3mJHy3nvvqaqqKqGsOCZW0v/yl7/o7bff1te+9rWkArokTZ8+XVdddVW/rRslyeVyyeVyJXUMAACAsco0Te071jRgT/RDZ9qiAf3iqYVJB3Sn3aorK0tSPqAna0ycOPrUU09Jkr70pS8N6nlM05Q1yRMWAAAAxrMPzrarqdPX75guX0Av/fmMJCnbYdNNCyclfbwrKouV5UjuqqTpIO0Tqdfr1bZt23T55ZcPeNJnf44fP669e/dq2bJlQzg7AACAsa/N49ehmtYBx+06VKsOb6gt480LJynXmVxRx/zyfE0qyEpq33SR9uUuv/rVr9TU1HTBVfR7771XW7du1dGjR1VRUSFJuv766/XJT35SixYtip44+sMf/lAWi0WPPPLISE4fAAAg7b11rEmBAepcqus69MeTzZKk2a5cXTy1MKljleY5tWiMtVvsS9qH9Keeeko5OTm6/fbb+3zcMAwZhqHY82MXLlyo559/Xo899pjcbrdcLpeuu+46PfTQQ5ozZ85ITR0AACDtfXSuXXXt3n7H+AJB/epPNZKkDJtFay6enFQtucNu1RWzxs5VRfuT9iH9v/7rv/p9fMuWLdqyZUuPbT/5yU+GcUYAAADjQ6c3oHdPtQw47rXD56L16jfML9OEJK8MumxmkXKSLJFJN2lfkw4AAIDRceBEkwJG/2UuNS1u/b66QZI0ZUKWls8qTupYc8vyNGVCchc8SkeEdAAAACTsREOnzrR4+h1jBE3tfOe0gqZktUi3LZkiaxJlLsW5Di1JsoY9XRHSAQAAkBCP34ieBNqf3x6p05nWUJC/Zk6pygoyEz5Whs2iKytLxkUdeixCOgAAABLyx5PN8gaC/Y559+NmvXa4TpJUkuvUirnJXQxy2czipFs1pjNCOgAAAOJ2qqlLJxu7+h1zrKFDL7wT6uaSmWHVnUunKcOWeOycMzFXU4vGTx16LEI6AAAA4lLX7tEfjjb2O6a+3auf7ftYhmnKZrHozqUVcuUnXuZSlJOhJdMmJDvVtEdIBwAAwICaOn1640h9vxct6vAGtPUPJ+T2G5Kk2z4xWTNLcxM+lj1ch24bZ3XosQjpAAAA6Fdrl1+/PVwnfz/tFv1GUP/+hxPRfugr57mSXglfOqNIeZkZSe07VhDSAQAAcEHtHr9eO3Ku3xNFg6ap7W+f0qlmtyRpydRCXTcvuRNF503KU0VxTlL7jiWEdAAAAPSpyxfQa4fr5Pb138nlv96r1aEzbZKkGSU5WvuJybIk0Q99blmuPjGO69BjEdIBAADQi8dv6LXDder0Gv2Oe+t4k373UeiKoqW5Tt25tEJ2a3KdXC6pKEpqrmMRIR0AAAA9+AJB/fZwndrcgX7HfXiuXf/x51CrxRynXV+8YrqyHLaEjzdnYq4unU5Aj0VIBwAAQJTfCOq3R+rU3OXvd9zZVreefetjBU3JbrXoC8sqVJTjSPh4swnofSKkAwAAQJJkBE397sN6NXb4+h3X6vZr65sn5AsEZZH0mUunJnXRodkTc3UZAb1PhHQAAAAoGDT13x/V61ybt99xXr+hf/vDCbV5QqUwq6vKVDW5IOHjVboI6P0hpAMAAIxzpmnqD8cadabF0+84I2jq5wdO6WxraNzSGUW6qrIk4ePNKs3R5TMI6P2xj/YEAAAAMLreOt6kk41d/Y4JBIN64Z0aHTnXLkmaOzFPtywqT7jV4qzSHC2dWZz0XMcLQjoAAMA4FQya+uPHzTpa39nvOK/f0LNvfayP6jokSZMKMnX7ZVNlsyYW0GcS0ONGSAcAABiHPH5De6sbBqxBb/f4tfUPJ6KlMFMmZOkLy6fLmZFYq8WZpTlaSolL3AjpAAAA40xzp0+/+6h+wAsVNXR49cze49F2jHMn5umOy6fJYU/stMYZJaGAnsxVSMcrQjoAAMA4crKxU/uPNSkQNPsdd6qpS1v/cEJdvlCQv6RigtZcPDnhEpfISaIE9MQQ0gEAAMYB0zT17qkWHT7bPuDYI7Vtevatj+U3QkH+2rkuXX+RK+GgvXhqgRaUJ96eEYR0AACAMc8bMPRmdWO0dWJ//niySTvfrVHQlCyS/r+Ly7V0RmIne9qtFi2bWaxpxYlf4AghhHQAAIAxrKXLp9991KCO8MWHLsQ0Tf32SL12f3BOUiho337ZVM1PcCU8M8OqT84pVUmuM+k5g5AOAAAwZn3c2KV9xxoHrD8PmqZe+vMZ7T/eJEnKyrDpC8srVFGck9DxCrMzdM2cUuU4iZiDxV8QAABgjDFNU38+3ar3z7QNONZvBPX8gVN6/2xobGFWhu66Yrpc+ZkJHXNSQaaurCxJuPML+kZIBwAAGEN8gaDePNoQ7Wveny5fQP/+h5M62RS62mhZfqa+eMV0FWRlJHTM2RNzdWnFBDq4DKG0/U+d119/XRaLpc+fffv2Dbh/XV2d7rrrLpWUlCg7O1vLly/Xnj17RmDmAAAAw6OuzaNX3quNK6Afb+jU/3mtOhrQZ5Tk6G+unplQQLdYpE9UFOqy6bRYHGppv5L+/e9/X9dee22PbVVVVf3u4/V6tXLlSrW0tOjxxx+Xy+XSpk2btHr1au3evVvXXHPNcE4ZAABgSAWMoP58ukVHajsGHGsETb12+JxeP1KvSKX6wskFWnfJFNlt8a/f2m0WXVlZosmFWUnOGv1J+5A+e/ZsLVu2LKF9nnrqKR06dEhvvvmmli9fLkm69tprtXjxYq1fv1779+8fjqkCAAAMubo2j/Ydbxqwe4skNXZ49Yu3T+lUs1tSqIPLpxZO0rIELzaU7bDpmjmlmpDjSHre6F/ah/Rk7Ny5U3Pnzo0GdEmy2+2688479cADD6impkaTJ08exRkCAAD0L5HVc9M09c7HLXrpL2fkCwQlherPP3vZVE1M8ATRohyHrplTqiyHLal5Iz5pW5Me8Xd/93ey2+3Kz8/XjTfeqN///vcD7nPo0CEtWrSo1/bItvfee2/I5wkAADBU6to9evlQbVwB3e0z9NyBU/rlO6ejAf3KWcW6b8WshAK63WrR4qkFumH+RAL6CEjblfSCggJ97Wtf04oVK1RcXKzq6mr96Ec/0ooVK/Sb3/xGN9544wX3bWxsVFFRUa/tkW2NjY0X3Leurk719fU9tlVXVyf5KgAAAOKXyOq5JB1r6ND2t0+r1e2XJOU57fr0JVM0Z2JeQsedVJCpS6dPUF5mYl1fkLy0DelLlizRkiVLovevvvpqrV27VgsXLtT69ev7DemS+q276u+xzZs3a+PGjYlPGAAAYBDq2j3adyy+2nMjaGr3B+f0uw+7Tw6dV5an2z4xRbkJXGjIabfqExUTNKMksYsaYfDSNqT3pbCwULfccov+5V/+RW63W1lZfZ9tXFxc3OdqeVNT6Cpbfa2yR9x///1at25dj23V1dVas2ZN8hMHAAC4gERXzxs6vHr+wCnVtHSfHHrTwklamuDJoTNKcrRkWqEyMyhtGQ1jKqRLoRMjpP5XwxcuXKiDBw/22h7Z1l8LR5fLJZfLNchZAgAADOzjxi796XRL3Kvnb51o0n8eqpXPCNWeTyrI1GcvnZrQ1UNzM+1aOqMo4RNKMbTGVEhvbm7Wr3/9a1188cXKzLzwB2vt2rW6//77tX//fi1dulSSFAgEtG3bNi1dulTl5eUjNWUAAIBezrV59O7HLWrq9MU1/lh9h379l7Oqbeu+iNFVlSW6Yf7EuHufWy3SvEn5Wji5QDYrFyYabWkb0j/3uc9p2rRpuvTSS1VSUqKPPvpIP/7xj3Xu3Dlt2bIlOu7ee+/V1q1bdfToUVVUVEiS7rnnHm3atEnr1q3To48+KpfLpc2bN+vIkSPavXv3KL0iAAAw3jV3+vSnUy062zrwFUMj43cdOqtDZ9qi2yZkZ2jNksma7Yr/5NDiXIeWzihSYTZ9z1NF2ob0RYsW6fnnn9e//Mu/qKOjQ0VFRbrqqqv07//+77rsssui4wzDkGEY0TIYSXI6ndqzZ4/Wr1+vv//7v1dXV5cuvvhi7dq1i6uNAgCAEdfpDejPp1t0oqErrvG+QFBvfFiv//6oXoFgKONk2CxaMdelqypLlBHn6nmWw6qq8gJVunITqlfH8LOYsekVSXnvvfdUVVWlQ4cOacGCBaM9HQAAkCY8fkPvnWlTdV27wmXk/TJNU3+padUrh2qjbRUlafGUAq2umqSCrPhaJDrsVl00KU9zJ+bFXQ6D5CWTFdN2JR0AACBdBYygDte264OzbfIb8a2Xnmlx66W/nNHJxu7V9vLCTN26qFwVxfG1SLTbLJo7MU8XTcqXw044T2WEdAAAgBESDJo61tChgzWtcvviWDqX1OEN6NX3a/X2ieZoz/Mch003LijTJyomyBpHmYrNKlW68rSgPJ+WimmCkA4AADDMgkFTR+s79P7ZNnV6jbj28foN7TvepDc+rJPHHwr0Vot0xawSXTfPFVfYtlpC/c6rJhcoJ4GLGGH08W4BAAAMEyNoqrquQx+cbVOXL75w7vYZevNYg96sbpTb373PnIm5umnhJLny4utfXlGcrYVTCpSfGV+dOlILIR0AAGCIBYygPqrr0OHatoTKWvZWN2jfsUZ5A937uPKcWr2gTHPL8uLqwDJ5QpYWTS7QhBzaKaYzQjoAAMAQ8RtBfXiuXYfPtvcI2v1pdfv1+4/q9daJph4nkZYXZGrFXJfml+fHVXc+tShLVeWE87GCkA4AADBIvkAonB+pjT+cN3f69MZH9frjyWYZwe5wPq0oW9fOLdWciQOvnFssUkVRthaUF6ggm7KWsYSQDgAAkCSP34iG83hbKTa0e/X6h/X606lmxWRzzSzN0bVzXZpZkjNgOLdapOklOVpQnq88as7HJEI6AABAgmpbPaqu69Dp5q4eQftCgqapY/Wd2n+8Ue+faVPsLnMn5mnF3NK4ep3brNLM0lzNn5RPt5YxjncXAAAgDt6AoWP1naqu61C7JxDXPl3egP74cbPeOt6kxk5fj8cWlOdrxVyXJhdmDfg8dqtFs1yhcJ7loM/5eEBIBwAA6Edde2jV/FRTl4w4ys1N09SpZrf2H2vUwZpWBWKW2u1WixZNKdTVs0s0MX/gVopOu1WVrlzNLcvjIkTjDCEdAADgPL5AUCcaQ6vmLV3+uPbx+g396XSL3jrepLOtnh6PleQ6tXRGkZZMK1S2Y+D4VZSToTkT81RRnCObdeDOLhh7COkAAAAKrYDXtXt1oqFTJxu7eqyA96e21aP9xxv1p1MtPTq7WC3S/PICLZ1RFPfJoNOKsjWnLE8luc5BvRakP0I6AAAYt4JBU2fbPDrV1KWaZnfc7RPbPX4drGnVn0616HSzu8djBVkZumx6kS6dPiGuq31mO2yqdOWq0pVLSQuiCOkAAGBc8RtBnW3x6FRzl860uONunej2GXr/bKv+fKpVR+s7enRosUiaPTFXS2cUa25ZXlwXH5qY79SciXmaXJglKyUtOA8hHQAAjHnegKGaZrdONbtV2+qO6wRQKRToD9e268+nWnTkXHuPiw5J0oTsDC2eUqhLpxepKI4rfdptFs0oydEcVx4XH0K/COkAAGBM6vQGdLrZrZqWLtW1eePqZy5JRtDU0foO/flUi94/29arBCbHadeiyQVaPLVQUydkDVhrLoVOBK105amiOFsZNmsyLwfjDCEdAACMGQ0dXtU0u1XT4o67K4sUWjE/Vt+pD2rb9F5Nqzp9Ro/HnXarFpQXaPHUAs0syY2r44rdZtH04hxVunLjWmUHYhHSAQBA2goYQdW2eVTT7NaZVrfcvjjrWCR1eAM6UtuuD862qbquQ77zamDsVovmluVp8ZRCzS3Li3sFPLRqnquK4hxWzZE0QjoAAEgrbp+hmpYunW52q67NG3erxEiLxcPhYH6qqUvn72mzWDSzNEeLpxRqfnl+3N1W7DaLKoqyVenKVTHtEzEECOkAACCl+Y2g6tq9qm31qK7No+YEyliMoKkTjZ06fLZNH9S2q6nT12tMtsOmuRPzNG9SvmYn0AbRYgldpGh6cbaml7BqjqFFSAcAACklYATV0OFTbZtH59o8au70xX3Sp2maqm/3qrq+Q0frOnSsobPP3ucluU5dNClP88ryNa0oO+6reloskivPqWlF2ZoyIVtZDvqaY3gQ0gEAwKgKBk01dHpV1xZaLW/s9MbdIlGSWt1+HQ2H8qP1HWrzBHqNsUiqKM7RRZPydFFZvkry4i9JsVqkifmZmlqUpSkTsrngEEYEIR0AAIwoXyCoxk6v6tu9auzwqb49/rpySfL4DR1v6FR1XYeq6ztU3+7tc1x+pl2zSkNX8pw7MU/Zzvhjj9UiTSzIDK+YZ8lpJ5hjZBHSAQDAsGrz+NXQHgrlDR0+tXn8MuPP5Or0BnSysUsnGzt1orFTNS3uPstfnHarZpbkaJYrV5WluSrNc8bVwzx2/4n5mSovzNSUCdly2Kkxx+ghpAMAgCETMIJq6vSpvqN7pbyvmvALMU1TTZ0+nQiH8pONXarv6Hul3GaxaGpRtipdOaoszdXkCfHXlkuSzSqV5jk1MT9Tkwqy6GWOlEJIBwAASfEGDDV3+tXc5VNzp09NXT61ewIJrZIbQVNnW9062dilE+FQ3uHtXVMuhUpQJhVkaUZJjmaV5mp6SXbCZSgTsjNUVpCpsoJMleY6ZacjC1IUIR0AAAyoyxdQU6dPLV1+NXX61NzlU6fXGHjHGKZpqrHTp9PNbtU0h/qcn2l1y2/0neoddqumFWWrojhb04tzkqoNz3HaNDE/U2X5oWDOSZ9IF2kb0l977TVt27ZNb775pk6dOqXCwkJdeumlevjhh3XJJZf0u++WLVt099139/nY2bNnVVZWNhxTBgAg5Xn8htrcfrV5/Gp1h36aO/0JlaxEtLr90TB+usWtmma33P4LB/u8TLumF+eoojhbFcU5KsvPTKh8RZIyM0J15aEfp/IyMxKeN5AK0jak/9//+3/V2Nior33ta5o/f77q6+v14x//WMuWLdN//ud/6rrrrhvwOZ555hnNmzevx7bi4uLhmjIAACnD7TOiIbzN41drV+h2MmHcNE21eQI62+rWmRaPalpCK+V9tUKMsFktmlSQqcmFWZpaFFopn5CdkdCJnpKUYbP0COWF2dSVY2xI25C+adMmuVyuHttWr16tyspKff/7348rpFdVVenSSy8drikCADDqYsN4S5dPbZ6AWt1++ZII41Kohryu3aPaVo/Otnp0ptWt2laPunwXXiG3KHSC5pQJoXaGUyZkqSw/M6l6cLvNotJcZzSUF+U4Eg72QDpI25B+fkCXpNzcXM2fP1+nTp0ahRkBADB6PH4jHMS7y1QGE8YlqcMbUF1bKIyfbfWottWtc+1eGQP0NJ+QnREN5JMnZGlyQZacSdSCWyxSQVaGinMcKs51qiTXoYKsxFfbgXSUtiG9L62trXrnnXfiWkWXpFtuuUX19fUqKCjQihUr9N3vfldVVVX97lNXV6f6+voe26qrq5OeMwAAifAbwXAQD53E2TKIMpWITm9A59o9qmvz6lybR3XtXtW1edTZz+q4FGqB6Mp3alJBliYVZIZ/spTlSO7kzCyHVcU5ThXnOlSSG1olz6D7CsapMRXS/+7v/k6dnZ168MEH+x1XVlamBx98UMuWLVN+fr4OHjyoRx99VMuWLdPevXu1ePHiC+67efNmbdy4cainDgBAD8GgqTZPdwhvCZerJNpRJcI0TXV4A2ro8IWDuEfn2ryqa/eq8wItD2NlO2wqK8hUeTiQlxVkqjTPKbs1uRCdmWHVhGyHCrMzosE8J4ErggJjncU0E+lmmroeeughfe9739M///M/6ytf+UrC+584cUILFy7UddddpxdffPGC4y60kr5mzRodOnRICxYsSPjYAIDxy+M31O4JqMMbULvHrw5PQC1uv9rc/j6vqjkQvxFUQ0foyp6hK3x2/3j8A6+2O2xWufKdcuWFar5deU6VFWQpP9OeVJmJxRLq2hIJ5BOyHZqQ7Uh6tR1IR++9956qqqoSyopj4j9ZN27cqO9973v6x3/8x6QCuiRNnz5dV111lfbt29fvOJfL1Wc9PAAAF9LpjYTwnmG83RtQ4AI9wvsTCIZKXho7fGrq9Kq+wxcK4u1etbj9cT1Hhs0SE8S7fxdkZ8iaZM23w25VfqZdE3IcmpCdocJshwqzMrhgEJCEtA/pGzdu1IYNG7RhwwY98MADg3ou0zRlTfJ/2wEAxidvwFCX11CX31CXN6Aun6FOX0Bun6FOnyG3LyAjiXJxb8BQU6cvHMRDP42d3ugFheKN9vmZdpXkOkM/eU6V5jpUmpepwkGE8cwMqwqyMpSflaGC8E9+Zgar48AQSuuQ/sgjj2jDhg369re/re985zuDeq7jx49r7969uv7664dodgCAdBcMmuryG+r0BsI/oQDe5QuF8S6voUAyNSkKrYa3dvnV3OVXc1foCp6Rq3k2dfrUEUedeITDZlVJrkMlec5oIC8Nd0NJpquKJFktUo7TrtxMu/Izw0E8y66CrIyEr/oJIHFpG9J//OMf6+GHH9bq1at188039ypTWbZsmSTp3nvv1datW3X06FFVVFRIkq6//np98pOf1KJFi6Injv7whz+UxWLRI488MuKvBQAwOoygGQ3cHbFB3BsIh3FDyZ655TeCaguf8Nnc6esVxtvc8a+GS1JWhk1FOQ4V5ThUnOtQcY5DRTmhDijJ1os77FblOu3Ky7Qr12lXTsztbIeNVofAKErbkP7SSy9Jkl555RW98sorvR6PnA9rGIYMw1Ds+bELFy7U888/r8cee0xut1sul0vXXXedHnroIc2ZM2dkXgAAYFj5jaC6fIbcPiMaxN3hVfHQNiPptoVB01S7J6DWLp9a3N3dV1pjOrHE0zEllkWhnuCF2eEAnhsO5DkOFec4B9XWMNeZobzMUADPc2YoNxzEHXZKPIFUNWa6u4ymZM7YBQAkJ1KC4g4HcLc//OMz5PYH5PYF1eULyJ/ECZlSKNy3h6/K2eYJrXi3uf3RK3W2hbcnWuVikZSflaEJ4Q4nhdkOFeWEQvmE7NBFemzW5Fausx226Ap4pDwlcp+TNoHRN267uwAA0p/fCMrtN+TxG/L4gj3Ctyd8u8tnDOpy9pHOKu2egNrCv9s9frW5Q/db3f5+L2/fH4fNqoLsDBVGTqbMzlBhVih8T8gO3U+2p7gU6saSlxmqC8/PzIgG8bxMgjgwFhHSAQDDxm8E5fGHykoiv90+Q96AIXdMEPf4kjsB0zRNeQPBaDvD2BAeCeCR310+I6Ea8Fh2q0X54Q4mBVl2FWSFen4XxoTxzAzroGu4bVYp2xEK3qHjRVbF6ZwCjDeEdABAXIygKV8gKF8gKK9hyOsPhkN4UJ5A6H7ktzf8O5ngHTTNUPtCb0AdvtCJnO0evzq8AXWE+4zH3k62u0pEjsMWDeD5kQ4mkdvhleusjKE5iTIzw6psh105Tlv0d44jdJJmjtOuzCQ7sQAYewjpADDORFafQz+hMO0zgtFw7Qs/5guEtkeCebJhOGia4XaFAXX6ujunRDqpdMTc7/CG+ooPMnfLalG4a0nMCZOR2zEnUeZm2gdVghI5VmaGTZkZVmVm2OS025TlCN8P385yhMJ4sjXnAMYfQjoApCnTNHuE6Njb3pj73kBQ3nCpiTcQWv1OtmWAETSjdeLRXuHh253e8O+YQN4VvqjPUHQosCjctzt8cmSu0668mNux23Oc9qQv1BORYbMoy2FTtsOmzIzQyndWhk1Z4UDuDN+mQwqA4UBIB4BRdn7A7nE/Zrvf6Bm+hyJse3xGd6cUf3drwki7wvODuMef3EmbF5KZYVWOwx4N3znOUNlHjqM7bOc4bcrLzFC2wzbo4G2xSE57aMU7K8MmZ0b37WxH6HdW+DcnYwIYTYR0ABgkvxEKzL4eAdrsEaz9McE6dlwgaCYdtANGzImX/mCPLiieHm0Je/9Otj94f2wWi7JjaqyznXblOGy9arCzIwHcMTRB2GZVuMzEKqe9Z/COlKFEbjvtgz+5EwBGAiEdwLhmBM1QcDaCChhmjzAdCJo9grXfMOUzDPkCoTITf/ixZOqng6YZLUVx+w15Y8K2J9KGMBK+/ZFuKIY84S4p7iS7ocQrEmyzw4E7y9F9u/vHHl19znHahywA222WnoE7/DuyAt4dyEP3M1jxBjAGEdIBpJ1AOED7I8E6GArQASP0u8f2QPfY6JigmXTAjg3Xnmidd0xHk8j2cKcTT3h7JHxHHvcFgkNSp90fu9USWlF29F3OEam3Dt2293hsqE5wzLBZ5MywyWGzyGkPBepI4Hbaw2G7RxAfumMDQDojpAMYVqZpym+YofZ9RlBGsDsoR0J1INi9ih0bvgPR8B267QuE9k8kWEeOH+peYsR0MenZySQSsCNdTSInWvrCLQYjjyd7IZ1k2MIhO/O8GuroyYuO7pKOrJiSjsj2oVhhzrBZ5LBblWGL/FjksFmVYe9532EP/5x3m9ISAEgOIR2ATNNUIGhGSz+MYOh+JDhH7wcj4dmUEQ7Qfe0TGRfZFq9AMCh/uJTEGzDkD5jyGkbfJ1Se172ku6NJ9/jI9uFese5Lhs0SXR2OlGdE2vNlhleMIy36Mh3dQTy2ld9gQ7bVolCQtlvliAnbjug2a0wAt0SDdSSQ07UEAEYPIR1IA5HyjkjoNcKB2QiH4j639xG6jUiAjoxNMEjH1m/7A7G/+9humH2eKNnn/fDtYSyxjovdGgqq55diRG/bQ233nPaeddLOmFKNSBgfTMlGJFzbw6vU9pgV68jt2JXtjJiwHdknw2albAQA0hghHUiQaXYH22B4BToYE4pjf3ptM0Mr0JFwbPSxf2SV2ggq+rs/QbN7xfv8Wuxo7XWw+3F/zNgeYyLBOjouPDYaukc/RMeKBNnYUB1bbhEJ1aHSC1v3bVvv4O2wD74W2mpRjwBtt3YHabvNErM9ZkwfYZtwDQCQCOlIU+cHZSNoKhhUOAR3b4sNx7FBODZcdz8eKreIhN6g2XfwvlBQjcypR411THlINECHV8V71mV3P+aPKTMJxDzu7yuIh8N+qrJI0ZXdaKnFeWUWfd139lHbHLntDJdq2K2WpOudLZZQvXeGzSKb1aoMq0X2cLC22yyyW63h391hOhKuo0Hc2jNkE6wBAEOJkI6kBKOrwjEh2VQ0CPcVgnsG3fMe7/V83avIkYAdO+b8vtKRYB0p94itjY4E59gTFo2Y8BwpJTm/njoQE4IDMd1Dzh8XG8TTiUUKnfxnPf/EQKscdkvP+9FwGrodqWeOLbNwxNQ1R7YnG6St4RBtjwnR/d23R+7HhO3Y+5HbsdsAAEhlhPQ019Dh7RV+ewbdmFXlmLAbNMMrv2Y4XIcfO/+2YZrRFWIjHG59gd7lHBeqh+4+mTDYx/hgTJ30eduCvUN3ZFvsMSL30ywf9ylSLhFdvY2WS1h61SH3fqz36q7D3rOm2dGjxtkimyWxAG2zSjarNfrbbrXIarH0CMC9fizdYTo0JrzfeaE59Ls7TFtZlQYAjHOE9DT3090fRtvS9VWaEQ3K55d/nBeq+6yjNrsDdOxq91gWu9Ka0UfpQ3dJRPdqbl/lEPZwKYU9JjTbY587NmDHrPYOxGIJXdXRarXIZlWPkBy5bQ2H49gwbI0JzLY+tkVCe2x4jg3arDwDADCyCOlp7udvnUq7MotYkZplW8wqa3Rl1dZzdbW/cobYANy9vWe9caQG2W61xjxuidYT28JB93yRMHx+CLZZJYulO+hG6py7Q7RFVkt3kI59jtiAbY0Jz9HbFousVvWxjRVmAADGA0J6mrNbLf2G9EhtbyQE28IlFbGrqrGrq+fft1p6huULjbP1FaYj223nb495Llt3kI0NwdbwirHFEhN2I0HV0n3fel5YtobDbffz9XzuSJDucTyLoscJHVOEYgAAMKoI6WnuK9dVKmiGO1XElF447aEOGDarNRp4rdbugGsJB9PofXWH1e4ALEk9V4QtPVaGu58jEm67g3RMiI4JztGgHA3UhGAAAIDzEdLT3JevmRUNvwAAABgbCOlpbrCXDQcAAEDqIeEBAAAAKYaQDgAAAKQYQjoAAACQYgjpAAAAQIohpAMAAAApJm1DekdHh77+9a+rvLxcmZmZuvjii/Xzn/88rn3r6up01113qaSkRNnZ2Vq+fLn27NkzzDMGAAAA4pO2LRhvu+02HThwQI8++qjmzJmjZ599VnfccYeCwaA+97nPXXA/r9erlStXqqWlRY8//rhcLpc2bdqk1atXa/fu3brmmmtG8FUAAAAAvaVlSH/55Zf16quvRoO5JF177bU6efKkvvGNb+izn/2sbDZbn/s+9dRTOnTokN58800tX748uu/ixYu1fv167d+/f8ReBwAAANCXtCx32blzp3Jzc7Vu3boe2++++26dOXOm36C9c+dOzZ07NxrQJclut+vOO+/UW2+9pZqammGbNwAAABCPtFxJP3TokC666CLZ7T2nv2jRoujjV1xxxQX3vfrqq3ttj+z73nvvafLkyRc8dl1dnerr63tsq66uTmj+AAAAQH/SMqQ3NjZq5syZvbYXFRVFH+9v38i4RPeVpM2bN2vjxo19PkZYBwAAwPkiGdHr9ca9T1qGdEmyWCxJPTbYfe+///5eZTavvfaavvrVr2rNmjX97gsAAIDx69SpU/rEJz4R19i0DOnFxcV9rng3NTVJUp8r5UOxryS5XC65XK4e2yZPnqxp06Zp6tSpcjqdA85/MKqrq7VmzRr96le/UmVl5bAeCwPj/UgdvBephfcjdfBepBbej9Qxku+F1+vVqVOnEuoimJYhfeHChXruuecUCAR61KUfPHhQklRVVdXvvpFxseLZ90IKCwv1V3/1VwnvNxiVlZVasGDBiB4TF8b7kTp4L1IL70fq4L1ILbwfqWOk3ot4V9Aj0rK7y9q1a9XR0aFf/vKXPbZv3bpV5eXlWrp0ab/7Hj58uEcHmEAgoG3btmnp0qUqLy8ftnkDAAAA8UjLlfRPfepTWrVqle677z61tbWpsrJSzz33nF555RVt27Yt2iP93nvv1datW3X06FFVVFRIku655x5t2rRJ69at06OPPiqXy6XNmzfryJEj2r1792i+LAAAAEBSmoZ0SXrhhRf04IMP6uGHH1ZTU5PmzZun5557Trfffnt0jGEYMgxDpmlGtzmdTu3Zs0fr16/X3//936urq0sXX3yxdu3axdVGAQAAkBLSNqTn5ubq8ccf1+OPP37BMVu2bNGWLVt6bZ84caK2bt06jLMbPqWlpfrOd76j0tLS0Z4KxPuRSngvUgvvR+rgvUgtvB+pI9XfC4sZu8wMAAAAYNSl5YmjAAAAwFhGSAcAAABSDCEdAAAASDGEdAAAACDFENJTXHt7u9avX68bbrhBpaWlslgs2rBhQ9z7b9myRRaLpc+f2tra4Zv4GDTY90KS6urqdNddd6mkpETZ2dlavny59uzZMzwTHgc6Ojr09a9/XeXl5crMzNTFF1+sn//853Hty3cjOYP5m/P5H1rJvhd89ofeYP994LsxtAbzfqTS9yNtWzCOF42NjXriiSe0ePFirVmzRk8++WRSz/PMM89o3rx5PbYVFxcPxRTHjcG+F16vVytXrlRLS4sef/xxuVwubdq0SatXr9bu3bvp05+E2267TQcOHNCjjz6qOXPm6Nlnn9Udd9yhYDCoz33uc3E9B9+NxCT7N+fzP/QG+/nnsz90BvPvA9+NoTcU2Sklvh8mUlowGDSDwaBpmqZZX19vSjK/853vxL3/M888Y0oyDxw4MEwzHD8G+15s2rTJlGS++eab0W1+v9+cP3++efnllw/1dMe83/zmN6Yk89lnn+2xfdWqVWZ5ebkZCAT63Z/vRuIG8zfn8z+0BvNe8NkfeoP594HvxtAbzPuRSt8Pyl1SXOR/sWD0Dfa92Llzp+bOnavly5dHt9ntdt1555166623VFNTMxTTHDd27typ3NxcrVu3rsf2u+++W2fOnNH+/ftHaWZj12D+5nz+hxaf/9QymH8f+G4MvbGSnQjp48Qtt9wim82moqIi3XbbbTp06NBoT2ncOXTokBYtWtRre2Tbe++9N9JTSmuHDh3SRRddJLu9Z9Ve5O8Z72ec70b8BvM35/M/tIbi889nPzXw3UhNqfD9oCZ9jCsrK9ODDz6oZcuWKT8/XwcPHtSjjz6qZcuWae/evVq8ePFoT3HcaGxsVFFRUa/tkW2NjY0jPaW01tjYqJkzZ/baHu/fk+9G4gbzN+fzP7QG817w2U8tfDdSSyp9PwjpI+j111/XtddeG9fYd999VxdffPGgj7l69WqtXr06ev+Tn/ykbr75Zi1cuFAPP/ywXnzxxUEfIx2Nxnshqd///TYW/tdcspJ9Pwbz9+S7kZzB/M35/A+tZP+efPZTD9+N1JFK3w9C+giaO3eu/vVf/zWusdOmTRu2eUyfPl1XXXWV9u3bN2zHSHWj8V4UFxf3uSLS1NQkSX2upIwXybwfw/H35LvRv8H8zfn8D62h/nvy2R89fDdS32h9PwjpI2jSpEn60pe+NNrTkCSZpimrdfyekjAa78XChQt18ODBXtsj26qqqkZ0Pqkkmfdj4cKFeu655xQIBHrU5Q727znevxv9GczfnM//0BqOzz+f/dHBdyM9jMb3g2/jOHT8+HHt3btXy5YtG+2pjCtr167V4cOHe3RdCAQC2rZtm5YuXary8vJRnF36Wbt2rTo6OvTLX/6yx/atW7eqvLxcS5cuTfg5+W70bzB/cz7/Q2uoP/989kcP343UN2rfj9HtAIl4vPzyy+b27dvNp59+2pRkrlu3zty+fbu5fft2s7OzMzrunnvuMW02m3nixInotpUrV5obN240d+7cae7Zs8f86U9/apaXl5t5eXnmwYMHR+PlpLXBvBcej8dcsGCBOXXqVPNnP/uZ+eqrr5pr16417Xa7+frrr4/Gy0l7q1atMidMmGA+8cQT5muvvWb+zd/8jSnJ3LZtW49xfDeGTjx/cz7/IyPZ94LP/vCI598HvhsjJ9n3I5W+H4T0NFBRUWFK6vPn+PHj0XFf/OIXe237+te/bs6fP9/My8sz7Xa7WV5ebt55553mkSNHRv6FjAGDeS9M0zRra2vNL3zhC2ZRUZGZmZlpLlu2zHz11VdH9kWMIe3t7eZXv/pVs6yszHQ4HOaiRYvM5557rtc4vhtDJ56/OZ//kZHse8Fnf3jE8+8D342Rk+z7kUrfD4tpmuZwrtQDAAAASAw16QAAAECKIaQDAAAAKYaQDgAAAKQYQjoAAACQYgjpAAAAQIohpAMAAAAphpAOAAAApBhCOgAAAJBiCOkAAABAiiGkAwAAACmGkA4AAACkGEI6AAAAkGII6QCAIeHxeLRkyRJVVlaqtbU1ur22tlZlZWVasWKFDMMYxRkCQPogpAMAhkRmZqZ+8YtfqK6uTvfcc48kKRgM6n/8j/8h0zT13HPPyWazjfIsASA92Ed7AgCAsWP27Nl68skn9dnPflaPP/64mpqa9Prrr+uVV17RpEmTRnt6AJA2LKZpmqM9CQDA2HL//ffrySeflGEYeuCBB/TII4+M9pQAIK0Q0gEAQ+7tt9/WZZddJofDodOnT6u0tHS0pwQAaYWQDgAYUp2dnbr00ksVDAZ17tw5XXPNNXrxxRdHe1oAkFY4cRQAMKT+9m//Vh9//LFeeOEFPfXUU/qP//gP/eQnPxntaQFAWiGkAwCGzJNPPqlt27Zp06ZNWrBggT796U/rK1/5ir75zW/qrbfeGu3pAUDaoNwFADAkDh48qKVLl+ozn/mMtmzZEt3u9Xp15ZVXqrGxUe+++64KCwtHbY4AkC4I6QAAAECKodwFAAAASDGEdAAAACDFENIBAACAFENIBwAAAFIMIR0AAABIMYR0AAAAIMUQ0gEAAIAUQ0gHAAAAUgwhHQAAAEgxhHQAAAAgxRDSAQAAgBRDSAcAAABSDCEdAAAASDH/P5NCfsIkjDYcAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# First, the mean of the response (default)\n", + "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n", + "bmb.interpret.plot_predictions(model, idata, \"x\", ax=ax);" ] }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plot_comparisons(\n", - " model=titanic_model,\n", - " idata=titanic_idata,\n", - " contrast={\"PClass\": [1, 3]},\n", - " conditional=[\"Age\", \"SexCode\"],\n", - " comparison_type=\"ratio\",\n", - " subplot_kwargs={\"main\": \"Age\", \"group\": \"SexCode\", \"panel\": \"SexCode\"},\n", - " fig_kwargs={\"figsize\":(12, 3), \"sharey\": True},\n", - " legend=False\n", - "\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The left panel shows that the ratio of the probability of survival comparing `PClass` $3$ to $1$ conditional on `Age` is non-constant. Whereas the right panel shows an approximately constant ratio in the probability of survival comparing `PClass` $3$ to $1$ conditional on `Age`. " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "bambinos", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.0" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below, instead of plotting the default target, `target=mean`, we set `target=alpha` to visualize how the model parameter `alpha` varies as a function of the `x` predictor." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Second, another param. of the distribution: alpha\n", + "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n", + "bmb.interpret.plot_predictions(model, idata, \"x\", target='alpha', ax=ax);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Last updated: Wed Aug 16 2023\n", + "\n", + "Python implementation: CPython\n", + "Python version : 3.11.0\n", + "IPython version : 8.13.2\n", + "\n", + "pandas : 2.0.1\n", + "matplotlib: 3.7.1\n", + "bambi : 0.10.0.dev0\n", + "arviz : 0.15.1\n", + "numpy : 1.24.2\n", + "\n", + "Watermark: 2.3.1\n", + "\n" + ] + } + ], + "source": [ + "%load_ext watermark\n", + "%watermark -n -u -v -iv -w" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "bambinos", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 + } \ No newline at end of file diff --git a/docs/notebooks/plot_predictions.ipynb b/docs/notebooks/plot_predictions.ipynb index a806b98c2..2f2f66831 100644 --- a/docs/notebooks/plot_predictions.ipynb +++ b/docs/notebooks/plot_predictions.ipynb @@ -1,997 +1,986 @@ { - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot Conditional Adjusted Predictions\n", - "\n", - "This notebook shows how to use, and the capabilities, of the `plot_predictions` function. The `plot_predictions` function is a part of Bambi's sub-package `plots` that features a set of tools used to interpret complex regression models that is inspired by the R package [marginaleffects](https://vincentarelbundock.github.io/marginaleffects/articles/predictions.html#conditional-adjusted-predictions-plot). " - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Interpreting Generalized Linear Models\n", - "\n", - "The purpose of the _generalized linear model_ (GLM) is to unify the approaches needed to analyze data for which either: (1) the assumption of a linear relation between $x$ and $y$, or (2) the assumption of normal variation is not appropriate. GLMs are typically specified in three stages:\n", - "1. the linear predictor $\\eta = X\\beta$ where $X$ is an $n$ x $p$ matrix of explanatory variables.\n", - "2. the link function $g(\\cdot)$ that relates the linear predictor to the mean of the outcome variable $\\mu = g^{-1}(\\eta) = g^{-1}(X\\beta)$\n", - "3. the random component specifying the distribution of the outcome variable $y$ with mean $\\mathbb{E}(y|X) = \\mu$.\n", - "\n", - "Based on these three specifications, the mean of the distribution of $y$, given $X$, is determined by $X\\beta: \\mathbb{E}(y|X) = g^{-1}(X\\beta)$. \n", - "\n", - "GLMs are a broad family of models where the output $y$ is typically assumed to follow an exponential family distribution, e.g., Binomial, Poisson, Gamma, Exponential, and Normal. The job of the link function is to map the linear space of the model $X\\beta$ onto the non-linear space of a parameter like $\\mu$. Commonly used link function are the _logit_ and _log_ link. Also known as the _canonical_ link functions. This brief introduction to GLMs is not meant to be exhuastive, and another good starting point is the Bambi [Basic Building Blocks](https://bambinos.github.io/bambi/notebooks/how_bambi_works.html#Link-functions) example.\n", - "\n", - "Due to the link function, there are typically three quantities of interest to interpret in a GLM:\n", - "1. the linear predictor $\\eta$\n", - "2. the mean $\\mu = g^{-1}(\\eta)$\n", - "3. the response variable $Y \\sim \\mathcal{D}(\\mu, \\theta)$ where $\\mu$ is the mean parameter and $\\theta$ is (possibly) a vector that contains all the other \"nuissance\" parameters of the distribution.\n", - "\n", - "As modelers, we are usually more interested in interpreting (2) and (3). However, $\\mu$ is not always on the same scale of the response variable and can be more difficult to interpret. Rather, the response scale is a more interpretable scale. Additionally, it is often the case that modelers would like to analyze how a model parameter varies across a range of explanatory variable values. To achieve such an analysis, Bambi has taken inspiration from the R package marginaleffects, and implemented a `plot_predictions` function that plots the conditional adjusted predictions to aid in the interpretation of GLMs. Below, it is briefly discussed what are conditionally adjusted predictions, how they are computed, and ultimately how to use the `plot_predictions` function." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conditionally Adjusted Predictions\n", - "\n", - "Adjusted predictions refers to the outcome predicted by a fitted model on a specified scale for a given combination of values of the predictor variables, such as their observed values, their means, or some user specified grid of values. The specification of the scale to make the predictions, the link or response scale, refers to the scale used to estimate the model. In normal linear regression, the link scale and the response scale are identical, and therefore, the adjusted prediction is expressed as the mean value of the response variable at the given values of the predictor variables. On the other hand, a logistic regression's link and response scale are not identical. An adjusted prediction on the link scale will be represented as the log-odds of a successful response given values of the predictor variables. Whereas an adjusted prediction on the response scale gives the probability that the response variable equals 1. The conditional part of conditionally adjusted predictions represents the specific predictor(s) and its values we would like to condition on when plotting predictions." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Computing Adjusted Predictions\n", - "\n", - "The objective of plotting conditional adjusted predictions is to visualize how a parameter of the (conditional) response distribution varies as a function of (some) interpolated explanatory variables. This is done by holding all other explanatory variables constant at some specified value, a _reference grid_, that may or may not correspond to actual observations in the dataset used to fit the model. By default, the `plot_predictions` function uses a grid of 200 equally spaced values between the minimum and maximum values of the specified explanatory variable as the reference grid.\n", - "\n", - "The `plot_predictions` function uses the fitted model to then compute the predicted values of the model parameter at each value of the reference grid. The `plot_predictions` function then uses these predictions to plot the model parameter as a function of (some) explanatory variable. " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import arviz as az\n", - "import bambi as bmb\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "from bambi.plots import plot_predictions\n", - "\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Gaussian Linear Model\n", - "\n", - "For the first demonstration, we will use a Gaussian linear regression model with the `mtcars` dataset to better understand the `plot_predictions` function and its arguments. The `mtcars` dataset was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973--74 models). The following is a brief description of the variables in the dataset:\n", - "\n", - "- mpg: Miles/(US) gallon\n", - "- cyl: Number of cylinders\n", - "- disp: Displacement (cu.in.)\n", - "- hp: Gross horsepower\n", - "- drat: Rear axle ratio\n", - "- wt: Weight (1000 lbs)\n", - "- qsec: 1/4 mile time\n", - "- vs: Engine (0 = V-shaped, 1 = straight)\n", - "- am: Transmission (0 = automatic, 1 = manual)\n", - "- gear: Number of forward gear" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Auto-assigning NUTS sampler...\n", - "Initializing NUTS using jitter+adapt_diag...\n", - "Multiprocess sampling (4 chains in 4 jobs)\n", - "NUTS: [mpg_sigma, hp, wt, hp:wt, cyl, gear]\n" - ] - }, - { - "data": { - "text/html": [ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot Conditional Adjusted Predictions\n", "\n", - "\n" - ], - "text/plain": [ - "" + "This notebook shows how to use, and the capabilities, of the `plot_predictions` function. The `plot_predictions` function is a part of Bambi's sub-package `interpret` that features a set of tools used to interpret complex regression models that is inspired by the R package [marginaleffects](https://vincentarelbundock.github.io/marginaleffects/articles/predictions.html#conditional-adjusted-predictions-plot). " ] }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Interpreting Generalized Linear Models\n", + "\n", + "The purpose of the _generalized linear model_ (GLM) is to unify the approaches needed to analyze data for which either: (1) the assumption of a linear relation between $x$ and $y$, or (2) the assumption of normal variation is not appropriate. GLMs are typically specified in three stages:\n", + "1. the linear predictor $\\eta = X\\beta$ where $X$ is an $n$ x $p$ matrix of explanatory variables.\n", + "2. the link function $g(\\cdot)$ that relates the linear predictor to the mean of the outcome variable $\\mu = g^{-1}(\\eta) = g^{-1}(X\\beta)$\n", + "3. the random component specifying the distribution of the outcome variable $y$ with mean $\\mathbb{E}(y|X) = \\mu$.\n", + "\n", + "Based on these three specifications, the mean of the distribution of $y$, given $X$, is determined by $X\\beta: \\mathbb{E}(y|X) = g^{-1}(X\\beta)$. \n", + "\n", + "GLMs are a broad family of models where the output $y$ is typically assumed to follow an exponential family distribution, e.g., Binomial, Poisson, Gamma, Exponential, and Normal. The job of the link function is to map the linear space of the model $X\\beta$ onto the non-linear space of a parameter like $\\mu$. Commonly used link function are the _logit_ and _log_ link. Also known as the _canonical_ link functions. This brief introduction to GLMs is not meant to be exhuastive, and another good starting point is the Bambi [Basic Building Blocks](https://bambinos.github.io/bambi/notebooks/how_bambi_works.html#Link-functions) example.\n", + "\n", + "Due to the link function, there are typically three quantities of interest to interpret in a GLM:\n", + "1. the linear predictor $\\eta$\n", + "2. the mean $\\mu = g^{-1}(\\eta)$\n", + "3. the response variable $Y \\sim \\mathcal{D}(\\mu, \\theta)$ where $\\mu$ is the mean parameter and $\\theta$ is (possibly) a vector that contains all the other \"nuissance\" parameters of the distribution.\n", + "\n", + "As modelers, we are usually more interested in interpreting (2) and (3). However, $\\mu$ is not always on the same scale of the response variable and can be more difficult to interpret. Rather, the response scale is a more interpretable scale. Additionally, it is often the case that modelers would like to analyze how a model parameter varies across a range of explanatory variable values. To achieve such an analysis, Bambi has taken inspiration from the R package marginaleffects, and implemented a `plot_predictions` function that plots the conditional adjusted predictions to aid in the interpretation of GLMs. Below, it is briefly discussed what are conditionally adjusted predictions, how they are computed, and ultimately how to use the `plot_predictions` function." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conditionally Adjusted Predictions\n", + "\n", + "Adjusted predictions refers to the outcome predicted by a fitted model on a specified scale for a given combination of values of the predictor variables, such as their observed values, their means, or some user specified grid of values. The specification of the scale to make the predictions, the link or response scale, refers to the scale used to estimate the model. In normal linear regression, the link scale and the response scale are identical, and therefore, the adjusted prediction is expressed as the mean value of the response variable at the given values of the predictor variables. On the other hand, a logistic regression's link and response scale are not identical. An adjusted prediction on the link scale will be represented as the log-odds of a successful response given values of the predictor variables. Whereas an adjusted prediction on the response scale gives the probability that the response variable equals 1. The conditional part of conditionally adjusted predictions represents the specific predictor(s) and its values we would like to condition on when plotting predictions." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Computing Adjusted Predictions\n", + "\n", + "The objective of plotting conditional adjusted predictions is to visualize how a parameter of the (conditional) response distribution varies as a function of (some) interpolated explanatory variables. This is done by holding all other explanatory variables constant at some specified value, a _reference grid_, that may or may not correspond to actual observations in the dataset used to fit the model. By default, the `plot_predictions` function uses a grid of 200 equally spaced values between the minimum and maximum values of the specified explanatory variable as the reference grid.\n", "\n", - "
\n", - " \n", - " 100.00% [8000/8000 00:17<00:00 Sampling 4 chains, 0 divergences]\n", - "
\n", - " " + "The `plot_predictions` function uses the fitted model to then compute the predicted values of the model parameter at each value of the reference grid. The `plot_predictions` function then uses these predictions to plot the model parameter as a function of (some) explanatory variable. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import arviz as az\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import bambi as bmb" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Gaussian Linear Model\n", + "\n", + "For the first demonstration, we will use a Gaussian linear regression model with the `mtcars` dataset to better understand the `plot_predictions` function and its arguments. The `mtcars` dataset was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973--74 models). The following is a brief description of the variables in the dataset:\n", + "\n", + "- mpg: Miles/(US) gallon\n", + "- cyl: Number of cylinders\n", + "- disp: Displacement (cu.in.)\n", + "- hp: Gross horsepower\n", + "- drat: Rear axle ratio\n", + "- wt: Weight (1000 lbs)\n", + "- qsec: 1/4 mile time\n", + "- vs: Engine (0 = V-shaped, 1 = straight)\n", + "- am: Transmission (0 = automatic, 1 = manual)\n", + "- gear: Number of forward gear" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [mpg_sigma, hp, wt, hp:wt, cyl, gear]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " 100.00% [8000/8000 00:19<00:00 Sampling 4 chains, 0 divergences]\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 20 seconds.\n" + ] + } ], - "text/plain": [ - "" + "source": [ + "# Load data\n", + "data = bmb.load_data('mtcars')\n", + "data[\"cyl\"] = data[\"cyl\"].replace({4: \"low\", 6: \"medium\", 8: \"high\"})\n", + "data[\"gear\"] = data[\"gear\"].replace({3: \"A\", 4: \"B\", 5: \"C\"})\n", + "data[\"cyl\"] = pd.Categorical(data[\"cyl\"], categories=[\"low\", \"medium\", \"high\"], ordered=True)\n", + "\n", + "# Define and fit the Bambi model\n", + "model = bmb.Model(\"mpg ~ 0 + hp * wt + cyl + gear\", data)\n", + "idata = model.fit(draws=1000, target_accept=0.95, random_seed=1234)" ] }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 18 seconds.\n" - ] - } - ], - "source": [ - "# Load data\n", - "data = bmb.load_data('mtcars')\n", - "data[\"cyl\"] = data[\"cyl\"].replace({4: \"low\", 6: \"medium\", 8: \"high\"})\n", - "data[\"gear\"] = data[\"gear\"].replace({3: \"A\", 4: \"B\", 5: \"C\"})\n", - "data[\"cyl\"] = pd.Categorical(data[\"cyl\"], categories=[\"low\", \"medium\", \"high\"], ordered=True)\n", - "\n", - "# Define and fit the Bambi model\n", - "model = bmb.Model(\"mpg ~ 0 + hp * wt + cyl + gear\", data)\n", - "idata = model.fit(draws=1000, target_accept=0.95, random_seed=1234)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can print the Bambi model object to obtain the model components. Below, we see that the Gaussian linear model uses an identity link function that results in no transformation of the linear predictor to the mean of the outcome variable, and the distrbution of the likelihood is Gaussian." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have fitted the model, we can visualize how a model parameter varies as a function of (some) interpolated covariate. For this example, we will visualize how the mean response `mpg` varies as a function of the covariate `hp`. \n", - "\n", - "The Bambi model, ArviZ inference data object (containing the posterior samples and the data used to fit the model), and a list or dictionary of covariates, in this example only `hp`, are passed to the `plot_predictions` function. The `plot_predictions` function then computes the conditional adjusted predictions for each `covariate` in the list or dictionary using the method described above. The `plot_predictions` function returns a `matplotlib` figure object that can be further customized. " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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9iIPTaRycTluPE3I7zJnuSg/vXvZkXpUMA4ikdUTSOl4dTaDN48BAyIW17W4uKSEiohWHIZvOiSSK6K20//vgWvNYvljGRLWbSWXnylg2b90nls0jls3jwIRZMSfA7MlcLarsC7jQ6ZchiQzeq0k0k0c0k8fvxxLoD7kwFPag0+ds9rCIiIgagiGbFswhiVjb7sbadrd1LKcX63arHI+ryFQKKw3UlhC8Mq+wssvvnBe8FbR7ZRZWrgJlA9amOD5FwvoOD2e3iYho2WPIpkXhkiWc1+nFeZ21wsqUVsRYLIeJSvieSKjQCrXCymoYr5KlamFlbat4v2JnYeUKllKLeO14Am+MJ9AfdGGo04Owl7PbRES0/DBk05IQBMHcPbDXX1dYGc3kzdnuhIqJylbx1cJKvVjG0bksjs7VCivdslRpIVjr4c0CupWnVAZGojmMRHPwK3YMhT1Y0+5i/20iIlo2mE6oaURBQIdXRodXxo5KYWWpbGAmpVlFlRMJFTMpDZXcjaxexKGZNA7N1Aorgy47eiudTPpCCnr9CmQuNVgxkmoBr4zGrbXb6zrcCHtlvqNBREQtraVC9nPPPYd//ud/xr//+79jbGwMgUAAF198Me677z7s3Lmz7txXX30Vd911F1544QVIkoSrrroK3/72t7Fu3bomjZ4awVbZ9KYnoOADa0MAzMLKqaS5lGQsnsNEXEV0XmFlPFdAPJfEm/MKKzu8ct1sd5fPCYlbxS9rxbKBY3NZHJvLwmkX0Rd0oT+koNPrhMjdSImIqMW0VMj+wQ9+gGg0ijvuuAObN29GJBLBd77zHVx66aX41a9+hauuugoAcPDgQVxxxRW48MIL8dOf/hSapuG+++7DZZddhtdffx0dHR1N/k6okRySiME2NwbbaoWVar40r42g2dkkpdUKK2fTOmbTOl49ngBghvcun7MueHewsHLZ0gplDM9mMDybgSyJ6AsqGGhzMXATEVHLEAzDMM582tKYnZ1FOByuO5bJZDA0NIStW7fiN7/5DQDgv/yX/4J9+/bhyJEj8Pl8AIDR0VFs2LABd955J/7mb/7mnJ/7rbfewtatW/Hmm29iy5YtC/9mztIzB6aQyBWW7PlWsqRaqG0VXymurBZWnoxDEtHjr/bvNsN30MXCyuXMUSmWHWhzodvHwE1ERI1zrlmxpWay3xuwAcDj8WDz5s0YGxsDABSLRTz99NP4r//1v1oBGwAGBwdx5ZVX4mc/+9n7Ctm0/PkVO/yKHZt7zJ8LwzAQy+YxVpnpHo+rmEyqKJTMvyvzxTJGolmMRGuFlS6HrW63yt6gAq/T3pTvh85dvli2lpTYbYK5CVLIhW4/lwsREdHSaqmQfTLJZBKvvvqqtVTkyJEjUFUV27ZtO+Hcbdu24de//jU0TYPTeeq2X7Ozs4hEInXHhoeHGztwajpBENDmkdHmkXFhfwCAWVg5m9asZSbj8VxdYWUuX8LhmQwOz2Ssx/Er9rplJr0BhT2cl4FCybA6lNT6sJt/OLFLCRERLbaWD9m33XYbstksvvrVrwIAotEoACAUCp1wbigUgmEYiMfj6O7uPuVjPvzww9i7d+/iDJhamk0U0O1X0O1XcMka81ihVMZUQrV2qxyP5zCXqRVWJtUCkmoBb02mrGMdHtkKbP1BF7r8Ttg5U9qy5vdhF4XajqP9QRcUBwM3ERE1XkuH7HvvvRePP/44vv/975/QXeR062bPtKb21ltvxY033lh3bHh4GLt27XrfY6Xly24TMdDmxsB7Civfu1V8Uq2tnY9kdEQyOl4bSwAARAHmTGnAZc16h30srGxFZQOYTuqYTup4eSSONo8DfUFzWYmPS4OIiKhBWjZk7927Fw8++CAeeugh3H777dbxtrY2ALUZ7flisRgEQUAgEDjtY4fD4ZOu/yaqUhw2DIU9GAp7rGNprVC3zGQ8rkItlACYwW0yoWEyoeHFEfP86prgvqDLLKwMKAi5HSysbDHRTB7RTB6/H0vCLdvgV+wIuBwIKHYEXHb4nHYWUBIR0TlryZC9d+9e7NmzB3v27ME999xTd9v69euhKAoOHDhwwv0OHDiAoaGh067HJnq/vE47zu+24/zuWmFlPFewAvd4PIfJhIZ8yexoMn9NcJVirxZW1sI3Z09bR1YvIauXMJnQrGOiAPgUOwKKHX5XLYBzp1EiIjqdlvu/xAMPPIA9e/bga1/7Gu6///4TbpckCZ/4xCfw1FNP4W//9m/h9XoBAMePH8e+fftw5513LvWQaZUSBAEhtwMhtwPb+gIAzK3iZ9O61c1kPK5iOqmhVOmUqRZKeHc2g3dn6wsre9+zVTwLK1tH2QASuYLZanPeG2h2m4CAy4GQ246QW0bI7YDPKfGdCiIiAtBiIfs73/kO7rvvPlx77bW47rrr8MILL9TdfumllwIwZ7ovueQSfPzjH8dXvvIVazOa9vZ2fPnLX27G0IkAmFvFd/mc6PI5sXPQPFYslTGV1DBeWeM9Flcxl9ZRbVBfLax8e6pWWNnucViBuy+goDugsLCyxRRKBiJpHZG0DsD8o0myCQi6HNYfXwzeRESrV0uF7F/84hcAgGeffRbPPvvsCbdX983ZtGkTnn/+edx999244YYb6rZV526P1Gokm4j+kAv9IRcAs6ZAK5QwOa+byXhCrduUaC6Tx1wmj9fnFVZ2vmfHyrDXCRvXCreUYl3wNs0P3m1uBzq8MpeaEBGtAi31m/75558/63N37txp7QBJtNw47Tas6/BgXUetsDKjF62Z7urOldl8rbByKqlhKqnhpZE4AHO5Qre1Y6UZvNtYWNlyTha8Q24H+kPsaEJEtJK1VMgmWs08soSNXT5s7KoVVibUQl03k8mECr1YK6w8HsvheCyH6mJhp120dqvsq4Rvv8IQ12pi2TxiWbOjScBlR3/Qhf6QgoDL0eyhERFRgzBkE7UoQTCXGQRdDlzQ6wdgFlbOpXUzeCfM4D2V1FCqbFmpFcoYns1geF5hpc8pWTPdfZWWgtyApXWYRZVJHJhIwuuUzKVFQQVtHrnZQyMiogVgyCZaRkRBQNjnRNjnxEWDQQBAsVzGdNLcKn4irmIsnkNkXmFlSisiNZXCO/MKK9vcDrN3d2XWuyegwCGxsLLZ0loRb0+m8PZkCm7ZZu5KGVIQdDlY+EpEtMwwZBMtc5JYWSISdFnH9KLZ63l+D+/4vMLKaDaPaDaPN8aTAMzCyrDXWbdVfKePhZXNlNVLODSdxqHpNABzKZBHluBxSvDKdnicEjyyBK9TYstHIqIWxJBNtALJkg1r291Y217bKj6rF61lJuaMt4qsXgRQ2Wo8pWE6peHlUbOwUhIFdPudtVaCQRfaPA5uFd8kWqEMrWB2nXkvySbAK0vwKXazi4nHgZDLAYmz30RETcOQTbRKuGUJG7u82NhlbuBkGAaSav1W8RPzCiuLZQNjlTBe5bSL6AmYM93VDXT8ip0dTZqsWDJ3H43nChit7DAqCuZGR2boltHucfBaEREtIYZsolVKEMwdCwMuB7bOL6zM6NZM90Q8h6mkhuK8wsqjkSyORrLW43hkqW6r+L6AAhf7QDdd2YAVvI9UrpckCghWZrrb3TJCHgc8vFZERIuCv12JyCIKAsJeJ8JeJ3YM1AorZ1K6tb57Iq5iJqVZhZUZvYiD02kcrKwdBoCgy163zKQn4IQscd1wsxXL83t2m9dLEgW4q2u9nRK8lc89sgS3Q4LIdflERO8LQzYRnZYkiugNKOgNKPjgWvNYvliu7Fhp7lY5HlcRy9bWCpszqGZbOgAQAHR4ZXOZSWXWu8vvhCRyzXCzFcvmsqGkWjjhNlEAXHJ98PZU1n57ZQZwIqLTYcgmonPmkESsaXdjzbzCyly+WLfMZDyhIq2ZhZUGgNm0jtm0jleOm4WVNquwUkFfwAzfHV6ZhZUtpGwAGa2IjFYEkvW3iQLgcUrwOe3wK3b4FDt8TjOAs90gERFDNhE1iMshYUOnFxs6vdYxs7CytsxkPJGDVjALK0tlwyq6BGIAAFkyCyvnr+8OuFis14rKBpBSi0ipxco1rHE5bPApkhm+nXZ0eGXuZklEqw5DNhEtGr9ih1/xY0tPrbAylslbu1VWt4qvFlbqxTKOzWVxbK5WWOl2mJuy9M4rrmSxXmvL5UvI5UuYTurWMbdsQ7dfQbffiS6/c8Gz3eWygbRetNaUExG1Gv5mIqIlIwoC2r0y2r0yLuw3CytLZQMzKc2a6R6vFFZWcjey+RIOzaRxaKZWWBlw2a0t4vuC5npxmRuytLSsXsLwbAbDsxmIgrlGvyegoMevwO+yn/Q+hmEgmy8hrRWQ1opIawWktCLSWhFZvQij8jNiEwGPbDcLN50SvM7a5y4H/zdHRM3B3z5E1FQ2UTDDVkDBJQgBMAsrp5LmTPdEQsVYLIfovMLKRK6ARK6ANyfNreIFAO1eGf1BBb2VZSbdfic3Y2lRZQOYSemYSel4DQlrljvosiOjFyuBuoiMXkCpfObHK5VxyuJNSRRqnVOc5rrxdq8Mn/PkwZ6IqFEYsomo5TgkEYNtbgy21Qor1XwJE9WOJpXNc1LzCiurrelePZ4AANgEAV3VwsrKMhMWVram6iz3YiiWDeuPMqB+Y6V2j4wOr/kv5HKwWwoRNRRDNhEtC4rDhqGwB0Nhj3UspRXMZSbx2hpvtVACAJQMAxMJcyb8d8fM8x2SiB6/Uhe8gyysXJW0Qnle4a054x1yO9BRWc7U4ZHhkPhOCBG9fwzZRLRs+Zx2+LrtOL/bB8BcwxvL5jGeUCvtBHOYTKgolMzFu/liGSPRLEaitcJKl8NWWdftqiw3UeDlUoJVp1g2rDaTVQGXHe0eGV6nBKfdBqddhCyZH52SjTPfRHRaDNlEtGIIgoA2j4w2j4ztfQEAZmFlJF3bsXI8kcN0slZYmcuXcHgmg8MzteUKfsVeayNYKax0srBy1aktMzk5u02ohG8bZEm0grhityHodnAJCtEqx5BNRCuaTTTXZnf5nbh4jXmsUCpjKqlhPJ6zNtCZy9RmMKtFdG9VCisBoN0jWzPdfUEXuhvQho6Wt0LJQKFUtDZdei9RAIJuB9o9DrS5ZbR5HOf0Lkm5bEAtlJDNF6Hly8jmi8gXy/ArdgTdDvgVvuNC1MoYsolo1bHbRAyEXBgIuaxjWqFaWFlb4z2/W8VcRsdcRsdrYwkAZoDq8jlrs91BBWGvEzbOXFJF2QCimTyimTwA850ShySizeNAeyV0S6KAXN4M0mqlv3guX4JaKEIrlK02hScj2QSEXA6EPA7rI7umELUOhmwiIgBOuw3rOzxY31ErrExXCivH4iomKj28c3mzsLJsAJNJDZNJDS+OmOfbbWY7wvk9vENuBwsryZIvljGV0DCV0Bb8WMXSievI7TazgDPodqCt8tErSw37GSyXDejFMvRiCVqhDFEA3LIEl8PGn3Oi92DIJiI6Ba/Tjk3ddmyaV1gZzxXquplMJlTkK82cCyUDo9EcRqM5AFEAgGK3WTPd/ZWdKznbSIulUDKsHuTz2URAEkVINgF2mwibKMBuE8xjogDJVrlNNG8rlMrQCqVKoDY/1wolq4j4vWwi4HJIZk9yWYJbluCRzf7kHlliz3palRiyiYjOkiCYs4QhtwPbKoWVZcOcTZyYF7ynkxpKlff51UIJ785m8O68PtA+p2TNdPcFXegNKFAcLKykxVMqA6VyGXoRAEqL8vjVTYSmTnK70y7CUwneEAAYZn97wwAMGJWP5h+yBszbAfP1pdht6K5sMMUCZFpOGLKJiBZAFAR0+Zzo8jmxc9A8VqwWViZUK3xH0no1NyClFfH2VApvT9UKK9vcjrqOJt1+hX2aacXQCmVohTzmMvkzn3wSI9EcBAEIuhzoCTjRE1DQxqVY1OIYsomIGkyyiegPudAfcgFoAwDo8wsrK+E7Pq89XDSbRzSbx+/HkwDMwspOnxO989Z3d/pYWEmrl2EAsWwesWweb06kIEsiuv1OznJTy2LIJiJaArLdhnUdHqybV1iZ0Yt1y0zG4zlk5xVWTiU1TCU1vDwaB2DuStgTmLdjZcCFkMfBreJpVdKLZYxEcxiJ5gAAIbc5y93ldyKgOPhOEDXdgkP22rVrT/l2jSiKCAQCuOSSS/Dnf/7nOP/88xf6dEREK4ZHlrCxy4eNXbXCyoRawHjcnOkeqxRW6kWzsLJYNnA8lsPxWM56DKddrJvt7gu64HM2rpsE0XIxf5YbAGRJNAsxnRK8st0swqwUYnLWm5bCgkP25Zdfjv3792NychJ/+Id/iM7OTkxPT+Pf//3f0dPTg/7+fjz11FP4x3/8R+zfvx8XX3xxI8ZNRLTiCIKAoMuBoMuBC3r9AMzCr7m0jvFErX/3VFJDqbJlpVYo40gkiyOR2lbxXlmqdDRxWbPeLgffuKTVRS+WoVt9yuvZbYIZvp12eGQJbtkGh80Gu2R2X7HbRMiSaHViIXo/Fvxb95prrsELL7yA4eFh9Pf3W8ePHz+Oj370o9i1axcee+wxXHHFFbj//vvxb//2bwt9yhVlbbsbhVIZoiBAsgmwCQJsovlv/jFRFFA2DJTKBool82Np3tdlw0CxbKBULpu3V24rlY3K/TDv89rH+bcTUesRBQFhnxNhnxMXDQQBAMVyGTNJHWOVHSvHEznMpmqFlWm9iHem03hnOm09TqhaWBkww3dvgIWVtHoVSgZi2QJi2cIZz7WJsIK3QxJht9WCeP3n9V87Km0RHZWwTqvPgkP2Qw89hD179tQFbAAYGBjAfffdhwceeAB/8id/gjvvvBN/8Rd/sdCnW3HOr/TfbQWlsoFCqWxtNKAXysiXyshX+qTqhVL915VzyqfZkYyIGk8SRfRWem9X6cUSJhOaNds9kVARy9Zm8Kpvpb9RKawUUCmsnLe+u9MvQxIZBojmq7Y/1ArvfzZKFADZLkKWbJClyke7WPtcEutuNwCUKhNmxXK5boKtWK5+LFuTZQYAh02E027e32k3H98p2fjHdBMtOGQPDw/D7/ef9LZgMIiRkREAwJo1a5DL5U56HrUGcwbdVlmrdvabZaj5EjJ6EVm9iGy+iKxeqnwsIqeXUGQKJ1p0smTD2nY31ra7rWNZvVjpaFIrrsyYjZJhAJhOaZhOaXhlXmFlt9+J3qAL/ZUQ3+6RWVhJtEBlA1DzZaj5pX/beH7Ad9pFSKIIURAgirDeKReF6jvomPe5AHMCXrBCfbFkhvxiqWwF/rrjlXfUBQjWxkfVTY/MdwIEa1Mkc6a/dnv945ifF0rlyvPVP0+pXMa1W7uX/L/luVpwyB4cHMRjjz2G//yf//MJtz366KMYGBgAAESjUYRCoYU+HbUgxWGD4rChwyuf9HatUDIDd75U+Vf/uZovcTYcgFs2tyXO6UX+96CGcMsSzuv04rxOLwCzsDJZLayshO+JhGrN0BXLBsYq28i/UHkMWaoWVtaKK/2KnYWVRMtEMwP+arfgkP2Xf/mX+LM/+zOMj4/jxhtvRGdnJ2ZmZvDTn/4Uv/vd7/DII48AAPbt28eix1XKaTdnx9tOc45WMEN3Vi9CrXyuF0oQBAGCYL61/d7PRQEQUDlW+by6cxhgFoxVdxGrfg4YKBuVXcaM6ue1NeyGUVu7Xq7cXv06XyyfckvhsyUIZkcJv2KHT7HDX/nnc9ZvO6zmzXcDcpV3Bap/kGR18+NC3rak1UsQBARcDgRcDmydV1gZzeTN2e6EiolKR5PqO1B6sYyjc1kcnasVVrplyZrp7guYwdsts7CSiGi+Bf9W/MIXvgDDMLBnzx586Utfso53dXXhf/7P/4nPfe5zAICvfvWrkOWTz3QSVYN4yO1o9lBOq1w2kC+VoRcqa9Ira9O1grlOPV+sHS+VDficdvgUyQrTXqf9rCrVq+8OwHPy20tlA7l8ERm9iFjWrJ6PZnXOVNA5EwUBHV4ZHV4ZOyqFlaWygZmUZvXunkiomElp1jssWb2Ig9NpHJxXWBl02a2Z7t6ggt6AAllimzQiWr0aMvXwxS9+EV/4whdw6NAhRKNRtLW1YePGjXVvJ3Z2djbiqYiaShQFON/HuvVGs4kCvE4ztHf7a8VvWb2IaCaPuayOaCaPeDbPNfF0zmyVTW96Ago+sNZc5pcvljGVrG2aMx5XEZ1XWBnPFRDPJXFgolZY2eGV5/XvVtDlc9a9Y0NEtJI17P09QRCwadOmRj0cEb0PblmCW5Yw0OYCYM68J9QCohkdc5XZ7oy28DXfkk2wesg6rLZWIgzDQKay/l4tlKylO7T8OSQRg21uDLbVCivVfAnjCbON4FhlA52UViusnE3rmE3rePW4WVhpqxZWBhT0B13oDSro8LKwkohWpoaE7JGREXzzm9/Evn37rJnsK6+8El/5ylewdu3aRjwFEb0Poigg5HYg5HZgw7w3k8rzq8LnVW6XrM/NNer2eT1eHVIlUNtEiGex5KVcNqz15JlKpxkzgJvLXFjwuvwpDhs2hL3YEPZax1KVwsrqGu/xeM6qISiVDavLye+OxQCY4b03UO3fbYbvgIuFlUS0/C04ZL/++uu48sorkcvl8KEPfQg7d+7E9PQ0HnvsMfzkJz/B888/jwsvvLABQyWiRhFFAQ5RgAOL99a9OG9Jy8kWixmGgVy+ZK4pz+YRqyxxWWhxKTWXT7Fjs2LH5p7aVvGxbN6a6R6Pq5hMqtZ1zhfLODaXxbF5hZUuh62um0lf0AUPCyuJaJlZ8G+tv/iLv0BHRwd+85vfWO36AGB0dBRXX3017rzzTuzbt2+hT0NEK4wgCNbylv6QyzqeVAuVjVPMJS6JXJ47ki5jgiCgzSOjzSPjwv4AAHNGezatWbPaE/EcpucVVubyJRyeyeDwTMZ6nIBit2a6q4WVZm0EEVFrWnDIfvHFF/G//tf/qgvYgNk/e8+ePfj85z+/0KcgolWk2omluqnK/HXl0awZulNaEUXOeC9b5tpsBd1+BZesMY8VSmVMJdTKEhPz31xGt+6TUAtIqAW8NZkCYBZWtntkq6iyL+hCl9/J7auJqGUsOGT7/f5T7vgYCATg87XOtuFEtPzUrSufd1zNl5DWCkhpRaS0AjLzPnKt9/Jjt4kYaHNj4D2FlROJyjKTSvhOqgUAZmFlJKMjktHx2lgCgLl7Xae/0tEkYAbvsI+FlUTUHAsO2bfccgt+/OMf42Mf+9gJt/3oRz/CzTffvNCnICI6QbWXePg9f8dXO5ykK6E7mSsgqZphPF/kupPlRHHYMBT2YChcaxif1grWTHe1laBaKAEASoaByYSGyYSGFyvn221mO8LqMpO+gIKQ28HCSiJadAsO2RdddBGefPJJfOADH8DNN9+Mrq4uTE9P41/+5V8wOzuLG2+8EU899ZR1/vXXX7/QpyQiOiVBqBVc9kCpu03Nl8zgrVaCd+Ujd9BcPrxOO87vtuP87lphZTxXsAJ3dfOcamFloWRgNJrDaDRnPYZit9UtM+kNKvA5m9f3nohWpgWH7D/+4z8GAIyNjeHll18+6e1GpVmuIAgolUoLfUoiovelOvvd6XPWHdeLJSTVArJ6CVqhspNn5aM27yM7n7QeQagtJ9rWFwBgbhU/m9YxHqttFT+VVK1lRGqhhHdnM3h3tlZY6VfsZivBeV1NWFhJRAux4JDNziFEtNzJkg1hrw3wnv68ctkwA3ixFrzVQgm5fAla3vyYK5ifc6fN5hEFAV0+J7p8TlxcOVYolTGd1KyZ7rG4irm0jupVqr678fZUynqcdo+j1kYwoKA7oLCwkojO2oJD9uWXX96IcRARtTxRFKzZ8DPJF8tQ8yXkCubGO7m8OUue0gqIpHW2JVxidpuI/pCrrl2kVqgWVtbWdycqhZUAMJfJYy6Tx+uVwkpRADp9zrqt4sNeJ2xnsTkTEa0+Denu//Of/xyPP/44RkdHoWla3W2CIOD3v/99I56GiGjZcEjmLpl+nLjWt1gqYyatYzqpYjKhIV3ZipyWltNuw/oOD9Z31AorM3rRCtzV8J3Nm8scywYwldQwldTw0oh5vt1mtiPsDyrorYTvNhZWEhEaELK/9a1v4e6770ZHRweGhobgdrvPfCciolVMsplbifcGFOwcNINdNXDPpDSu/W4ijyxhU5cPm7pqhZWJXMHaIn48rmIioVqdagolA8djORyP5QBEAQBOu4i+QG22uzfogl9hYSXRarPgkP3www/js5/9LH74wx/CZmORCBHRufLIEobCXgyFvSiXDcxldUwlzBnTWDbf7OGtaoIgIOh2IOh24IJec0+IsmEgktbNme6EGbynkhpKlXX4WqGM4UgGw5FaYaXXKdWt7+4NKnA5uFU80Uq24Fd4NBrFLbfc0rCAnU6n8cADD+D111/Ha6+9hrm5Odx///3Ys2dP3Xmf+cxn8L//9/8+4f4bN27EwYMHGzIWIqKlJooCwl4nwl4ntveb64ZTagHpSu/vjFZERi8grRU5490koiCg0+dEp8+JiwaDAIBiuVpYWevhHZlXWJnWinhnKoV35hVWhtyOWjeTgIKegAKHxMJKopViwSH7D//wD/HOO+/gqquuasR4EI1G8cgjj2D79u3YtWsXfvzjH5/yXEVR8Nxzz51wjIhopXDabXDabQif5Da9WJoXvItI5AqYSWnQuenOkpNEsTJTXSus1IslTCa0uh7e8VytsDKWzSOWzeON8SQAs7Ay7HVWlpiYG+h0+lhYSbRcLThkf+9738Pu3bvR39+Pa6+9Fg6HY0GPNzg4iHg8DkEQMDc3d9qQLYoiLr300gU9HxHRciVLNsgeG9o9snXMMAzEsnlMJTVMJzXMZXRuM98ksmTD2nY31rbXapWyetEM3IlcpbBSRUY3C1/LBjCd0jCd0vDyaBwAIIkCuv3zO5q40OZxcKt4omVgwSF7aGgIH/nIR7B7924IggCXy1V3uyAISCaTZ/14rMgmInr/BEFAm0dGm0fG1l4/CqUyZiudTKaSGlIqO5k0k1uWsLHLi41dZlN2wzCQVOdtFV8J39V3I4plA2Nxs693ldMu1raKr2yg41fs/P8nUYtZcMi+66678A//8A+48MILcf755y94JvtcqKqKrq4uRCIRdHd3Y9euXfj617+OUCi0ZGMgImpl9nmdTABzJnU6Zc5yTye5tKTZBEFAwOVAwOXA1nmFlXMZs7ByLK5iIp7DVFKzNjjSCmUcjWRxNJK1HscjS3VbxfcFFLhkFlYSNdOCX4GPPfYY7r77bnzzm99sxHjO2vbt27F9+3Zs3boVALB//35897vfxW9/+1u89NJL8Hg8p7zv7OwsIpFI3bHh4eFFHS8RUStwy1Jdb+hkroBIRkckrWMuo7NndwsQhVrx646BWmHlTEqv6+E9k9KswsqMXsTB6TQOTqetxwm5HXVbxfeysJJoSS04ZJdKJVx99dWNGMs5ufPOO+u+vvrqq7Fjxw7ccMMN+NGPfnTC7fM9/PDD2Lt372IPkYio5flddvhddgyFzdCtFUqYy+iYy+QRSeuIZ/PcIr4FSGLtHYkPrjWP5Yvlyo6VuUofb7Wu5WO1sPLAhLlkUwAQ9slmD++Qgr6AC51+GZLI4E20GBYcsj/60Y/ihRdeaFh3kYXYvXs33G43XnjhhdOed+utt+LGG2+sOzY8PIxdu3Yt4uiIiFqf026r65JRLhuI5fJm8E7nEcloUPNcYtIKHJJ4QmFlTi9agXuiMuudrhRWGgBmUjpmUjpeOV4rrOyaX1gZUNDulVlYSdQACw7Z9957L2666Sa43W5cd911J10PvZRrpA3DgHiGv8rD4TDC4ZM1xCIiovlEUUC7RzY7mHSZx5Kq2SpwOskdKluNS5ZwXqcX53XWCitTWrGujeBEQoVWqBVWVosuq2RJtJaZVLeKD7CwkuicLThkb9++HQDwpS99CV/60pdOek6pVFro05yVJ598Erlcjm39iIgWkV+xw6/YcV6nF4ZhIJrNYyZlBu5IWkeJE90tQxCEyvXyY0tPrbAylsljPJGrFFaqmEyo1rIgvVjG0bksjs7VCivdsoQ+a323Gb49LKwkOq0Fv0Luu+++hv91+8tf/hLZbBbptFnA8fbbb+PJJ58EAHzsYx9DJBLBLbfcgj/6oz/C0NAQBEHA/v378b3vfQ9btmzB5z//+YaOh4iITk4QajPdW3r8KJXNzhhTlVnuWDYPgxPdLUUUBLR7ZbR7ZVzYbxZWlsoGZlJa3Wz3TEqzeqxn9SIOzaRxaKZWWBl02c2Z7oCCvpCCXr8C2d6Y3Z+JVgLBMFrv19+aNWswOjp60tuOHTsGv9+Pz33uc3jttdcwMzODUqmEwcFB7N69G/fccw/8fv85P+dbb72FrVu34s0338SWLVsW+i0QERHM4rxjc1kcnE4hqy/Nu5rUGPliGVPJ2jbx43EV0XmFle8lAOjwytZMd39QQZfPCcnGwkpqvFs+OLDkz3muWbEl3+sZGRk54zlPPfXU4g+EiIgWxCGJ2NjlxYawB8djORycTiGWLZz5jtR0DknEYJsbg221wko1X6rbrXI8nkNKqxVWzqZ1zKZ1vHo8AQCwCdXCytoykzALK2mVaMmQTUREK4soCljT7saadjemkxremU5hKqE1e1h0jhSHDRvCXmwIe61jqeqOlfPCt1ow37UoGYbZZjCh4nfHzPMdNnPHyvmb5wRdLKyklYchm4iIllSX34kuvxOJXB7vTKUxGs2CrbiXL59ix2bFjs09PgBmR5NYNl+3zGQyqVpdaPKlMkaiWYxEa4WVLofNnOkOuKzw7XXam/L9EDUKQzYRETVFwOXAH6xvw/Z+Pw5Om2E7XyyzO8kyJwgC2jwy2jwytvcHAJiFlbNprW6ZyfS8wspcvoTDMxkcnslYj+NX7Fbv7r6QuWOlk4WVtIwwZBMRUVO5HBIuGgjiosoW4uWygWLZQLFcNj+WKp+X5n1eNlAolTGb1jGd1NjBpMXZRAHdfgXdfgUXrzGPFUplTCW1eT28VcxldOs+SbWApFrAW5Mp61i7R0Z/UEFvZZlJt98JOwsrqUUxZBMRUUsRRQEOUYADZw5PW2AW441EsxiZyyKeY1HlcmG3iRgIuTAQclnHtEIJEwkV47HaVvFJtXZN5zI65jI6XhtLAABEwVx+1GctM3GhwyvDJnJ9NzUfQzYRES1risOG87t9OL/bh0Quj6NzWYxGs9z+fRly2m1Y3+HB+g6PdSytFayZ7omEOeudy5uFlWUDmExomExoeHHEPN9uE8zCysoyk76AgpDbwcJKWnIM2UREtGIEXA5cNODAjv4AplMajkWyGI/XdjOk5cfrtOP8bjvO764VVsZzhbplJpMJFfnKYv5CycBoNIfRaA44EgUAKPZKYWVQQX/Qhd6gAh8LK2mRMWQTEdGKIwi1NcCFUhljsRyOzWUxm9a5fnuZEwQBIbcDIbcD2/oCAMyt4mfTOibmBe/ppIZS5WKrhRLenc3g3dlaYaXPKaEv6LLCd1/ABcXBwkpqHIZsIiJa0ew2Ees6PFjX4UFGL+LgVApHI1nObq8goiCgy+dEl8+JnYPmsWK1sDKhWuE7ktZRveoprYi3p1J4e6pWWNnmdlhru/uCCnoCCgsr6X1jyCYiolXDI0u4eE0IW3v9ODyTxuGZDPJFrt1eiSSbiP6QC/0hF4A2AIBeLayMq1b4nl8sG83mEc3m8fvxJACzsLLT50RvoLbMpNPnZGElnRWGbCIiWnWcdhu29QWwuduH4UgGh6bTyOqlZg+LFplst1nvalRl9GLdMpPxeA7ZeYWVU0kNU0kNL4/GAQCSKNTvWBlwIeRxcKt4OgFDNhERrVqSTcSmLh/OC3sxEs3inal0Xcs4Wvk8soSNXT5s7KoVViYqW8VPxHMYqxRW6pV3PIplA8djORyP5azHcNpF9AVclf7d5nITv8LCytWOIZuIiFY9URSsGc6JhIq3J1OIpPUz35FWHEEQEHQ5EHQ5cEGvH4BZWDmX1ivLTMxZ76mkhlJlXb9WKGM4ksFwpFZY6XVK6Aso6A3Wtop3ORi7VhNebSIionl6Awp6AwoiaR1vT6UwmVDZkWSVEwUBYZ8TYZ8TFw2aO5MWy2VMJ7XKjLcZvmdTtcLKtFbEO9NpvDOdth4nVC2sDJiz3T0BBQ6JhZUrFUM2ERHRSXR4ZVzu7YCaL2E0lsVoNIdoJt/sYVGLkESx0oWktmOlXixhMjF/q/j6wspYNo9YNo83KoWVAiqFlfOWmXSxsHLFYMgmIiI6DcVhw6YuHzZ1+ZDRixiNmoE7wS3c6T1kyYa17W6sbXdbx7J6sdLRpFZcmdGLAAADwHRKw3RKwyvzCiu7/U70Bl3or/TwbvfILKxchhiyiYiIzpJHlrClx48tPX4k1YIVuNNasdlDoxblliWc1+nFeZ1eAGZhZVKtbRU/nshhIl5fWDkWVzEWV/FC5TFkSURvoNa/uy+owK/YuVV8i2PIJiIieh/8ih3b+gLY1hdALJvHaDSL47EcWwHSaQmCgIDLgYDLga3zCiujmXzdMpOppGZtmKQXyzg6l8XRuaz1OG5Zsma6+wJm+HbLjHWthFeDiIhogarbfO8YCGI2peGd6TQm4mqzh0XLhCgI6PDK6PDK2DFgFlaWygamU+b67onKrPdMSrMKK7N6EQen0zg4r7Ay6LLXbRXfG1AgS9wqvlkYsomIiBqo2oUimSvgrakkjkdz4A7udK5somB1usFa81i+WMZkwtytshq+o9laMW48V0A8l8SBiVphZYdXrltm0uV3QhLZ0WQpMGQTEREtAr/Ljg+tb8f2viIOTqdwZDZrvf1P9H44JBFr2t1YM6+wMpcvVloI1nasrNYIGABm0zpm0zpePW4WVtqqhZXz1nh3eFlYuRgYsomIiBaRW5awczCELT1+HJ5J4/BMBvlKkRvRQrkcEjZ0erGhUlgJAEm1UL9VfCIHrWD+zJXKhnX8d8diAMzw3huotRHsCygIuFhYuVAM2UREREvAabdhW18Am7t9GI5kcHAqjVyeRZLUeH7FDr/ix+aeWmFlLJuvayM4lVRRKJnvrOSLZRyby+LY/MJKhw19wfqt4j0srDwn/K9FRES0hCSbiE1dPpwX9iKazSORMzcoiecKSKp5lDjJTQ0mCgLaPTLaPTIu7K8VVs6mNWuJSbWwsrqiKZsv4dBMGodmaoWVAZfd2q2yWljptLOw8lQYsomIiJpAFGsdJarKZQMprYB4roBYJYDHcwUuL6GGM9dmK+j2K7hkTQgAUCiVMZWoX989N2+X00SugESugDcnUwDMwsp2r1wJ3pUdK/1O2G0srAQYsomIiFqGKNZ6KM/fNTCjFxHP5jGX0XEkkmXopkVht4kYaHNjoK32s6fmS3U7Vk4kVCRVc7dTA0AkrSOS1vHaWAIAYBMEdPnNreLNPt4uhFdpYSVDNhERUYvzyBI8soT+kAtbe80CykPTaauYjWixKA4bhsIeDIU91rGUVqj07q6t8VYLZn1ByTAwkTDD+IvHzPMdNhE9Aee8VoIuBFdBYSVDNhER0TJit4nY0uPHxk4vjkSyeGcqxQJKWlI+px2+bjvO7/YBMLeKj2XzGE+oVvieSMwrrCyVMRLNYSSasx7D5bCZm+YEaj28vU57U76fxcKQTUREtAxJNhEbu7zYEPbg6FwWb0+lkKn0RyZaSoIgoM0jo80jY3tfAIBZWBlJ6+Zsd2W5yXSyVliZy5dweCaDwzMZ63H8it0M3AFzmUlfcHkXVjJkExERLWOiKGAo7MH6DjdGozm8NZmy1swSNYtNNNdmd/mduLhyrFAqYzqp1S0zmcvo1lbxSbWApFrAW5XCSgBo98jWTHdfQEF3QFk2hZUM2URERCuAIAjWboBjsRzemkwilmXYptZht4noD7nQH3JZx7SCWVg5EVcxVtkqPjHvj8S5jI65jI7XK4WVogB0+py4bEN73eO0IoZsIiKiFaYaZCYTKg7NpDGd1GBwR3dqQU67Des7PFjfUSusTGuFSkeTWnFlte6gbAAzKa2u9WWrYsgmIiJaoXoCCnoCCtR8CaOxLI5FsojnOLtNrc3rtGNTlx2bumqFlfFcwSyojKvQiqVlsVabIZuIiGiFUxw2bOryYVOXD4lcHsfmshiJZqHm2QKQWp8gCAi5HQi5HdhWKaxcDhiyiYiIVpGAy4EdAw5c2B/AdErDsbksxmMqimWuJyFqJIZsIiKiVUgQattqF9aUMRbLYSSaxUxK5/ptogZgyCYiIlrl7DYR6zo8WNfhgZovYSalIZLRMZvS2Q6Q6H1iyCYiIiKL4rBZrQABs8VaJK0jktERSeuIZ/PgyhKiM2PIJiIiolNy2m11vY2LpTLmMnnMpjUcj+WQUrnLJNHJMGQTERHRWZNsorWT37a+QGXjmxRi2Xyzh0bUUhiyiYiI6H2rznJPJzW8PZXEdFJv9pCIWgJDNhERES1YdXY7mtHx9lQK43GVXUpoVWPIJiIiooZp88i4bEMHkmoB70ylMDKXZaEkrUpiswdAREREK49fsePSdW345IU92NjlhSQKzR4S0ZLiTDYREREtGpdDws7BILb0+DA8m8GxuSzSGjuS0MrHkE1ERESLzmm3YWuvH1t7/Yhl8xiNZnE8lkNWLzV7aESLgiGbiIiIllTI7UDI7cCOgSAiaR3HY2bgVvPlZg+NqGEYsomIiKhpOrwyOrwyLhoIYjatYzSaw1gsB73IwE3LG0M2ERERNZ0gCOj0OdHpc+LiwSCmU5oVuItsT0LLEEM2ERERtRRRFNATUNATULCtz4+XRmKYTGjNHhbROWm5Fn7pdBp33XUXPvrRj6KjowOCIGDPnj0nPffVV1/FRz7yEXg8HgQCAVx//fU4evTo0g6YiIiIFo1blnDFxjA+tL4NstRysYXolFrupzUajeKRRx6BruvYtWvXKc87ePAgrrjiCuTzefz0pz/Fo48+isOHD+Oyyy5DJBJZugETERHRolvT7sZ127qxtt3d7KEQnZWWWy4yODiIeDwOQRAwNzeHH//4xyc977777oMsy3j66afh8/kAADt37sSGDRvw7W9/G3/zN3+zlMMmIiKiRea02/AH69uwpt2FF4/F2P6PWlrLzWQLggBBOP2uUMViEU8//TQ+/elPWwEbMAP6lVdeiZ/97GeLPUwiIiJqkm6/gusu6Mambi/OEBmImqblZrLPxpEjR6CqKrZt23bCbdu2bcOvf/1raJoGp9N50vvPzs6esKRkeHh4UcZKREREjSfZRFw0EMSaNjd+dzSKeK7Q7CER1VmWITsajQIAQqHQCbeFQiEYhoF4PI7u7u6T3v/hhx/G3r17F3WMREREtPhCbgeu2dKFd6ZTeHMiiRLba1OLWJYhu+p0y0pOd9utt96KG2+8se7Y8PDwaQstiYiIqDWJooAtPX4MhMy12jMpvdlDIlqeIbutrQ1AbUZ7vlgsBkEQEAgETnn/cDiMcDi8WMMjIiKiJvA67fjw+Z2YTWl4ayqFKfbWpiZaliF7/fr1UBQFBw4cOOG2AwcOYGho6JTrsYmIiGhlC/ucCPucSOTyeHsyheOxHLhpJC21lusucjYkScInPvEJPPXUU0in09bx48ePY9++fbj++uubODoiIiJqBQGXAx8aasfHt/fgvE4PJJGtSGjptORM9i9/+Utks1krQL/99tt48sknAQAf+9jH4HK5sHfvXlxyySX4+Mc/jq985SvQNA333Xcf2tvb8eUvf7mZwyciIqIW4pElXLwmhK29fhyeSePwTAb5IiskaXG1ZMj+b//tv2F0dNT6+oknnsATTzwBADh27BjWrFmDTZs24fnnn8fdd9+NG264AZIk4aqrrsK3v/1tdHR0NGvoRERE1KKcdhu29QWwuduH4UgGh6bT3NCGFk1LhuyRkZGzOm/nzp34zW9+s7iDISIiohVFsonY1OXDeWEvRqJZHJxOI8E+29RgLRmyiYiIiBabKApY1+HBug4PImkdw7MZHI9l2WubGoIhm4iIiFa9Dq+MDq+MiwYDODaXxfBsBim12Oxh0TLGkE1ERERUIUs2bOryYVOXD7MpDe/OZjDGFoD0PjBkExEREZ1Etd+2VijhaCSL4UgGGY2z23R2GLKJiIiITsNpt2Fzjw+be3yYSqoYns1gIq5ydptOiyGbiIiI6Cx1+xV0+xWo+RKORDI4EsmwDSCdFEM2ERER0TlSHDZs7fVjS48Pk0kN786kMZXUYHB2myoYsomIiIjeJ0EQ0BtQ0BtQkNWL1uy2mmcfwNWOIZuIiIioAdyyhG19AWzt8WMiYa7dnkpqzR4WNQlDNhEREVEDiaKA/pAL/SEX0loBw7MZHI1koRc5u72aMGQTERERLRKv044dA0Fs6wtgLJbDu7MZRNJ6s4dFS4Ahm4iIiGiR2UQBa9rdWNPuRiKXx7uzGYzMZVEosVJypWLIJiIiIlpCAZcDl6wJ4cL+AEaj5hbusWyh2cOiBmPIJiIiImoCu03EUNiLobAXcxkd786YW7gXucvNisCQTURERNRk7R4Z7R4ZFw0GMBZTcTyWxUxKZ9/tZYwhm4iIiKhFyJINQ2EPhsIeaIUSxmI5jEZziGQYuJcbhmwiIiKiFuS027Ch04sNnV6o+RKOx3IYjWYxl8k3e2h0FhiyiYiIiFqc4rBhY5cXG7u8yOrFSuDOIZZl4G5VDNlEREREy4hblnB+tw/nd/uQ0YsYmcvi3dk0t3JvMQzZRERERMuUR5awtdePzd0+jMVzODSd5nKSFsGQTURERLTMiaKAwTY3BtvciGZ0HJpJ43g0B3YDbB6GbCIiIqIVpM0j40MeGRcNlPDuTAbDES4laQaGbCIiIqIVyGm34YI+P7b0+DAaM5eSsFBy6TBkExEREa1goihgbbsba9vdiGXzOBLJYGQui0KJa0kWE0M2ERER0SoRcjsQcoewoz+A47EcjkSyiKT1Zg9rRWLIJiIiIlplJJuIdR0erOvwIKkWcCSSwbFIFnqRa7cbhSGbiIiIaBXzK3ZcNBDEhX0BjMdVDEfSmE5ydnuhGLKJiIiICKIoYKDNhYE2FzJ6EUdmMzg2l0UuX2r20JYlhmwiIiIiquORJWzvD2B7fwDxbB4TCRUTCRWxbB4G6yXPCkM2EREREZ1S0O1A0O3A1l4/tEIJU0kNkwkVkwmVHUpOgyGbiIiIiM6K026z2gGWywbmMjomEiomExqSaqHZw2spDNlEREREdM5EUUDY50TY58SOASCjFzGZUHE0kkEsy8DNkE1EREREC+aRJZzX6cV5nV7MpjUcns5gLJ5btWu4GbKJiIiIqKHCXifCXieyehGHZ9I4Eskiv8p6cDNkExEREdGicMsSdgwEcUGvH8fmsjg0k0ZKLTZ7WEuCIZuIiIiIFpVkE7Gh04sNnV5MJVUcnE5jKqE1e1iLiiGbiIiIiJZMt19Bt19BUi3g8EwaxyJZFMsrb+E2QzYRERERLTm/Yscla0K4sD+AqYSG47EcJpMqiiuk9zZDNhERERE1jd0mWtu5l8oGJhMqxuI5TMSX92Y3DNlERERE1BJsooD+kAv9IRfKZQPTKQ1jsRzG4yr0ZdadhCGbiIiIiFqOKAroCSjoCSj4gGFgNq1bgXs5YMgmIiIiopYmCAI6fU50+py4eE2zR3N2xGYPgIiIiIhopWHIJiIiIiJqMIZsIiIiIqIGY8gmIiIiImowhmwiIiIiogZjyCYiIiIiajCGbCIiIiKiBmPIJiIiIiJqMIZsIiIiIqIGY8gmIiIiImowhmwiIiIiogaTmj2AVqHrOgBgeHi4ySMhIiIiolZTzYjVzHgmDNkVY2NjAIBdu3Y1dyBERERE1LLGxsZw0UUXnfE8wTAMYwnG0/ISiQT279+P/v5+yLK8KM8xPDyMXbt24ec//zmGhoYW5TloYXiNWhuvT+vjNWptvD6tj9eodem6jrGxMVx++eUIBAJnPJ8z2RWBQACf+tSnluS5hoaGsGXLliV5Lnp/eI1aG69P6+M1am28Pq2P16g1nc0MdhULH4mIiIiIGowhm4iIiIiowRiyiYiIiIgajCF7CXV0dOD+++9HR0dHs4dCp8Br1Np4fVofr1Fr4/VpfbxGKwe7ixARERERNRhnsomIiIiIGowhm4iIiIiowRiyiYiIiIgajCGbiIiIiKjBGLIb6Pnnn4cgCCf998ILL9Sd++qrr+IjH/kIPB4PAoEArr/+ehw9erRJI1950uk07rrrLnz0ox9FR0cHBEHAnj17TnruuVyL73//+9i0aRNkWcbatWuxd+9eFAqFRfxOVq6zvUaf+cxnTvqa2rRp00kfl9eoMZ577jl89rOfxaZNm+B2u9Hb24tPfepTeOWVV044l6+h5jjba8TXUHO8/vrruO666zAwMABFURAKhfAHf/AH+Od//ucTzuVraGViyF4E3/jGN/Af//Efdf+2bt1q3X7w4EFcccUVyOfz+OlPf4pHH30Uhw8fxmWXXYZIJNLEka8c0WgUjzzyCHRdx65du0553rlci4ceegh33HEHrr/+evzqV7/Crbfeim984xu47bbbFvm7WZnO9hoBgKIoJ7ymfvKTn5xwHq9R4/zgBz/AyMgI7rjjDjzzzDP4u7/7O8zOzuLSSy/Fc889Z53H11DznO01AvgaaoZEIoH+/n584xvfwDPPPIN//Md/xJo1a/DHf/zHePDBB63z+BpawQxqmH379hkAjCeeeOK05914441Ge3u7kUwmrWMjIyOG3W437rrrrsUe5qpQLpeNcrlsGIZhRCIRA4Bx//33n3De2V6Lubk5w+l0Gl/84hfr7v/QQw8ZgiAYb7311uJ8IyvY2V6jP/mTPzHcbvcZH4/XqLFmZmZOOJZOp43Ozk7jwx/+sHWMr6HmOdtrxNdQa/ngBz9o9Pf3W1/zNbRycSZ7iRWLRTz99NP49Kc/DZ/PZx0fHBzElVdeiZ/97GdNHN3KUX079HTO5Vo8++yz0DQNf/qnf1r3GH/6p38KwzDw85//vKHjXw3O5hqdC16jxgqHwycc83g82Lx5M8bGxgDwNdRsZ3ONzgWv0dJob2+HJEkA+Bpa6RiyF8Ftt90GSZLg8/lwzTXX4P/+3/9r3XbkyBGoqopt27adcL9t27ZheHgYmqYt5XBXrXO5Fm+++SYA4IILLqg7r7u7G+3t7dbttDhUVUVXVxdsNhv6+vpw++23IxaL1Z3Da7T4kskkXn31VWzZsgUAX0Ot6L3XqIqvoeYpl8soFouIRCJ4+OGH8atf/Qp33303AL6GVjqp2QNYSfx+P+644w5cccUVaGtrw/DwML71rW/hiiuuwL/927/hmmuuQTQaBQCEQqET7h8KhWAYBuLxOLq7u5d6+KvOuVyLaDQKWZbhdrtPem71sajxtm/fju3bt1t1Dfv378d3v/td/Pa3v8VLL70Ej8cDALxGS+C2225DNpvFV7/6VQB8DbWi914jgK+hZrv11lvxwx/+EADgcDjw93//9/izP/szAHwNrXQM2Q20Y8cO7Nixw/r6sssuw+7du3HBBRfgrrvuwjXXXGPddrq3yRv5Fjqd2dleC16z5rjzzjvrvr766quxY8cO3HDDDfjRj35Udzuv0eK599578fjjj+P73/8+du7cWXcbX0Ot4VTXiK+h5rrnnnvw+c9/HrOzs/jFL36B22+/HdlsFn/5l39pncPX0MrE5SKLLBAI4OMf/zjeeOMNqKqKtrY2ADjpX5yxWAyCICAQCCzxKFenc7kWbW1t0DQNuVzupOeebBaCFs/u3bvhdrvrWmPyGi2evXv34sEHH8RDDz2E22+/3TrO11DrONU1OhW+hpbOwMAALr74YnzsYx/DD37wA3zxi1/EX//1XyMSifA1tMIxZC8BwzAAmH9lrl+/Hoqi4MCBAyecd+DAAQwNDcHpdC71EFelc7kW1TVw7z13enoac3NzdS0aaWkYhgFRrP0K4zVaHHv37sWePXuwZ88e3HPPPXW38TXUGk53jU6Hr6Hm+MAHPoBisYijR4/yNbTCMWQvsng8jqeffhoXXnghnE4nJEnCJz7xCTz11FNIp9PWecePH8e+fftw/fXXN3G0q8u5XItrr70WTqcTjz32WN1jPPbYYxAE4Yx9nqmxnnzySeRyOVx66aXWMV6jxnvggQewZ88efO1rX8P9999/wu18DTXfma7RqfA11Dz79u2DKIpYt24dX0MrXXM6B65MN998s3H33XcbTzzxhLFv3z7jkUceMTZu3GhIkmT8+te/ts575513DI/HY/yn//SfjGeeecZ46qmnjK1btxo9PT3G7OxsE7+DleWZZ54xnnjiCePRRx81ABg33nij8cQTTxhPPPGEkc1mDcM4t2vx4IMPGoIgGPfcc4/x/PPPG9/61rcMWZaNL3zhC8349laEM12jkZER40Mf+pDx93//98Yzzzxj/PKXvzS+8pWvGE6n09iyZYuRyWTqHo/XqHG+/e1vGwCMa6+91viP//iPE/5V8TXUPGdzjfgaap4vfOELxpe//GXjJz/5ifH8888bTz75pHHTTTcZAIy/+qu/ss7ja2jlYshuoG9+85vGhRdeaPj9fsNmsxkdHR3G7t27jRdffPGEc19++WXjwx/+sOFyuQyfz2fs2rXLGB4ebsKoV67BwUEDwEn/HTt2zDrvXK7F3/3d3xnnnXee4XA4jIGBAeP+++838vn8En1HK8+ZrlEsFjN2795trFmzxlAUxXA4HMaGDRuMu+66y0gkEid9TF6jxrj88stPeW3eOz/D11BznM014muoeR599FHjsssuM9rb2w1JkoxAIGBcfvnlxj/90z+dcC5fQyuTYBiVBcNERERERNQQXJNNRERERNRgDNlERERERA3GkE1ERERE1GAM2UREREREDcaQTURERETUYAzZREREREQNxpBNRERERNRgDNlERERERA3GkE1ERERE1GAM2UREq9CePXsgCALm5uaaPRQiohWJIZuIiIiIqMEYsomIiIiIGowhm4hoFZuZmcHNN98Mv9+Pzs5OfPazn0UymbRuFwQBt99+O374wx/ivPPOgyzL2Lx5M/7P//k/TRw1EVHrk5o9ACIiap5Pf/rTuOmmm/C5z30OBw4cwF//9V8DAB599FHrnH/913/Fvn378PWvfx1utxsPP/wwbr75ZkiShBtuuKFZQyciammCYRhGswdBRERLa8+ePdi7dy/+9m//Fn/1V39lHb/tttvw6KOPIpfLQRAECIIARVFw7NgxdHZ2AgBKpRK2bt2KYrGId999t1nfAhFRS+NyESKiVeyTn/xk3dfbtm2DpmmYnZ21jn34wx+2AjYA2Gw23HTTTRgeHsb4+PiSjZWIaDlhyCYiWsXa2trqvpZlGQCgqqp1rKur64T7VY9Fo9FFHB0R0fLFkE1ERKc1PT19ymPvDelERGRiyCYiotP67W9/i5mZGevrUqmEn/zkJ1i/fj36+vqaODIiotbF7iJERHRa7e3tuOqqq3Dvvfda3UUOHjzINn5ERKfBkE1ERKf1yU9+Elu2bMHXvvY1HD9+HOvXr8fjjz+Om266qdlDIyJqWWzhR0REpyQIAm677Tb8wz/8Q7OHQkS0rHBNNhERERFRgzFkExERERE1GNdkExHRKXFFIRHR+8OZbCIiIiKiBmPIJiIiIiJqMIZsIiIiIqIGY8gmIiIiImowhmwiIiIiogZjyCYiIiIiajCGbCIiIiKiBmPIJiIiIiJqMIZsIiIiIqIGY8gmIiIiImqw/w8iaqNfZXDkmAAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n", - "plot_predictions(model, idata, \"hp\", ax=ax);" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The plot above shows that as `hp` increases, the mean `mpg` decreases. As stated above, this insight was obtained by creating the reference grid and then using the fitted model to compute the predicted values of the model parameter, in this example `mpg`, at each value of the reference grid.\n", - "\n", - "By default, `plot_predictions` uses the highest density interval (HDI) of the posterior distribution to compute the credible interval of the conditional adjusted predictions. The HDI is a Bayesian analog to the frequentist confidence interval. The HDI is the shortest interval that contains a specified probability of the posterior distribution. By default, `plot_predictions` uses the 94% HDI.\n", - "\n", - "`plot_predictions` uses the posterior distribution by default to visualize some mean outcome parameter . However, the posterior predictive distribution can also be plotted by specifying `pps=True` where `pps` stands for posterior predictive samples of the response variable." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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KQMgjo7nc6qs74sXKsAcBt3ORfioiWu7GskXsORhHxCdjc2cQnSGGbSJafhiy64zkENERcqPjhD9KWU1HIqtVFpAYy9n3iWwRRWPqYie760AR47kiDo9m8f8dSQAAmjxOrIp4sTLixcqIBy1+BaLAGW8iWjiJbBF7D8XhVRzoDnuwKuJFk1eu9bCIiBYFQ/YS4VMk+BQJKyPeGdsty0JW0yvhO5Gze+MmskXEMxqMci8ve+Y7idcGkgAAt3Pyj54H3REvVjS54TyLxS2IiM5UTjNwYDiDA8MZBNxS+R/8nkpvbyKiRsSQvcQJggC/ywm/y4nVzTMDeMmwF7DoS+TQl8ijbzxXafFVKBk4NJrBodEMAMAhCugMubGiyY2OoBvtIReifhfru4moqtIFHW8OpvDmYAoRn4xVES+6w57TLh1vWRY03SzfDGglE0XDhOwQ4XfZkxBnswomEdFCY8huYE6HiNXN3kr4Ni0L8YyGY4kc+hN5HEvkMJEvAQAM00L/eB794/nK4yVRQGvAhfagyy5hCbrQFnRDlviHjIjmL5EtIpEt4tX+CbQGFET9LpQMcypMlwwUDbMSqE+3yI5HdsCnSHbodkkIuJwM4ERUMwzZy4go2KG5NeDCB1ZHAADpQgl94/nKbPdIWoVRbm+imxaOJws4niwAfRMA7M4mzT4F7SEXOoJ27Xg0oMCvSBBY401E58CygJGUhpGUNq/nyRcN5IsGYpnZz+OWRfgVO3R7J4O4Yn/tcp56Fn2hWJbF35tEDYwhe5kLuJ3Y0hnEls4gAHtGO5ZRMZxUMZQqYChpdzfRdLvMxAIQz2qIZzW8OZiqPI/LKSLqdyHqVxAN2PetARcCLoZvIqq9QtFEoajNGcCdDqESvidnwie/9soSxCqWzemGieGUiuPJAoaSBUgOEWvKnzh6ufIvUUPhO5pmcIgC2oNutAfdeB+aANhlJhO5IoZSKoaSBQyXw3dW0yuPU0vmrHITAFAk0Q7efhdagy50BF1oD7pPW39JRLRYSoaF8VwJ47nSrH2iAHgVCUG3c8Yt4Hae8TUrasnA4EQBgxN5xNIadHNa3UvJrNSotwYUrG62a9RZ3kK09DFk02mJgoBIebXJyRlvAEirJYykVMQzGmIZFbG0htGMWrm4EgA03cTARAED5ZUtJzV5nHaYL5edtAddCLqdnPUmorpiWkBG1ZFRdQxO+z0mlMN3qBy4K+HbZdd/p/IlDCbzGJwoIJEtntFr2cvUa/hd3wS6mjxY0+LlUvVESxhDNp2zgMuJgMuJ9a3+yrbJloKxjIZYWrXvMxpG0yry0xbUmVxM553hdGWbR3bYF1kG3WgPudEWdCHildlakIjqjmUBWVVHVtWBE8K37BArJXbnQjcsHB3L4ehYDl7FUbmAnS0PiZYWhmyqquktBde2+CrbLctCWtUxnCxgqLyK5VCyUOluAtgXLR2J53Aknpt6Pth142GvjEj5FvYp9r1XrtkFS0REc7EszCtgnyinGXjreBpvHU+jxa9gVcSDJq+MoNvJCQiiOldXIfvll1/Gv/3bv+HXv/41BgYGEAqFcNFFF+G+++7DhRdeOOPYV199FXfeeSf27dsHSZJwxRVX4JFHHsGaNWtqNHo6FUEQKh+nbmgPVLYXigaG0wUMJ9VKrXcso2KyZNECkCqUkCqUcHQsN+t5vYo0LXzLiPpd6Ay50eRh6QkRNZZ4RkN82oWbXsVRKVUJTStZYT03UX2oq5D93e9+F4lEArfffjs2btyIeDyORx99FDt27MAvfvELXHHFFQCAgwcP4rLLLsP27dvxox/9CKqq4r777sMll1yC119/HS0tLTX+SehMuWUH1jT7sKZ5atZbN0yMlktMJpeIn1zNcnrJCQDkNB05TZ91waXLKaI96EZnyF3p882l5ImokeQ0AznNwHBSnbHdqzjs4O2xZ7wn2xXykz+ixVVXIfuf//mfEY1GZ2y7+uqr0dPTg4cffrgSsu+77z4oioLnn38egYA9K3rhhRdi3bp1eOSRR/C3f/u3iz52qh7JIaIzZAfkExWKhh26c1o5fBeRyBUxntOQVmd2O5msaZzkdAhoC9iBuzNk1323+hXO+hBRQ5kM30MnhG+HCHhkO3B7ZAe85XufIsGjSPA4HVVtV0i03NVVyD4xYAOAz+fDxo0bMTAwAADQdR3PP/88/tt/+2+VgA0AK1euxOWXX44f//jHDNkNzC070Cm70dk0O4AXdRMjqUKl1eBQqoDRlAajvExcybBmdToRBSDiVdDit2/R8n2LT4HCWR8iaiCGOdUpZS6CYF+A7pUlhH0yWnz270POgBOdm7oK2XNJpVJ49dVXK7PYR44cQaFQwNatW2cdu3XrVvzyl7+Eqqpwudj2aLmRJRHdES+6I97KNt00EUtrldA9ubhOybCDt2lNLa6D4ZnPF3BJ5fDtqgTvFr/CBXaIqCFZ1tQseCyj4SAyAAC/S0Kzb2oyIuhmlxOiM1H3IfvWW29FLpfD1772NQBAIpEAAITD4VnHhsNhWJaFiYkJtLe3n/Q5Y7EY4vH4jG29vb1VHDXVC0kU0RGyl3+fZFoWxjIahlIqRlKFcp9vu/xk2hIRSKs60qo+o9sJYIf5Zp889UfHp6C5fJMllp4QUWOZnP2eLL9TJBERn4wWv4ImjwyvIsErO1h6R3SCug7Z9957L5566il85zvfmdVd5FQziaebZXzsscewe/fuqoyRlh5REOyl3wMuoCtU2a4bJhK5YiV0j2XtRXbiGa0y8w3YZSlDSXVWvSMAhNxONPvtwB31K1jV7EWrX+HMNxE1DO0kvwNdTrEcuCX4XHbw9ipTS9SfbIVM3TBRMiyUTBMlvfy1YZZvFkIeJxfloSWpbkP27t278eCDD+Khhx7CbbfdVtkeiUQATM1oTzc+Pg5BEBAKhU753LfccgtuuOGGGdt6e3uxc+fOeY+bli7JIaI14Jr1y9y0LKQLJcSzGsYyWvm+iHhWQ6owcxnmZKGEZKGE3li2ss2rSFjT7MWaFi/WtvgQ8coM3UTUcNSSCbVURAJzr3DpcorwyBJMy6oEaN0wMX2V+ZPpCLlwQVcTgh6WqtDSUZche/fu3di1axd27dqFe+65Z8a+tWvXwu12Y//+/bMet3//fvT09Jy2Hjsajc55kSXRXERBQMgjI+SRsS7qn7GvqJsYy2onBHD7fnL2O6fp2H88hf3HUwCAoNtZDt0+rG3xIuSRF/1nIiJabJMh/FzY19MMY22LD1tXBHkxZgOZXKyuEWv96y5kP/DAA9i1axe+/vWv4/7775+1X5IkfPKTn8Rzzz2Hv/u7v4Pfb4ee/v5+7NmzB3fcccdiD5mWMVmaXfMN2LPfiWwR741lcSSew3vxbKXHd6pQwmsDSbw2kAQAhL0y1jTbs9zdYQ+CHif7eRMRncCygN5YFscSOWxsD2BDm39J14GrJQNZTUdW1ZHV7Lr3nKYjV9TR5JGxMuJBZ8i9pH/G0xkYz+PNwRTSaglbVwSxqSNY6yFVVV2F7EcffRT33Xcfrr76anz84x/Hvn37ZuzfsWMHAHum++KLL8YnPvEJ3H333ZXFaJqbm/HVr361FkMnmkEUhMqV+B9YHYFpWYilNRyJZ/FePIv3xnKVpZcnF9z5Xd8EALuft91O0FVpKRj1uxD2yietaSQiWi50w8KbgykciWexdUUIq5u9p3/QHNSSgdG0ipGUirSqw+10wC07Km0MJ792n0X/cNO0a8t1w4JerjMv6iZymo7MtECd1XToxsnrZHJaAYMTBUiigM4mN7rDHnSE3A3zN2AoWcCbg0mM56ZKLt8YSCGVL+EDayIN83PWVcj+6U9/CgB48cUX8eKLL87ab5X7HW/YsAGvvPIK7rrrLlx//fUzllXnao9Uj0RBQFvQhbagCx/qaYZhWhhOFfBePIcjcXtmZrK8pGRYc15U5BAERHxyOXjbATwasLubNPJMBxHRXHKagd8cSeDQSBrv626yL2Y/Bd0wEc9qGEmp5RWFS6c8fpIg2PXkbqe9eI+FqYs1ddOEYVpnVV9+NnTTQl8ij75EHk6HgBVNHqyMeNAWcC3JhYNiaRVvDKYQz2hz7j+WyCOt6vjw+mZ45LqKqOdEsCaT6zL39ttvY/PmzXjrrbewadOmRXvdF/YPI5k/szc6NS7dNHF8ooDhlFrubmLfp0+yaMR0ogBE/S60B+1bW9BeSt6rLP1fUEREZ6qzyY3tXaFKba9lWUjkipVQPZbVYJg1HmSVKJKIrrAduKML2MEqVShhYDyPoNs5r4WJElkNbw6mMJya3ZVrLm5ZxCXrWtDsU87p9RbK2WZF/hUmqgOSKGJlxIuVkZkfe6ole1GIeEZFLKMhlrYvqpyY1tPbtICRtIqRtIrXBqYeG3BJaC8H7vaQG+0BF8I+mfXeRNSQjk8UMJwsYFWzF0XdRCyjoag3SKo+gaab6I1l0RvLwi2LWBXx4vz2QNUuCE3mi3h7KI3+8TymT8V6FUdlXYiIT0bYI59yRj2ZL+LNwRQGp620fCYKRRMvHRjFxavCWNPiO9cfo+YYsonqmMvpQHfYg+6wZ8b2kmFWZryHU+VbsoBc+eJKYHIxnQwOjWYq22SHiGigvHx8eTGdZr+CiFdpmBo4Ilq+TAt474QFxBpdoWjiwHAG745m0dPqw8Z5hO3xXBFvD6UwMD53KLZXBLXLVwDAIQIhj1wO3va9V5GQUUvYP5hC3wkh/WwYJrDvvXFM5Et4X3doSba+ZcgmWoKcjqmuJtu77G2WZSGj6RgpB+7htIrhpP0R6eTvuKJhYnCiMGtWQRSAiHdq2eSWaUsos1UWEVH9000LB4cz6B3NYm3UDttu+cx+f49lNbx1PDXnImunYphAIltEIlvEofI2tyxCK1WvPv3QSAbpQgkf7IlAkZbW3yOGbKIGIQgCAi4nAi4n1rdO9fMu6iZG05Mz3oVy+YmGrDZV721aQLzc7xvDM5834JLKgXt6txMFPkVakjMLRESNTDctHBrJoDeWQU/Uh43twZOG7VhGxdvH02dcK30mCsXql+gMp1T8n7dH8eH1LUuqnzZDNlGDk8sXyHSdUHJSKBp2sC7XfMfLi+iM54ozZiDsshMdR074CNblFBH1uyqhu8WvoNXvQsjjZPgmIqoxwwQOjdh122tbfNjYEah07BhNq9g/mELsJF0+6lFG1fF/3h7BB3ua0XnC2hT1iiGbaJlyy3PXe+umifFs0Z7xLofwyW4npWl9XdWSif7xPPrH8zMer0gi2gIutAZdaAvYHU9aAy6WnRAR1YBhAodHszgSz2J1sw/JfBFj2XNbebPWSoaF/3s4vmQWrmHIJqIZJFFENOCa1XPWtCykCqVy6J7qeBLPaJXVLAH7qve+8Tz6TgjfTR7njPDdFnTxgksiokVimPaKmUudZdkL1zBkE1HDEAUBTR4ZTR55Rs03AGQ1HfGMVlk9bbKl4PT2WRP5EibyJRwYmep2Ion2Ij12j2/7Qs62gAuyxMV1iIhoaWPIJqJ58ykSfIo0Y3lj07KQzJfKobtQCd+J7FSPb920pnU7sZeVFwA0+xS0h1zoCLor91xch4iIlhL+1SKiBSEKAsJeGWGvjI0dgcp2e5EIe8bbbjNor3SplWe9LUx1OnlzMFV5XNDttFe0DLgQKfdkDXtldjkhIqK6xJBNRItKlkSsaPJgRdPUBZemZWEiV8RQaip0D6UKyExbVj5VKCFVKOHgtHITwL7QMuKVEfYpiHjlcvi2Q7jftXRaPRERUWNhyCaimhMFARGfgohPwZbOqYtZMmqpsprlUErFULKA8WlLygP2hZZDKRVDc/R5DXmcWNPsxepmH9a0eNHkkRfhpyEiImLIJqI65nc54T9hcR3dMDGeL2I8W0QiV8RYubd3IlfExAkBPJkv4dX+JF7tTwKwO5ysbvZhTbMXa1q8CDF0ExHRAmHIJqIlRXLYi+BE/a5Z+3TTRDJXQiKnYTSt4ehYDscSuUq990S+hIn+Cbzab19k2eRxYk2zD6tbvFjTzNBNRETVw5BNRA1DEkU0+xU0+xWc1wZ8eH0LDNPCcKqA9+K5OUP37/sn8Pty6A66nWgN2CtXRgNKOcwrULiQDhERnSWGbCJqaA5RqFxoeWLofm8si2OJfKWf9+TFlYdHZy7YEHQ7EfUraA3YoTtavucqlkREdDIM2US0rMwVuoeSBbw3lsNQsoBYRsVYpgjDmqrungzf756wWlrAJaE1YC8b31a+jwYUOB1cTIeIaLmbd8hevXr1SXvUiqKIUCiEiy++GH/+53+O888/f74vR0RUVQ5RQFfYg67wVEtBw7SQyGmIpTXEysvHx9J2727DnArfaVVHWs3OCN8CgLBXRlvQVQngrQGFS8gTES0z8w7Zl156Kfbu3YuhoSF86EMfQmtrK0ZGRvDrX/8aHR0d6OrqwnPPPYd/+Zd/wd69e3HRRRdVY9xERAvGIQrTLq6cailomHY/79Fy8B5Nq3b4zmiVmW8LQKLc7eTtoXTlsZIooMVvtyl0SSLkyZvDvlckEbLkqHwvSyKU8tc+lwSRC+4QES0p8w7ZV111Ffbt24fe3l50dXVVtvf39+NjH/sYdu7ciSeffBKXXXYZ7r//fvzsZz+b70sSEdWEQxQqF1ZumrbdMC2MZe3QPZJWMZq2v57eUlA3Lbvn9xz9vE/HKztwXlsAG9r8WNfqgyKxFpyIqN7NO2Q/9NBD2LVr14yADQDd3d2477778MADD+Czn/0s7rjjDvzFX/zFfF+OiKjuOEShUhqyddr26UvIj5bDd7JQRFE3UTRMFHUTpnXSp63IFQ28Wm496BAFrG3xYkM5dLPtIBFRfZp3yO7t7UUwGJxzX1NTE44dOwYAWLVqFfL5/HxfjohoyZhrCfnpLMuCblozQndRN6HpJkqGfZ8v6jgSy6I3nkXJsGCYFg6PZnF4NIt/fwNoD7qwoS2A89v96Ai5WVZCRFQn5h2yV65ciSeffBJ/9Ed/NGvfE088ge7ubgBAIpFAOBye78sRETUMQRDgdAhwOkR4T3HcB9c2o2SYOBLP4sBwBodG0kirOgBUSlD2HIrBq0joanJjRZMHXU1udDa54ZHZRIqIqBbm/dv3L//yL/Fnf/ZnGBwcxA033IDW1laMjo7iRz/6Ef7zP/8Tjz/+OABgz549vOiRiOgcOR1iuUQkAMvqwFBSxYGRNA4OpzFUrvPOaToOjmRwcCRTeVzEK2NFOXivaHKjI+Rmi0EiokUw75D9pS99CZZlYdeuXfjKV75S2d7W1obvfe97+MIXvgAA+NrXvgZFUeb7ckREy54gCOgsz1R/9PxWJPNFHBzJ4OhYDseTBYznipVjJzudvDGYAgCIAtAacFWCd2fIjWhAgSQyeBMRVVNVPkf88pe/jC996Us4dOgQEokEIpEIzjvvvBn9s1tbW6vxUkREdIKQR8aONRHsWBMBYM9oH08WMDCRx+B4AYPJAnKaXV5iWlMlJr89Zi8n7xAERAMKOoJutIdc9n3QxeXkiYjmoWrFeoIgYMOGDdV6OiIiOkdeRcL6Vj/Wt/oB2BdYJgslDE4UMDiRx+BEAceThcpy8oY1rb1g/9TzRLwy2kNudARd6AjZwdvvctbiRyIiWnKqErKPHTuGb3zjG9izZ09lJvvyyy/H3XffjdWrV1fjJYiI6BwJgoAmj4wmj4wtnXY3KNOyEM9oGEoWMJxSMZQsYChVgFoyK4+bLDV563iqss2nSGjyOBHyyAh5nAi5p38twy1z9puICKhCyH799ddx+eWXI5/P44Mf/CAuvPBCjIyM4Mknn8TTTz+NV155Bdu3b6/CUImIqFpEYaq39wXlbZMz3sPJAobKwXs4pSJVKFUel9V0ZDUdAxOFOZ9XkcRK4A6Vw3jUr2BFk5uz4ES0rMw7ZP/FX/wFWlpa8Ktf/arSrg8A+vr6cOWVV+KOO+7Anj175vsyRES0wKbPeG/smFr/IKvpGE4VMJxUEcuoSOZLSBZKSOVLleXkJ2m6WV7xUpv1/EG3c0ank86QGy7WfRNRg5p3yP6v//ov/I//8T9mBGzA7p+9a9cufPGLX5zvSxARUQ35FAnron6si/pnbDctC1lVRzJfRLJQQjJfwkS+iNS0rzV9qvwkVSghVSjh7aE0AEAA0OxXKr29O0NuNPsUlpwQUUOYd8gOBoMnXfExFAohEAjM9yWIiKgOiYKAgNuJgNuJ7pMcUygaGEoVZlx0OVl+YgGIZzTEMxpe7U9WHuORHYh4ZYS9MiI+BRGvbH/vU+CVHTM6VxER1at5h+ybb74ZP/zhD3HNNdfM2veDH/wAN91003xfgoiIlii37MDaFh/Wtvgq29JqCcenhe6BifyMCy7zRQP5YmHOum9FEiuBO+KV0RFyo6fFx9lvIqo78w7Z73vf+/Dss8/i/e9/P2666Sa0tbVhZGQE/+t//S/EYjHccMMNeO655yrHX3fddfN9SSIiWsICLicC7U6c325/0mlZFsZzRQylVIznihjPaUhk7c4m0y+6BOya76GUWlnlErDLTrrCHqxr9WF91I/OJjdEznYTUY3NO2T/yZ/8CQBgYGAAv/vd7+bcb5UvjBEEAYZhzPcliYiogQiCYJeF+GavClwyTEyUWwkmckUkshrGy18n80WYll120j+eR/94Hi8diMEjO9ATtQP3ulYfu5oQUU3MO2SzcwgRES0Up0NENOBCNOCata9kmOhL5HF4NIPDoxnEMnZHk3zRwJuDKbxZXkq+PejC+lY7cHeHPVxCnogWxbxD9qWXXlqNcRAREZ0Vp0NET9SHnqgP12xpR6pQwrvlwN0bz1bqvCdXs9x7OA7ZIaLZLyPiVdDsU9Dssy+ubPbK8ChVWwSZiKg6Kz7+5Cc/wVNPPYW+vj6oqjpjnyAIeOONN6rxMkRERCcVdDtx0aowLloVhmFaGJzI4/BoFu/GMjg+UYAFoGiYGEqqGEqqsx7vdjoqoTvik+0Q7lUQ9nIlSyI6e/MO2d/85jdx1113oaWlBT09PfB6vdUYFxER0TlziAJWRrxYGfHiyo2tyGo6emNZ9CVySOSKGMtqSOVLmL6UTqFkYGBi7q4mLqeIJo+MkEdGk8dZXrTHWf6eIZyIZpt3yH7sscfw+c9/Ht///vfhcPCXDBER1R+fImF7Vwjbu0KVbSXDtC+izGoYyxaRyJXvsxrSqj7j8WrJrJSdzKUSwt1ONHlldIc9WNvig5clKETL1rzf/YlEAjfffDMDNhERLSlOh4jWgAutc1xUqelGpY3gRK6IifzkrYRkvoiSMXM5+RND+K+PJADYF132tPiwNurDqogXssSLLomWi3mH7A996EM4cOAArrjiimqMB5lMBg888ABef/11vPbaaxgbG8P999+PXbt2zTjuc5/7HP7n//yfsx5/3nnn4eDBg1UZCxERLU+K5EBHyI2OkHvWPsuykCsaSJZD92QIn1xKfjxXhG7aIXwyeP+/3jE4BAHdEQ/WtnjR0+JDZ5MHDpH9vIka1bxD9re//W1ce+216OrqwtVXXw1Zluf1fIlEAo8//ji2bduGnTt34oc//OFJj3W73Xj55ZdnbWs0ggA0eZwQBAG6YUE3TRimBd20YJgWLOv0zzGdJAqQHAIkhwinaN9LDgEOQUAso6Gom6d/EiKiZUoQBPgUCT5Fwoqm2ft1w0T/eB698SyOxLIYLF90aVgWjo7lcHQsh18diEGRRHSFPWgLuNAaUNAacCHqd3G2m6hBzDtk9/T04KMf/SiuvfZaCIIAj8czY78gCEilUmf8fCtXrsTExAQEQcDY2NgpQ7YoitixY8c5j72ehTzOM/6lqxtmJXBP3ZuwrMlALcLpECCJ9r1wipXQDNPCwHgevbFspecsERGdOckhYk2LD2tafMBGoFA0cHQsZ4fueBbx8u9WTTfRG8uiN5ad8fiwV0bUr1RKWVoDClp8CiQHwzfRUjLvkH3nnXfin/7pn7B9+3acf/75857JPlUAbGQ+l4RWv4K2oP1L1eU88xp3eya6OuNwiAJWNXuxqtmLVKGEI/EsjsZz0Di7TUR0TtyyAxs7AtjYYS8jnyqU8F45cA8lVcQzGoxpH0naS8sXcXAkU9kmCkDEqyAasFsKhjwymtx2d5OQx3lWfzOIaHHMO2Q/+eSTuOuuu/CNb3yjGuM5K4VCAW1tbYjH42hvb8fOnTvxN3/zNwiHw6d8XCwWQzwen7Gtt7d3IYd6UltXBBHyyPDV4RXoQbcT7+tuwvYVIQxM5HEknsVIirPbRETzEXQ7cUF3Ey7otmtNDNNCIqthNKNhNK2WbxoSWa3SYtC0gHhWQzw79+/g6S0GQx7njAAe8SpsMUhUA/NOdoZh4Morr6zGWM7Ktm3bsG3bNmzevBkAsHfvXnzrW9/CSy+9hN/+9rfw+Xwnfexjjz2G3bt3L9ZQT2lFk+f0B9WYOK3fbEYt4Ug8h/emraZGRETnziEKlaXjt3QGK9tLhomxrFYJ3aNpFbGM3d/bsE7d3eREYa+MzpDbvjW50RF0M3gTLbB5h+yPfexj2LdvX9W6i5ypO+64Y8b3V155JS644AJcf/31+MEPfjBr/3S33HILbrjhhhnbent7sXPnzoUYakPxu5zY3hXC1s4gjicL6EvkMZ4vIntCT1kiIpofp0NEe9CN9uDMC/pNy0JW1SvdTZL5IiYK9n0yX0IyX0LRmDkJMlmCsv/41DVSEa+MziZ3JXx3hNwsOyGqonmH7HvvvRc33ngjvF4vPv7xj89ZqnG68o1qufbaa+H1erFv375THheNRhGNRhdlTI1KFAV0hT3oCtsz8UXdRDJfxHi+iImc3cYqXSjBPMvOJ0REdGqiICDgdiLgdqI7Mnu/ZVkoFI1K8B5Nqzg+UcDxZGHGIjuJnN0H/M3BqeDd7JPRMS10c8ab6NzNO2Rv27YNAPCVr3wFX/nKV+Y8xjCM+b7MGbMsC6LIK7AXmyyJlY87JxmmVZ5psWdbxjIakoXSWbccPJsxmJYF3WCyJ6LlSxAEeBQJHkVCZ8iNTR1TJSgZtYTjyUIldB+fKCCjTQXvsWwRY9mZwbvJ40R7sBy6Qy50hNwIuJyL+jMRLUXzDtn33Xdf3XQEefbZZ5HP5xu2rd9S4xAFRHwKIj6lsm1yGeN4xr6AJ5EtnlNfbskhIOyREfbJaPYqCPumLh5VSwbSaglZVUdW05FR7VtW09kDnIiWNb/LiQ1tTmxoC1S2pQvl4D0tfGenBe+JfAkT+RLeGU5XtvkUqRK4u5rsTzXr8QJ+olqa9zvixJUYq+HnP/85crkcMhm7fdE777yDZ599FgBwzTXXIB6P4+abb8Yf//Efo6enB4IgYO/evfj2t7+NTZs24Ytf/GLVx0TVMdcyxqlCCfGMfSV9PKshXZhZ3y0KQMgjI+KTEfHKiHgVBNzSSf9x53I64HI6EPXP3qfphh24y6E7p+lT/cUtC2b5a7P8vWFO3RyiAFkS4XSIkB0iZEmsfO90CFAkEbLDAackwOkQYVnA4EQe/eN55LTF+zSHiOhsTJaenN8+LXirJQwlCxhKqhhOFTCULGAiX6rsz2o6Do9mcXh0qsd3k8dplxGWQ3dH0MXe3rSsCZa1UB/en7tVq1ahr69vzn1Hjx5FMBjEF77wBbz22msYHR2FYRhYuXIlrr32Wtxzzz0IBoNzPvZU3n77bWzevBlvvfUWNm3aNN8fgeZB0w0kskXkizqaPDKaPDLEJb708FhWQ/94Hv2JPPJFBm4iWnryRR3DKRVDyQKGUyqOJwsYy0y1GTyRQxTQHnRVgnd32FNZvZhovm7+QPeiv+bZZsW6/Gzn2LFjpz3mueeeW/iBUE0okgMdIffpD1xCmn0Kmn0K3tfdhHhGQ/94Dv3jeRSKLF8hoqXBI0tY2+LD2papFrlqycDgRAGDE3kMjOfRP1FArlxqYphWeV8Bv0Gi/BwOtAZc9qeSPgURr4xmn73ADpeTp0ZTlyGbqJG1+BW0+KcCd9+4/cfpTPqOiwIqZSuSKMIh2kszF4oGO7kQ0aJzOR3oifrQE7WDt2VZSOZL6J/IY3A8j4EJu9REL/+CypeXmD86lpv1XAGXhIhPQbPPLguM+KaCuJNlJ7QEMWQT1YggTC1AcdHKJoymNeSLOpwOEZJDgCTatd6SQ4Qk2nXejlOUzWi6AbVkQi0ZUEsGCiX7+0LRgKob0MrbOHtORAtFEAQ0eWU0eWVsWxECAOimiZGUiv7xPI5PFDCW1ZDIFWeVzqVVHWlVnxXABdiTEx0hN9qDrsq9R2aEofrG/0OJ6oAgCGgLuk5/4CkokgOK5EDQferWWqlCCb2xDN6L51Biu0MiWmCSKGJFk2fWCseFooFETsNYtohEzu42lZgjgFsAYhkNsYyG1wemHh9yO9EecqNjWvAOulnzTfWDIZtomQm6nbhwZRjbVoRwLJHDu6PZGV0DiIgWg1t2YIU8O3wDdgC3Z7w1jKa1cocTdUZrwWShhGShhAPTWgt6ZAc6gm60h1yV+2afApHBm2qAIZtomZIcInqifvRE/YhlVPSOZtE/nmdtNxHVnFt2zFhVeFJaLWG43N1kKFnAUErFeK5Y2Z8vGuiNZ9Ebn2otKDtEtAVddl/voBvtITda/QrbC9KCY8gmIkT9LkT9LryvZKA3lsWReHZWb29ZEuF2OuBy2veK0zH1veyAS3Igq+k4Es9iOKUu2MqeRLR8BVxOBNqcOG/aYjpqyZjWWtCe8Y5l1MqEQdEw7Raq4/nKYxyCgGhAqcx2twfdiHhl+F0nX4OB6GwxZBNRhcvpwObOIDZ1BBDPanAIQiVAn0mv8iavjK6wB/mijvfiObw3lkNW1U/7OCKic+VyOrC62YvVzd7KtpJhIpbWyrPddoeTkbRauQ7FsCwMp1QMp1Sgf+q5nA4BTR4ZYe+0W/n7JnY5obPEkE1EswiCgKj/3C/E9MgSNncGsbkziNG0iiOxLAYm8jDY2ISIFoHTIaKzyY3Opqk1F0zLQjwzVd89Gb6nt08tGVblIsu5BFwSQh4ZAbcTIbcTwRNuPpfE+m+qYMgmogXVGnChNeBCUTfRl8jhSDyL8RwvtCSixSUKQuX30fYue9tkX+/RjIqJXBHjk7e8fX9iB6bJNoMnfw17mfrpwTvsldEd9qA14GIAX2YYsoloUciSiHWtfqxr9WMiV8SReBZ9iTwMy4LTIcAhinCKAhzTeoJLot0n3N5m77MsoFAykC8aKBQNFEo61JLJGnAiOmvT+3qfyLIsZDV9KniXb6lCqXLTT7hS3LSAZL6E5Bwdm1xOESvDXqxq9mJVxIPOJjckkeUnjYwhm4gWXZNXxkXeMC5aFa7K81mWdULwNpDVdPQlclx8h4jOiSAI8Luc8LucWBnxztpvWRZyRcMO3PkSUmr5vjAVxNMFHUZ5BkAtmTg0msGh0QwAu/57RZMHqyJerGr2oDvsgSI5FvVnpIXFkE1ES54gCPDI0qwV4LavCKFvPI9DI5kZbb6IiOZLEAT4FAk+RUJnyD3nMaZlYSyroW8sj2OJHI4lcpV1CUqGNbXE/CG71KQ96MbqZi/ag67KhZc+hR1PliqGbCJqWKIoVLoOjGU1HB7JsBc4ES0asXwRedTvwsWr7U/uUoUSjo3ZgfvoWK5ykaVpAceTBRxPFmY8hyyJlQ4nEa+MsG/yawVBtxOOM+j8RLXBkE1Ey0KzT0Fzj4ILigbejWXQG8vO6CpARLQYgm4ntnWFsK0rBADIazr6xvOV4H08WZgxEVDUTYykVYyk1VnPJQpAyCOj2SejI2h3U+kMubm8fJ1gyCaiZcUtO7B1RQibOoLoS+RweDTDbidEVDMeRcL57QGc324vsFMyzMpFlolcEeM5zf46W0QyX6rUeAP27PfksYdHp1a5nCxh6WxyY0U5ePtdzkX/2ZY7hmwiWpYcooA1LT6safEhntFweDSDoWQBummxUwkR1YzTIVZaDZ7ItCyk8qVy+LYDeCJXRCyjYSyjYfJXV1bTZ1xkCdgz6J2hqdDdGXLDozAGLiT+1yWiZa/Fr6DFr1S+1w0TummhZJgoGRZ0s3w/7XvdsFA0TOQ0HemCjqxW4mI7RLSgxFO0HNR0A0NJFceTBQxO5HF8ooDEtAu+JzuevDOcrmxr8jixoslTmfXuDLnhcrLDSbUwZBMRnUByiJAcOKs/NpPtvDKq3bYro5aQUXWk1RJymrGAoyUiAhRp9vLyhaKBoVQBxyfs4D2YLMzo4T2RL2Ein8L+46nKtmafMmO2uyPkhiyxn/e5YMgmIqqC6e282oMz9+mGiWx5xlvVDYiCPSMlCvYCO6IoQBQAh2B/PXkvCvbz9sayODSS5kw5EZ0Vt+zA2hYf1rb4Kttyml7pYjI4UcDxifyMVSzHshrGshpeH0gCAATYaxu0+hVEAy5E/QpaAy60+BU4HQzfp8KQTUS0wCSHiJBHRsgz+yPeM7G9K4SeqA+v9yfRP56v8uiIaDnxKhLWt/qxvtVf2ZZWSxiaKGAwOTXrnSvan8BZmLq48sDIVI23ACDslRENuMoBXEHUz/A9HUM2EdES4FMk/OG6ZsQyKl7tS3JxHSKqmoDLiUC7ExvKHU4sy0KqUMLxZAFDSRWxjIrRtIbxnFZpL2gBSJQ7oBwYnnouAUBb0FUJ8t1hz7Lt5c2QTUS0hET9Lly9uQ3vxbN4YzDJZeOJqOoEQah8+rapY6r+TTdMxLMaYmkNoxkVsbSGWEZFIlusdDaxAAynVAynVOw9HIciieiJ+nBeqx/rWv0IupdPK0GGbCKiJWhNiw/dYQ/eGU7j4HAG+mmWsfQqDgTcTgTdTgRc9r3fJcEwLeSKOvKaYd8XDeS0qfuSwX6GRGSTHCLag260B2cuI18yTIxlNYymNYymVRyJZ3F8ogALgKabeHsojbeH7K4mbQEX1rf6sL7Vj5URb0PPcjNkExEtUZJDxNYVIaxt8eGNAbte2+eSKiF6KlRLkE5RI+lVJMA/976ibiJf1JErh+7xXBF9iRwvwiSiCucc4Tur6Xh3NIPDoxm8G8siX67xnly98v++OwZFErG2xYd1rT40+xS7bMUlQWmQNoIM2URES5xXkfDBnmb8gWVVfSllWRIhSzJCnqlt27tC9h/O0Sw0nWmbiGbzKRIu6G7CBd1NMC0LxycKOFQO3dNnud8ZTs/o3Q0AiiQi4HLC75YQdNkTBgGXBP+0CQTTtCDW+Sw4QzYRUYOodsA+GZfTXpp+Y3sAR8dyODCSQXZaCzAioulEQUBX2IOusAcfPb8VWU1HbyyDw6NZHB7NVGa5J2m6Xfsdz2onfc6bP9Bd9/XdDNlERHROJIeIda1+9ER9GJwo4J3hNBJZdj0holPzKRK2dzVhe5c9yz2eLSKllpAulJBW9fL91PcZtYTpl504HQICrvqPsPU/QiIiqmvCtFmqWEbFweEMjicLsHjNJBGdhigIaPYraPYrJz3GtCzkygt6pdUSNN1YtE/u5oMhm4iIqibqdyHqdyGtlnBoJIOj8dxpO58QEZ2KKAjwu5zwu5zohPv0D6gTDNlERFR1AZcTF68KY0tnEMeTBYym7I4CaokXShLR8sCQTUREC8bldGBtiw9rW3wAgGS+aLfwSqmIZTTo7MNNRA2KIZuIiBbN5CpyG9oCME0LYzkNIyk7dI/nimBlCRE1CoZsIiKqCVEUKjXcW1cApmmhUDLsW9FAvmggX9Snvi4ZKBR1LoRDREsCQzYREdUFURTgVSR7BcpTUEsG1JKBXNHARK6I4ZSKRFbjLDgR1RWGbCIiWlJcTgdcTgdCHqAz5MbmziB0w0Qso2EkrWI0pWIiX6r1MIlomWPIJiKiJU9yiOgIudERstt7qSUDsbQdukfSKlekJKJFx5BNREQNx+V0oDviQXfEAwDIanrlAsuhZIG9u4lowTFkExFRw/MpEnqiPvREfdANE8eTBfQl8hhOFXghJREtCIZsIiJaViSHiJURL1ZGvCjqJgYn8uhL5DGaVnnxJBFVDUM2EREtW7IkYk2LD2tafFBLBgbG7cAdz2qwziJwK5IIr2JfkKlIDmQ1HalCCUWd0+REyxVDNhEREew67nWtfqxr9SNf1NFfDtxqyYDb6YBHluCWHfCUb/bXEjxOB0RRmPM5C0UDqUIJqUIJyXyx8nWJK10SNby6C9mZTAYPPPAAXn/9dbz22msYGxvD/fffj127ds069tVXX8Wdd96Jffv2QZIkXHHFFXjkkUewZs2axR84ERE1DI8sYUNbABvaAvN6Hnc5jLcFXTO254v2TPfAeAFHx7KsCydqQGKtB3CiRCKBxx9/HJqmYefOnSc97uDBg7jssstQLBbxox/9CE888QQOHz6MSy65BPF4fPEGTEREdJY8soT2oBvvXx3Gp7d3YktnEIpUd3+SiWge6m4me+XKlZiYmIAgCBgbG8MPf/jDOY+77777oCgKnn/+eQQC9kzDhRdeiHXr1uGRRx7B3/7t3y7msImIiM6Jy+nAlhVBbOwI4OhYFgdHMkgX2NebaKmru382C4IAQZi7tm2Srut4/vnn8ZnPfKYSsAE7oF9++eX48Y9/vNDDJCIiqiqHKKAn6scntnbgw+ubEfUrtR4SEc1D3c1kn4kjR46gUChg69ats/Zt3boVv/zlL6GqKlwu1xyPBmKx2KySkt7e3gUZKxER0dla0eTBiiYPxnNFHBhOY2A8z/aCREvMkgzZiUQCABAOh2ftC4fDsCwLExMTaG9vn/Pxjz32GHbv3r2gYyQiIpqvsFfGh3qakdN0HBrNoDeWhc7OJERLwpIM2ZNOVVZyqn233HILbrjhhhnbent7T3mhJRERUa14FQnv627CthUhJHIa4hn7NpYtshc3UZ1akiE7EokAmJrRnm58fByCICAUCp308dFoFNFodKGGR0REtCAcooCo34Wof6ocMpUvIZ4tB++shqzKiyaJ6sGSDNlr166F2+3G/v37Z+3bv38/enp6TlqPTURE1EiCHieCHid6oj4A9gI4Y1kNsYyGsayG8VzxrFavJKLqqLvuImdCkiR88pOfxHPPPYdMJlPZ3t/fjz179uC6666r4eiIiIhqxy070BX24MKVTbhqUxs+ua0D57X5ITlO3bmLiKqrLmeyf/7znyOXy1UC9DvvvINnn30WAHDNNdfA4/Fg9+7duPjii/GJT3wCd999N1RVxX333Yfm5mZ89atfreXwiYiI6oZPkXDhyiZs6QziSDyLw6MZ5DSj1sMianh1GbL/+3//7+jr66t8/8wzz+CZZ54BABw9ehSrVq3Chg0b8Morr+Cuu+7C9ddfP2NZ9ZaWlloNnYiIqC7Jkojz2wM4r9WPgYk8Do5kkMgWaz0sooZVlyH72LFjZ3TchRdeiF/96lcLOxgiIqIGIooCVka8WBnxIpZRcWgkg8GJAuu2iaqsLkM2ERERLbzJTiUZtYTDoxkciefYh5uoShiyiYiIljm/y4kLV4axpTOEI/Esjo3lMJEv1XpYREsaQzYREREBmKrbPr89gLRaQn8ij75EHqkCAzfR2WLIJiIiolkCLic2dwaxuTOIVL6EvvEc+hJ5ZLjYDdEZYcgmIiKiUwp6nNjqCWHrihDGc0X0j+fRl8ixFSDRKTBkExER0RkLe2WEvTK2d4WQUUvIqDrSagnpgo6MWkJaLaFQNGs9TKKaY8gmIiKic+J3OeF3OdEB94ztRd0sB24d6cJUEM+oJRjM37RMMGQTERFRVcmSiIhPQcSnzNhuWRZShRLGc0VM5ItIZItI5kvQTbYNpMbDkE1ERESLQhAEhDwyQh65ss2yLKQLOhI5jcGbGgpDNhEREdWMIAgIepwIepyVbdOD97FEDiMprYYjJDo3DNlERERUV6YH7zUtPrw7msFr/UnObtOSItZ6AERERESnsq7Vjz/a0oYWv3L6g4nqBEM2ERER1T2/y4mPnh/FBd0hOJheaAng/6ZERES0JAiCgPPbA7h6czsiPvn0DyCqIYZsIiIiWlKCbic+trEVW1cEIQq1Hg3R3BiyiYiIaMkRBAGbO4O4enMbmqZ1JjkbToeAFr+C9qCLYZ2qjt1FiIiIaMkKeWRctakNbw2l8M5QGidrQOKWRTR5ZDR57GXhQx57tcpJasnAwHgeR8dyGMsWF2n01MgYsomIiGhJE0UBW1eE0Bly4z+PjsO0LDR57CAd9trB2uV0nPI5XE4H1rX6sa7Vj4xaQl8ij2OJHNIFfZF+Cmo0DNlERETUECI+BddsaZ/38/hdTmzuDGJzZxDjuSKOJXLoS+RQKJpVGCUtFwzZRERERCcR9trlJRd0hTCa1jCSVhHPaEhktZOWphABDNlEREREpyUIAtqCLrQFXQAAw7SQyGqIZTTEsxrGMhpKBlM3TWHIJiIiIjpLDlFANOBCNGCHbsuyMJEvIZ7REM9oiGVUqCWWlyxnDNlERERE8yQIQqW05Lw2PwAgrZYQS9uBO57RkNOMGo+SFhNDNhEREdECCLicCLic6In6AABZTUcsrSKW0TCaVhm6GxxDNhEREdEi8CkSfC0+rGmxQ3dO0xHLaJXgnVHZLrCRMGQTERER1YBXkbBakbC62QsAKBQNjKZVvBvLIp7Rajw6mi+GbCIiIqI64JYdWNXsxapmL0ZSKvYfTzFsL2EM2URERER1ZrJdIMP20sWQTURERFSnJsP2aFrFm4MM20sJQzYRERFRnWsNuHDlRjts7x9MIcawXfcYsomIiIiWiNaAC60M20sCQzYRERHREjMZtmPlbiSFooGiYULTDRR1EwYXm6w5hmwiIiKiJWr60u7T6YaJomGiqJvQ9Ml7A32JPEbTnP1eDAzZRERERA1GcoiQHCI88sztPVE/4hkN7wyncXyiUJvBLRMM2URERETLSItfwaX+FiTzRbwzlEbfeB6WVetRNR6GbCIiIqJlKOSR8cGeZmxRSzgwnMHRsSxruatIrPUAiIiIiKh2/C4n3r86jE9t68SGdj8kh1DrITUEzmQTEREREdyyA+/rbsKmjgAOj2RxaDSDos6p7XPFkE1EREREFYrkwJYVQWxo9+P4RAG6acK0AMO0YFoWLAswLav8PWCd8LUgCJAcAhyiAEm0750O8aTfF0oGft2bgNZggZ4hm4iIiIhmcTpErGr2LvjrhAB8bFMrXjkUR0bVF/z1FgtrsomIiIiopvwuJ67c2Ipmn3z6g5eIJRuyX3nlFQiCMOdt3759tR4eEREREZ0Fl9OBj5zfiu6wp9ZDqYolXy7y8MMP4/LLL5+xbfPmzTUaDRERERGdK4co4A/XNeO1/gkcGM7UejjzsuRD9rp167Bjx45aD4OIiIiIquSC7ib4FAm/65tYsgvlLNlyESIiIiJqXOta/fjw+hZI4tLs273kQ/att94KSZIQCARw1VVX4T/+4z9O+5hYLIa33357xq23t3cRRktEREREZ6oz5MZHN7bCLS+9yLpky0WCwSBuv/12XHbZZYhEIujt7cU3v/lNXHbZZfjZz36Gq6666qSPfeyxx7B79+5FHC0RERERnYuwV8bHNrbhlUNxpAqlWg/njAmWtVQrXWZLJpPYsmULwuEw3njjjZMeF4vFEI/HZ2zr7e3Fzp078dZbb2HTpk0LPVQiIiIiOgtF3cT/ezeO0bSGmz/Qveiv//bbb2Pz5s1nnBWX7Ez2XEKhED7xiU/ge9/7HgqFAtxu95zHRaNRRKPRRR4dEREREZ0rWRJx+XlR/OfR8VoP5YwsvQKX05icmBeEpVkkT0RERERzE0UBf7A2UuthnJGGCtkTExN4/vnnsX37drhcrloPh4iIiIiWqSVbLnLzzTeju7sbF110EZqbm/Huu+/i0UcfxejoKJ588slaD4+IiIiIlrElG7K3bt2Kp59+Gt/73veQzWYRDofxh3/4h/jXf/1XXHzxxbUeHhEREREtY0s2ZN999924++67az0MIiIiIqJZGqomm4iIiIioHjBkExERERFVGUM2EREREVGVMWQTEREREVUZQzYRERERUZUxZBMRERERVRlDNhERERFRlS3ZPtnVpmkaAKC3t7fGIyEiIiKiejOZEScz4+kwZJcNDAwAAHbu3FnbgRARERFR3RoYGMD73ve+0x4nWJZlLcJ46l4ymcTevXvR1dUFRVEW5DV6e3uxc+dO/OQnP0FPT8+CvAbND89RfeP5qX88R/WN56f+8RzVL03TMDAwgEsvvRShUOi0x3MmuywUCuHTn/70orxWT08PNm3atCivReeG56i+8fzUP56j+sbzU/94jurTmcxgT+KFj0REREREVcaQTURERERUZQzZRERERERVxpC9iFpaWnD//fejpaWl1kOhk+A5qm88P/WP56i+8fzUP56jxsHuIkREREREVcaZbCIiIiKiKmPIJiIiIiKqMoZsIiIiIqIqY8gmIiIiIqoyhuwqeuWVVyAIwpy3ffv2zTj21VdfxUc/+lH4fD6EQiFcd911eO+992o08saTyWRw55134mMf+xhaWlogCAJ27do157Fncy6+853vYMOGDVAUBatXr8bu3btRKpUW8CdpXGd6jj73uc/N+Z7asGHDnM/Lc1QdL7/8Mj7/+c9jw4YN8Hq96OzsxKc//Wn8/ve/n3Us30O1cabniO+h2nj99dfx8Y9/HN3d3XC73QiHw/iDP/gD/Nu//dusY/keakwM2Qvg4Ycfxm9+85sZt82bN1f2Hzx4EJdddhmKxSJ+9KMf4YknnsDhw4dxySWXIB6P13DkjSORSODxxx+HpmnYuXPnSY87m3Px0EMP4fbbb8d1112HX/ziF7jlllvw8MMP49Zbb13gn6Yxnek5AgC32z3rPfX000/POo7nqHq++93v4tixY7j99tvxwgsv4B/+4R8Qi8WwY8cOvPzyy5Xj+B6qnTM9RwDfQ7WQTCbR1dWFhx9+GC+88AL+5V/+BatWrcKf/Mmf4MEHH6wcx/dQA7Ooavbs2WMBsJ555plTHnfDDTdYzc3NViqVqmw7duyY5XQ6rTvvvHOhh7ksmKZpmaZpWZZlxeNxC4B1//33zzruTM/F2NiY5XK5rC9/+cszHv/QQw9ZgiBYb7/99sL8IA3sTM/RZz/7Wcvr9Z72+XiOqmt0dHTWtkwmY7W2tlof+chHKtv4HqqdMz1HfA/Vlw984ANWV1dX5Xu+hxoXZ7IXma7reP755/GZz3wGgUCgsn3lypW4/PLL8eMf/7iGo2sckx+HnsrZnIsXX3wRqqriT//0T2c8x5/+6Z/Csiz85Cc/qer4l4MzOUdng+eouqLR6KxtPp8PGzduxMDAAAC+h2rtTM7R2eA5WhzNzc2QJAkA30ONjiF7Adx6662QJAmBQABXXXUV/uM//qOy78iRIygUCti6deusx23duhW9vb1QVXUxh7tsnc25eOuttwAAW7ZsmXFce3s7mpubK/tpYRQKBbS1tcHhcGDFihW47bbbMD4+PuMYnqOFl0ql8Oqrr2LTpk0A+B6qRyeeo0l8D9WOaZrQdR3xeByPPfYYfvGLX+Cuu+4CwPdQo5NqPYBGEgwGcfvtt+Oyyy5DJBJBb28vvvnNb+Kyyy7Dz372M1x11VVIJBIAgHA4POvx4XAYlmVhYmIC7e3tiz38ZedszkUikYCiKPB6vXMeO/lcVH3btm3Dtm3bKtc17N27F9/61rfw0ksv4be//S18Ph8A8BwtgltvvRW5XA5f+9rXAPA9VI9OPEcA30O1dsstt+D73/8+AECWZfzjP/4j/uzP/gwA30ONjiG7ii644AJccMEFle8vueQSXHvttdiyZQvuvPNOXHXVVZV9p/qYvJofodPpnem54DmrjTvuuGPG91deeSUuuOACXH/99fjBD34wYz/P0cK599578dRTT+E73/kOLrzwwhn7+B6qDyc7R3wP1dY999yDL37xi4jFYvjpT3+K2267DblcDn/5l39ZOYbvocbEcpEFFgqF8IlPfAJvvvkmCoUCIpEIAMz5L87x8XEIgoBQKLTIo1yezuZcRCIRqKqKfD4/57FzzULQwrn22mvh9XpntMbkOVo4u3fvxoMPPoiHHnoIt912W2U730P142Tn6GT4Hlo83d3duOiii3DNNdfgu9/9Lr785S/jr//6rxGPx/keanAM2YvAsiwA9r8y165dC7fbjf379886bv/+/ejp6YHL5VrsIS5LZ3MuJmvgTjx2ZGQEY2NjM1o00uKwLAuiOPUrjOdoYezevRu7du3Crl27cM8998zYx/dQfTjVOToVvodq4/3vfz90Xcd7773H91CDY8heYBMTE3j++eexfft2uFwuSJKET37yk3juueeQyWQqx/X392PPnj247rrrajja5eVszsXVV18Nl8uFJ598csZzPPnkkxAE4bR9nqm6nn32WeTzeezYsaOyjeeo+h544AHs2rULX//613H//ffP2s/3UO2d7hydDN9DtbNnzx6Ioog1a9bwPdToatM5sDHddNNN1l133WU988wz1p49e6zHH3/cOu+88yxJkqxf/vKXleMOHDhg+Xw+68Mf/rD1wgsvWM8995y1efNmq6Ojw4rFYjX8CRrLCy+8YD3zzDPWE088YQGwbrjhBuuZZ56xnnnmGSuXy1mWdXbn4sEHH7QEQbDuuece65VXXrG++c1vWoqiWF/60pdq8eM1hNOdo2PHjlkf/OAHrX/8x3+0XnjhBevnP/+5dffdd1sul8vatGmTlc1mZzwfz1H1PPLIIxYA6+qrr7Z+85vfzLpN4nuods7kHPE9VDtf+tKXrK9+9avW008/bb3yyivWs88+a914440WAOuv/uqvKsfxPdS4GLKr6Bvf+Ia1fft2KxgMWg6Hw2ppabGuvfZa67/+679mHfu73/3O+shHPmJ5PB4rEAhYO3futHp7e2sw6sa1cuVKC8Cct6NHj1aOO5tz8Q//8A/W+vXrLVmWre7ubuv++++3isXiIv1Ejed052h8fNy69tprrVWrVllut9uSZdlat26ddeedd1rJZHLO5+Q5qo5LL730pOfmxPkZvodq40zOEd9DtfPEE09Yl1xyidXc3GxJkmSFQiHr0ksvtf71X/911rF8DzUmwbLKBcNERERERFQVrMkmIiIiIqoyhmwiIiIioipjyCYiIiIiqjKGbCIiIiKiKmPIJiIiIiKqMoZsIiIiIqIqY8gmIiIiIqoyhmwiIiIioipjyCYiIiIiqjKGbCKiZWjXrl0QBAFjY2O1HgoRUUNiyCYiIiIiqjKGbCIiIiKiKmPIJiJaxkZHR3HTTTchGAyitbUVn//855FKpSr7BUHAbbfdhu9///tYv349FEXBxo0b8b//9/+u4aiJiOqfVOsBEBFR7XzmM5/BjTfeiC984QvYv38//vqv/xoA8MQTT1SO+fd//3fs2bMHf/M3fwOv14vHHnsMN910EyRJwvXXX1+roRMR1TXBsiyr1oMgIqLFtWvXLuzevRt/93d/h7/6q7+qbL/11lvxxBNPIJ/PQxAECIIAt9uNo0ePorW1FQBgGAY2b94MXdfx7rvv1upHICKqaywXISJaxj71qU/N+H7r1q1QVRWxWKyy7SMf+UglYAOAw+HAjTfeiN7eXgwODi7aWImIlhKGbCKiZSwSicz4XlEUAEChUKhsa2trm/W4yW2JRGIBR0dEtHQxZBMR0SmNjIycdNuJIZ2IiGwM2UREdEovvfQSRkdHK98bhoGnn34aa9euxYoVK2o4MiKi+sXuIkREdErNzc244oorcO+991a6ixw8eJBt/IiIToEhm4iITulTn/oUNm3ahK9//evo7+/H2rVr8dRTT+HGG2+s9dCIiOoWW/gREdFJCYKAW2+9Ff/0T/9U66EQES0prMkmIiIiIqoyhmwiIiIioipjTTYREZ0UKwqJiM4NZ7KJiIiIiKqMIZuIiIiIqMoYsomIiIiIqowhm4iIiIioyhiyiYiIiIiqjCGbiIiIiKjKGLKJiIiIiKqMIZuIiIiIqMoYsomIiIiIqowhm4iIiIioyv5/YiOvG2YkU9YAAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n", - "plot_predictions(model, idata, \"hp\", pps=True, ax=ax);" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here, we notice that the uncertainty in the conditional adjusted predictions is much larger than the uncertainty when `pps=False`. This is because the posterior predictive distribution accounts for the uncertainty in the model parameters and the uncertainty in the data. Whereas, the posterior distribution only accounts for the uncertainty in the model parameters.\n", - "\n", - "`plot_predictions` allows up to three covariates to be plotted simultaneously where the first element in the list represents the main (x-axis) covariate, the second element the group (hue / color), and the third element the facet (panel). However, when plotting more than one covariate, it can be useful to pass specific `group` and `panel` arguments to aid in the interpretation of the plot. Therefore, `subplot_kwargs` allows the user to manipulate the plotting by passing a dictionary where the keys are `{\"main\": ..., \"group\": ..., \"panel\": ...}` and the values are the names of the covariates to be plotted. For example, passing two covariates `hp` and `wt` and specifying `subplot_kwargs={\"main\": \"hp\", \"group\": \"wt\", \"panel\": \"wt\"}`. " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_predictions(\n", - " model=model, \n", - " idata=idata, \n", - " covariates=[\"hp\", \"wt\"],\n", - " pps=False,\n", - " legend=False,\n", - " subplot_kwargs={\"main\": \"hp\", \"group\": \"wt\", \"panel\": \"wt\"},\n", - " fig_kwargs={\"figsize\": (20, 8), \"sharey\": True}\n", - ")\n", - "plt.tight_layout();" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Furthermore, categorical covariates can also be plotted. We plot the the mean `mpg` as a function of the two categorical covariates `gear` and `cyl` below. The `plot_predictions` function automatically plots the conditional adjusted predictions for each level of the categorical covariate. Furthermore, when passing a list of covariates into the `plot_predictions` function, the list will be converted into a dictionary object where the key is taken from (\"horizontal\", \"color\", \"panel\") and the values are the names of the variables. By default, the first element of the list is specified as the \"horizontal\" covariate, the second element of the list is specified as the \"color\" covariate, and the third element of the list is mapped to different plot panels." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n", - "plot_predictions(model, idata, [\"gear\", \"cyl\"], ax=ax);" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Negative Binomial Model\n", - "\n", - "Lets move onto a model that uses a distribution that is a member of the exponential distribution family and utilizes a link function. For this, we will implement the Negative binomial model from the students absences [example](https://bambinos.github.io/bambi/notebooks/negative_binomial.html#Negative-binomial-in-GLM). School administrators study the attendance behavior of high school juniors at two schools. Predictors of the number of days of absence include the type of program in which the student is enrolled and a standardized test in math. We have attendance data on 314 high school juniors. The variables of insterest in the dataset are the following:\n", - "\n", - "- daysabs: The number of days of absence. It is our response variable.\n", - "- progr: The type of program. Can be one of ‘General’, ‘Academic’, or ‘Vocational’.\n", - "- math: Score in a standardized math test." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Auto-assigning NUTS sampler...\n", - "Initializing NUTS using jitter+adapt_diag...\n", - "Multiprocess sampling (4 chains in 4 jobs)\n", - "NUTS: [daysabs_alpha, prog, scale(math), prog:scale(math)]\n" - ] - }, - { - "data": { - "text/html": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can print the Bambi model object to obtain the model components. Below, we see that the Gaussian linear model uses an identity link function that results in no transformation of the linear predictor to the mean of the outcome variable, and the distrbution of the likelihood is Gaussian." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have fitted the model, we can visualize how a model parameter varies as a function of (some) interpolated covariate. For this example, we will visualize how the mean response `mpg` varies as a function of the covariate `hp`. \n", "\n", - "\n" + "The Bambi model, ArviZ inference data object (containing the posterior samples and the data used to fit the model), and a list or dictionary of covariates, in this example only `hp`, are passed to the `plot_predictions` function. The `plot_predictions` function then computes the conditional adjusted predictions for each `covariate` in the list or dictionary using the method described above. The `plot_predictions` function returns a `matplotlib` figure object that can be further customized. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } ], - "text/plain": [ - "" + "source": [ + "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n", + "bmb.interpret.plot_predictions(model, idata, \"hp\", ax=ax);" ] }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The plot above shows that as `hp` increases, the mean `mpg` decreases. As stated above, this insight was obtained by creating the reference grid and then using the fitted model to compute the predicted values of the model parameter, in this example `mpg`, at each value of the reference grid.\n", "\n", - "
\n", - " \n", - " 100.00% [8000/8000 00:02<00:00 Sampling 4 chains, 0 divergences]\n", - "
\n", - " " + "By default, `plot_predictions` uses the highest density interval (HDI) of the posterior distribution to compute the credible interval of the conditional adjusted predictions. The HDI is a Bayesian analog to the frequentist confidence interval. The HDI is the shortest interval that contains a specified probability of the posterior distribution. By default, `plot_predictions` uses the 94% HDI.\n", + "\n", + "`plot_predictions` uses the posterior distribution by default to visualize some mean outcome parameter . However, the posterior predictive distribution can also be plotted by specifying `pps=True` where `pps` stands for posterior predictive samples of the response variable." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } ], - "text/plain": [ - "" + "source": [ + "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n", + "bmb.interpret.plot_predictions(model, idata, \"hp\", pps=True, ax=ax);" ] }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n" - ] - } - ], - "source": [ - "# Load data, define and fit Bambi model\n", - "data = pd.read_stata(\"https://stats.idre.ucla.edu/stat/stata/dae/nb_data.dta\")\n", - "data[\"prog\"] = data[\"prog\"].map({1: \"General\", 2: \"Academic\", 3: \"Vocational\"})\n", - "\n", - "model_interaction = bmb.Model(\n", - " \"daysabs ~ 0 + prog + scale(math) + prog:scale(math)\",\n", - " data,\n", - " family=\"negativebinomial\"\n", - ")\n", - "idata_interaction = model_interaction.fit(\n", - " draws=1000, target_accept=0.95, random_seed=1234, chains=4\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This model utilizes a log link function and a negative binomial distribution for the likelihood. Also note that this model also contains an interaction `prog:sale(math)`." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - " Formula: daysabs ~ 0 + prog + scale(math) + prog:scale(math)\n", - " Family: negativebinomial\n", - " Link: mu = log\n", - " Observations: 314\n", - " Priors: \n", - " target = mu\n", - " Common-level effects\n", - " prog ~ Normal(mu: [0. 0. 0.], sigma: [5.0102 7.4983 5.2746])\n", - " scale(math) ~ Normal(mu: 0.0, sigma: 2.5)\n", - " prog:scale(math) ~ Normal(mu: [0. 0.], sigma: [6.1735 4.847 ])\n", - " \n", - " Auxiliary parameters\n", - " alpha ~ HalfCauchy(beta: 1.0)\n", - "------\n", - "* To see a plot of the priors call the .plot_priors() method.\n", - "* To see a summary or plot of the posterior pass the object returned by .fit() to az.summary() or az.plot_trace()" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model_interaction" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n", - "plot_predictions(\n", - " model_interaction, \n", - " idata_interaction, \n", - " \"math\", \n", - " ax=ax, \n", - " pps=False\n", - ");" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The plot above shows that as `math` increases, the mean `daysabs` decreases. However, as the model contains an interaction term, the effect of `math` on `daysabs` depends on the value of `prog`. Therefore, we will use `plot_predictions` to plot the conditional adjusted predictions for each level of `prog`." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n", - "plot_predictions(\n", - " model_interaction, \n", - " idata_interaction, \n", - " [\"math\", \"prog\"], \n", - " ax=ax, \n", - " pps=False\n", - ");" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Passing specific `subplot_kwargs` can allow for a more interpretable plot. Especially when the posterior predictive distribution plot results in overlapping credible intervals." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_predictions(\n", - " model_interaction, \n", - " idata_interaction, \n", - " covariates=[\"math\", \"prog\"],\n", - " pps=True,\n", - " subplot_kwargs={\"main\": \"math\", \"group\": \"prog\", \"panel\": \"prog\"},\n", - " legend=False,\n", - " fig_kwargs={\"figsize\": (16, 5), \"sharey\": True}\n", - ");" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Logistic Regression\n", - "\n", - "To further demonstrate the `plot_predictions` function, we will implement a logistic regression model. This example is taken from the marginaleffects `plot_predictions` [documentation](https://vincentarelbundock.github.io/marginaleffects/articles/predictions.html#prediction-type-or-scale). The internet movie database, http://imdb.com/, is a website devoted to collecting movie data supplied by studios and fans. It claims to be the biggest movie database on the web and is run by Amazon. The movies in this dataset were selected for inclusion if they had a known length and had been rated by at least one imdb user. The dataset below contains 28,819 rows and 24 columns. The variables of interest in the dataset are the following:\n", - "- title. Title of the movie.\n", - "- year. Year of release.\n", - "- budget. Total budget (if known) in US dollars\n", - "- length. Length in minutes.\n", - "- rating. Average IMDB user rating.\n", - "- votes. Number of IMDB users who rated this movie.\n", - "- r1-10. Multiplying by ten gives percentile (to nearest 10%) of users who rated this movie a 1.\n", - "- mpaa. MPAA rating.\n", - "- action, animation, comedy, drama, documentary, romance, short. Binary variables represent- ing if movie was classified as belonging to that genre." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Modeling the probability that certified_fresh==1\n", - "Auto-assigning NUTS sampler...\n", - "Initializing NUTS using adapt_diag...\n", - "Multiprocess sampling (4 chains in 4 jobs)\n", - "NUTS: [length, style, length:style]\n" - ] - }, - { - "data": { - "text/html": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we notice that the uncertainty in the conditional adjusted predictions is much larger than the uncertainty when `pps=False`. This is because the posterior predictive distribution accounts for the uncertainty in the model parameters and the uncertainty in the data. Whereas, the posterior distribution only accounts for the uncertainty in the model parameters.\n", "\n", - "\n" + "`plot_predictions` allows up to three covariates to be plotted simultaneously where the first element in the list represents the main (x-axis) covariate, the second element the group (hue / color), and the third element the facet (panel). However, when plotting more than one covariate, it can be useful to pass specific `group` and `panel` arguments to aid in the interpretation of the plot. Therefore, `subplot_kwargs` allows the user to manipulate the plotting by passing a dictionary where the keys are `{\"main\": ..., \"group\": ..., \"panel\": ...}` and the values are the names of the covariates to be plotted. For example, passing two covariates `hp` and `wt` and specifying `subplot_kwargs={\"main\": \"hp\", \"group\": \"wt\", \"panel\": \"wt\"}`. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } ], - "text/plain": [ - "" + "source": [ + "bmb.interpret.plot_predictions(\n", + " model=model, \n", + " idata=idata, \n", + " covariates=[\"hp\", \"wt\"],\n", + " pps=False,\n", + " legend=False,\n", + " subplot_kwargs={\"main\": \"hp\", \"group\": \"wt\", \"panel\": \"wt\"},\n", + " fig_kwargs={\"figsize\": (20, 8), \"sharey\": True}\n", + ")\n", + "plt.tight_layout();" ] }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Furthermore, categorical covariates can also be plotted. We plot the the mean `mpg` as a function of the two categorical covariates `gear` and `cyl` below. The `plot_predictions` function automatically plots the conditional adjusted predictions for each level of the categorical covariate. Furthermore, when passing a list of covariates into the `plot_predictions` function, the list will be converted into a dictionary object where the key is taken from (\"horizontal\", \"color\", \"panel\") and the values are the names of the variables. By default, the first element of the list is specified as the \"horizontal\" covariate, the second element of the list is specified as the \"color\" covariate, and the third element of the list is mapped to different plot panels." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n", + "bmb.interpret.plot_predictions(model, idata, [\"gear\", \"cyl\"], ax=ax);" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Negative Binomial Model\n", + "\n", + "Lets move onto a model that uses a distribution that is a member of the exponential distribution family and utilizes a link function. For this, we will implement the Negative binomial model from the students absences [example](https://bambinos.github.io/bambi/notebooks/negative_binomial.html#Negative-binomial-in-GLM). School administrators study the attendance behavior of high school juniors at two schools. Predictors of the number of days of absence include the type of program in which the student is enrolled and a standardized test in math. We have attendance data on 314 high school juniors. The variables of insterest in the dataset are the following:\n", "\n", - "
\n", - " \n", - " 100.00% [8000/8000 04:45<00:00 Sampling 4 chains, 0 divergences]\n", - "
\n", - " " + "- daysabs: The number of days of absence. It is our response variable.\n", + "- progr: The type of program. Can be one of ‘General’, ‘Academic’, or ‘Vocational’.\n", + "- math: Score in a standardized math test." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [daysabs_alpha, prog, scale(math), prog:scale(math)]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " 100.00% [8000/8000 00:02<00:00 Sampling 4 chains, 0 divergences]\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n" + ] + } ], - "text/plain": [ - "" + "source": [ + "# Load data, define and fit Bambi model\n", + "data = pd.read_stata(\"https://stats.idre.ucla.edu/stat/stata/dae/nb_data.dta\")\n", + "data[\"prog\"] = data[\"prog\"].map({1: \"General\", 2: \"Academic\", 3: \"Vocational\"})\n", + "\n", + "model_interaction = bmb.Model(\n", + " \"daysabs ~ 0 + prog + scale(math) + prog:scale(math)\",\n", + " data,\n", + " family=\"negativebinomial\"\n", + ")\n", + "idata_interaction = model_interaction.fit(\n", + " draws=1000, target_accept=0.95, random_seed=1234, chains=4\n", + ")" ] }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 286 seconds.\n" - ] - } - ], - "source": [ - "data = pd.read_csv(\"https://vincentarelbundock.github.io/Rdatasets/csv/ggplot2movies/movies.csv\")\n", - "\n", - "data[\"style\"] = \"Other\"\n", - "data.loc[data[\"Action\"] == 1, \"style\"] = \"Action\"\n", - "data.loc[data[\"Comedy\"] == 1, \"style\"] = \"Comedy\"\n", - "data.loc[data[\"Drama\"] == 1, \"style\"] = \"Drama\"\n", - "data[\"certified_fresh\"] = (data[\"rating\"] >= 8) * 1\n", - "data = data[data[\"length\"] < 240]\n", - "\n", - "priors = {\"style\": bmb.Prior(\"Normal\", mu=0, sigma=2)}\n", - "model = bmb.Model(\"certified_fresh ~ 0 + length * style\", data=data, priors=priors, family=\"bernoulli\")\n", - "idata = model.fit(random_seed=1234, target_accept=0.9, init=\"adapt_diag\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The logistic regression model uses a logit link function and a Bernoulli likelihood. Therefore, the link scale is the log-odds of a successful response and the response scale is the probability of a successful response." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - " Formula: certified_fresh ~ 0 + length * style\n", - " Family: bernoulli\n", - " Link: p = logit\n", - " Observations: 58662\n", - " Priors: \n", - " target = p\n", - " Common-level effects\n", - " length ~ Normal(mu: 0.0, sigma: 0.0708)\n", - " style ~ Normal(mu: 0.0, sigma: 2.0)\n", - " length:style ~ Normal(mu: [0. 0. 0.], sigma: [0.0702 0.0509 0.0611])\n", - "------\n", - "* To see a plot of the priors call the .plot_priors() method.\n", - "* To see a summary or plot of the posterior pass the object returned by .fit() to az.summary() or az.plot_trace()" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Again, by default, the `plot_predictions` function plots the mean outcome on the response scale. Therefore, the plot below shows the probability of a successful response `certified_fresh` as a function of `length`." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n", - "plot_predictions(model, idata, \"length\", ax=ax);" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Additionally, we can see how the probability of `certified_fresh` varies as a function of categorical covariates. " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n", - "plot_predictions(model, idata, \"style\", ax=ax);" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plotting other model parameters\n", - "\n", - "`plot_predictions` also has the argument `target` where `target` determines what parameter of the response distribution is plotted as a function of the explanatory variables. This argument is useful in distributional models, i.e., when the response distribution contains a parameter for location, scale and or shape. The default of this argument is `mean` and passing a parameter into `target` only works when the argument `pps=False` because when `pps=True` the posterior predictive distribution is plotted and thus, can only refer to the outcome variable (instead of any of the parameters of the response distribution). For this example, we will simulate our own dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Auto-assigning NUTS sampler...\n", - "Initializing NUTS using jitter+adapt_diag...\n", - "Multiprocess sampling (4 chains in 4 jobs)\n", - "NUTS: [Intercept, x, alpha_Intercept, alpha_x]\n" - ] - }, - { - "data": { - "text/html": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This model utilizes a log link function and a negative binomial distribution for the likelihood. Also note that this model also contains an interaction `prog:sale(math)`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " Formula: daysabs ~ 0 + prog + scale(math) + prog:scale(math)\n", + " Family: negativebinomial\n", + " Link: mu = log\n", + " Observations: 314\n", + " Priors: \n", + " target = mu\n", + " Common-level effects\n", + " prog ~ Normal(mu: [0. 0. 0.], sigma: [5.0102 7.4983 5.2746])\n", + " scale(math) ~ Normal(mu: 0.0, sigma: 2.5)\n", + " prog:scale(math) ~ Normal(mu: [0. 0.], sigma: [6.1735 4.847 ])\n", + " \n", + " Auxiliary parameters\n", + " alpha ~ HalfCauchy(beta: 1.0)\n", + "------\n", + "* To see a plot of the priors call the .plot_priors() method.\n", + "* To see a summary or plot of the posterior pass the object returned by .fit() to az.summary() or az.plot_trace()" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model_interaction" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n", + "bmb.interpret.plot_predictions(\n", + " model_interaction, \n", + " idata_interaction, \n", + " \"math\", \n", + " ax=ax, \n", + " pps=False\n", + ");" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The plot above shows that as `math` increases, the mean `daysabs` decreases. However, as the model contains an interaction term, the effect of `math` on `daysabs` depends on the value of `prog`. Therefore, we will use `plot_predictions` to plot the conditional adjusted predictions for each level of `prog`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n", + "bmb.interpret.plot_predictions(\n", + " model_interaction, \n", + " idata_interaction, \n", + " [\"math\", \"prog\"], \n", + " ax=ax, \n", + " pps=False\n", + ");" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Passing specific `subplot_kwargs` can allow for a more interpretable plot. Especially when the posterior predictive distribution plot results in overlapping credible intervals." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bmb.interpret.plot_predictions(\n", + " model_interaction, \n", + " idata_interaction, \n", + " covariates=[\"math\", \"prog\"],\n", + " pps=True,\n", + " subplot_kwargs={\"main\": \"math\", \"group\": \"prog\", \"panel\": \"prog\"},\n", + " legend=False,\n", + " fig_kwargs={\"figsize\": (16, 5), \"sharey\": True}\n", + ");" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Logistic Regression\n", "\n", - "\n" + "To further demonstrate the `plot_predictions` function, we will implement a logistic regression model. This example is taken from the marginaleffects `plot_predictions` [documentation](https://vincentarelbundock.github.io/marginaleffects/articles/predictions.html#prediction-type-or-scale). The internet movie database, http://imdb.com/, is a website devoted to collecting movie data supplied by studios and fans. It claims to be the biggest movie database on the web and is run by Amazon. The movies in this dataset were selected for inclusion if they had a known length and had been rated by at least one imdb user. The dataset below contains 28,819 rows and 24 columns. The variables of interest in the dataset are the following:\n", + "- title. Title of the movie.\n", + "- year. Year of release.\n", + "- budget. Total budget (if known) in US dollars\n", + "- length. Length in minutes.\n", + "- rating. Average IMDB user rating.\n", + "- votes. Number of IMDB users who rated this movie.\n", + "- r1-10. Multiplying by ten gives percentile (to nearest 10%) of users who rated this movie a 1.\n", + "- mpaa. MPAA rating.\n", + "- action, animation, comedy, drama, documentary, romance, short. Binary variables represent- ing if movie was classified as belonging to that genre." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Modeling the probability that certified_fresh==1\n", + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [length, style, length:style]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } ], - "text/plain": [ - "" + "source": [ + "data = pd.read_csv(\"https://vincentarelbundock.github.io/Rdatasets/csv/ggplot2movies/movies.csv\")\n", + "\n", + "data[\"style\"] = \"Other\"\n", + "data.loc[data[\"Action\"] == 1, \"style\"] = \"Action\"\n", + "data.loc[data[\"Comedy\"] == 1, \"style\"] = \"Comedy\"\n", + "data.loc[data[\"Drama\"] == 1, \"style\"] = \"Drama\"\n", + "data[\"certified_fresh\"] = (data[\"rating\"] >= 8) * 1\n", + "data = data[data[\"length\"] < 240]\n", + "\n", + "priors = {\"style\": bmb.Prior(\"Normal\", mu=0, sigma=2)}\n", + "model = bmb.Model(\"certified_fresh ~ 0 + length * style\", data=data, priors=priors, family=\"bernoulli\")\n", + "idata = model.fit(random_seed=1234, target_accept=0.9, init=\"adapt_diag\")" ] }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The logistic regression model uses a logit link function and a Bernoulli likelihood. Therefore, the link scale is the log-odds of a successful response and the response scale is the probability of a successful response." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " Formula: certified_fresh ~ 0 + length * style\n", + " Family: bernoulli\n", + " Link: p = logit\n", + " Observations: 58662\n", + " Priors: \n", + " target = p\n", + " Common-level effects\n", + " length ~ Normal(mu: 0.0, sigma: 0.0708)\n", + " style ~ Normal(mu: 0.0, sigma: 2.0)\n", + " length:style ~ Normal(mu: [0. 0. 0.], sigma: [0.0702 0.0509 0.0611])\n", + "------\n", + "* To see a plot of the priors call the .plot_priors() method.\n", + "* To see a summary or plot of the posterior pass the object returned by .fit() to az.summary() or az.plot_trace()" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, by default, the `plot_predictions` function plots the mean outcome on the response scale. Therefore, the plot below shows the probability of a successful response `certified_fresh` as a function of `length`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n", + "bmb.interpret.plot_predictions(model, idata, \"length\", ax=ax);" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Additionally, we can see how the probability of `certified_fresh` varies as a function of categorical covariates. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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XAqAIXHrg0bhx4xQVFeX4evTo0fr2228VFRWlxYsXq169eho7dqzLRQIAgOKLaVmzpEsAUAwuzaj/5z//0Z133ilJys7O1tKlSzV16lQtWbJEkyZNUlxcnFuKBAAAAEobl4J6RkaGbrvtNknS1q1b9dtvv+nhhx+WJLVq1UoHDx50uUAAAACgNHIpqNvtdu3du1eStHr1aoWEhCg4+I/1b2fPnpW3t7frFQIAAAClkEtr1Dt16qSXX35ZycnJmjt3rvr16+do2717t2rXru1qfQAAAECp5FJQ/9vf/qaDBw/qo48+UqtWrfTXv/7V0bZw4UK1adPG5QIBAACA0siloB4YGKhVq1YV2JaYmCg/Pz9Xdg8AAACUWm574NH58+d15MgRZWdnS5LKly8vHx8fd+0eAAAAKFVcDuqJiYmKiIhQuXLlFBISoh9//FGSNGzYMC1ZssTlAgEAAIDSyOUnk95///26cOGCXnjhBeXm5jraAgMDNXfuXFfrAwAAAEoll4L6uHHj1LlzZ23fvl2vvvqqU1uzZs20Y8cOV3YPAAAAlFouXUy6fft2LVq0SJJks9mc2qpUqaK0tDRXdg8AAACUWi7NqHt5eSkrK6vAtrS0NJUrV86V3QMAAACllktB/a677tKnn35aYNvixYsVERHhyu4BAACAUsulpS9jxozRAw88oG7duqlv376y2WzauHGjPvnkEy1evFiJiYnuqhMAAAAoVVwK6v/zP/+j2NhYjRgxQl9++aWkP27LeNttt2nu3Llq166dW4oEAAAASptiB/WcnBzt27dPXbp00aOPPqr169frxIkTCgwMVNu2bVW2bFl31gkAAACUKsUO6sYYNWrUSMuXL1d0dLQ6duzozroAAACAUq3YF5N6eXmpWrVqTg85AgAAAOAeLt31pVevXpo3b567agEAAADw/7l0MWnz5s31+eef67777lP37t1VvXr1fA8+6t69u0sFAgAAAKWRS0G9b9++kqQjR45o7dq1+dptNptycnJcOQQAAABQKrkU1LlPOgAAAHB9FDmoP//883ruuedUs2ZNhYaGqnr16vL29r4etQEAAAClVpEvJp0+fbqOHTsmSQoNDdX27dvdXhQAAABQ2hU5qFesWFEnTpyQ9Me91C+/eBQAAACA64q89OXuu+/WoEGD1KpVK0nSyJEjddtttxXY12az6csvv3SpQAAAAKA0KnJQnzVrlkaMGKHk5GTZbDalpKTI19e3wL7MtgMAAADFU+SgHhISoqVLl0qSPDw8tGzZMsfsOgAAAAD3cOnJpImJiWrUqFGh+hpjNGnSJB0/ftyVQwIAAAClgktBvX379goICChU39zcXE2cOFFHjx515ZAAAABAqeBSUC8qY8yNPBwAAABw07qhQR0AAABA4RDUAQAAAAsiqAMAAAAWRFAHAAAALIigDgAAAFgQQR0AAACwoBsW1D09PbV//341adLkmn0zMzM1YsQIBQUFyc/PT82bN9dnn31WqOOkpaWpf//+CgwMlL+/vyIiIpSQkFBg399++03jxo1T/fr15evrq8qVKysyMlJ79+4t0rkBAAAA7uZV1AGTJk0qdF+bzaaxY8c6vg4JCSnUuO7du2vz5s2aMmWK6tevr4ULF+qxxx5Tbm6uevfufcVxFy9eVMeOHXX69Gm98847stvtmjlzpjp16qTVq1erffv2jr6ZmZmKjIzU0aNHNWbMGDVt2lRnzpzR+vXrde7cuUKfIwAAAHA92EwRn0Lk4eE8CW+z2fI9yMhmszn+Oycnp0gFrVixQg8++KAjnOe5//77lZycrIMHD8rT07PAsbNmzdKwYcO0fv16RURESJKys7PVrFkzBQQEaOPGjY6+I0aM0Mcff6wff/xRt99+e5FqvJLk5GSFh4crKSlJjRs3dss+AQAAcGsoalYs8tKX3Nxcx2vPnj0KDQ3Va6+9pv379+v8+fPav3+/Jk+erNDQUO3evbvIJ7B06VIFBAQoJibGafuAAQN09OhRp7Bd0NgGDRo4QrokeXl56fHHH9emTZt05MgRSdK5c+f08ccfKyYmxm0hHQAAAHAnl9aoP/vss+rbt69eeuklhYSEyNfXVyEhIXr55ZfVp08fPfPMM0XeZ1JSksLCwuTl5bwqp2nTpo72q43N61fQ2OTkZEnS1q1b9dtvv6levXoaMmSIKlasKB8fH7Vs2VJff/11kWsGAAAA3M2loP7tt9+qbdu2Bba1bdtW//d//1fkfaanp6tSpUr5tudtS09Pd3ls3sz61KlTtXPnTs2bN09Lly5V+fLl9dBDD+mbb765Zp1paWlKTk52eqWkpFz7BAEAAIBCKPLFpJfy9fXVli1b1LFjx3xtW7ZskY+PT7H2e+ka96K0FXZsbm6uJMnHx0crV65UuXLlJEmRkZGqV6+eJk+erAceeOCqx5k1a5YmTpx41T4AAABAcbkU1Lt166aJEycqICBAvXv3VsWKFfXrr79qwYIFmjRpkv785z8XeZ+VK1cucNb81KlTklTgjHlRx1auXFmS1KZNG0dIlyR/f3+1b99ey5Ytu2adQ4cOzbeOPiUlRV27dr3mWAAAAOBaXArqb7/9tvbt26fhw4frmWeekZeXl7Kzs2WM0b333qu33367yPts0qSJ4uLilJ2d7bROfefOnZKk8PDwq47N63epy8cWtI49jzEm351tCmK322W326/ZDwAAACgOl9aolytXTmvWrNGKFSv0wgsvqG/fvho1apRWrlypxMREp9nqwurWrZsyMzP1xRdfOG2PjY1VUFCQWrdufdWxu3fvdrozTHZ2tubPn6/WrVsrKChIklS9enVFRETou+++U0ZGhqPvuXPntG7dOt19991FrhsAAABwJ5dm1PN06tRJnTp1cseuFB0draioKA0ZMkQZGRmqW7eu4uLitGrVKs2fP99xD/VBgwYpNjZW+/btczxIaeDAgZo5c6ZiYmI0ZcoU2e12zZo1S3v27NHq1audjvPmm28qMjJSDzzwgF588UXZbDa99dZb+uWXXzR58mS3nAsAAABQXC7NqOf55ptv9NJLL+mJJ57QwYMHJUmbN2/WyZMni7W/JUuWqE+fPho3bpw6deqkjRs3Ki4uzmnNe05OjnJycpwetuTr66uEhARFRkZq+PDheuihh3Ts2DGtXLnS6amk0h/r0xMSEuTr66s///nP6t27t7y9vbV27Vqn+7ADuPks2nJIf4//jxZtOVTSpQAAUGxFfjLppc6dO6dHHnlECQkJjjuqbN68WXfeead69uypmjVr6s0333RbsVbHk0kBa+j54QZt3H9KrUMr6fMn+cMbAGAN1/3JpJd65ZVXtGXLFn3xxRc6c+aM0+z2/fffn2+5CQAAAIDCcWmN+qJFizR58mR169ZNOTk5Tm21atVyLIMBAAAAUDQuzaifPHnyitP2Hh4eOn/+vCu7BwAAAEotl4J6jRo1CrxvuST9+OOPCg0NdWX3AAAAQKnlUlDv3r27XnvtNW3fvt2xzWaz6cCBA/r73/+e78mdAAAAAArHpaA+fvx4BQUFqVWrVmrZsqVsNpsGDBig8PBw2e12jRkzxl11AkChZOXkKuNCliQp40KWsnJyS7giAACKx+Unk65fv16TJ09WQECA6tSpI39/f7300kv697//rTJlyrirTgC4qqycXL2bsFcRrydo17GzkqRdx86qzetr9G7CXgI7AOCm4/KTScuUKaMxY8Ywew6gxGTl5GrwvC1K3HNStsvafsm8qLfj/6Mdh07rwz4t5O3plue8AQBw3fGJBeCm9/7afUrc88eTkC9/glve12t2p+mDtftuaF0AALiiyDPqAwcO1NixYxUaGqqBAwdeta/NZtPs2bOLXRwAXEtWTq7mbUiVTflD+qVskuZtOKCnOtRhVh0AcFMoclBPTEzUs88+K0las2aNbLbL/6H5v67WBgDusCX1V/2S+fs1+xlJJzMvakvqr4qoU/n6FwYAgIuKHNT379/v+O/U1FR31gIARXbm/LVDuiv9AQAoKUX+998777xTycnJkqR58+YpPT3d7UUBQGFVKONzXfsDAFBSihzUf/zxR2VmZkqSBgwYoH37uDgLQMlpWbuiAgN88t3t5XI2SVUCfNWydsUbURYAAC4rclC32+3atm2bJMkYwzp0ACXK29NDfSNqX/VCUumPNep9I0K4kBQAcNMo8ifWww8/rGHDhikgIEA2m02RkZEqX758ga8KFSpcj5oBwMmQDnV0X0O7JOWbWc/7+r6Gdj3Voc4NrQsAAFcU+WLSGTNmqFGjRtq5c6c++eQTdejQQVWqVLketQFAoXh7eujDPi30wdp9mrfhgE5mXnS0BQb4qm9ECLdlBADcdGzGmGv9i/EVeXh46Pvvv1erVq3cWdNNKzk5WeHh4UpKSlLjxo1LuhygVMrKydXD7/2fdh07q7Dq5fTV0+0I6AAASyhqVizyjPqlcnNzXRkOAG7n7emh8n7ekqTyft6EdADATYtPMAAAAMCCihzUPT09tWnTpj8Ge3jI09Pzii8vL5cm7AEAAIBSq8hJety4cQoODnb8N7dnBAAAANyvyEF9/Pjxjv+eMGGCO2sBAAAA8P+5tEZ90qRJOnr0aIFtx44d06RJk1zZPQAUS48WwXq2Yz31aBFc0qUAAFBsLgX1iRMn6vDhwwW2HT16VBMnTnRl9wBQLDEta+q5qPqKaVmzpEsBAKDYXArqV7sFe2Zmpry9vV3ZPQAAAFBqFXmN+o8//qgdO3Y4vl6xYoV2797t1Of8+fNasGCB6tThcd0AAABAcRQ5qC9dutSxpMVms11xHXqZMmU0Z84c16oDAAAASqkiB/XBgwerS5cuMsaoVatWmjNnjsLDw536+Pr6qk6dOipTpozbCgUAAABKkyIH9erVq6t69er6/ffftXLlStWoUSNfUAcAAADgmmJfTOrl5aWHHnpIJ06ccGc9AAAAAORCUPfw8FBwcLAyMjLcWQ8AAAAAuXh7xkGDBmnmzJnKyclxVz0AAAAAVIw16pfy8fHRnj17FBYWpocffljVq1eXzWZztNtsNj333HMuFwkAAACUNi4F9RdffNHx32+//Xa+doI6AAAAUDwuBfX9+/e7qw4AAAAAl3ApqIeEhLirDgAAAACXcCmo59m9e7fWrVunX375RYMGDVK1atV09OhRVaxYkYceAQAAAMXgUlDPycnR4MGDNXfuXBljZLPZFB0drWrVqunJJ5/UHXfcoUmTJrmrVgAAAKDUcOn2jK+99poWLlyoN954Q0lJSTLGONqio6O1atUqlwsEAAAASiOXZtTnzp2rsWPH6vnnn893L/XQ0FAuNgUAAACKyaUZ9SNHjigiIqLANj8/P509e9aV3QMAAACllktB3W636+effy6wbc+ePQoODnZl9wAAAECp5VJQ79y5s1577TUdOXLEsc1ms+nMmTN699139dBDD7lcIAAAAFAauRTUJ02apOzsbDVq1EiPPvqobDabXn75ZYWHh+vChQsaO3asu+oEAAAAShWXgnrVqlW1efNmPfbYY9q6das8PT31ww8/KDo6WuvXr1elSpXcVScAAABQqrh015esrCwFBATogw8+yNf222+/KSsrS97e3q4cAgAAACiVXJpRf+KJJ/SXv/ylwLbBgwdryJAhruweAAAAKLVcCuqJiYl6+OGHC2x76KGHlJCQ4MruAQAAgFLLpaB+4sQJVa9evcC2atWq6fjx467sHgAAACi1XArqt912m1JSUgpsS0lJUbly5VzZPQAAAFBquRTUIyMj9frrr+vUqVNO20+dOqUpU6bovvvuc6k4AAAAoLRy6a4vEyZM0F133aV69eqpZ8+eqlGjhg4fPqxFixYpKytLEydOdFedAAAAQKniUlBv0KCBvv32Wz3//PP66KOPlJOTI09PT7Vv315vv/22GjRo4K46AQAAgFLFpaUvktSsWTMlJCQoIyNDhw8f1tmzZ7V69Wo1bdq02PvMzMzUiBEjFBQUJD8/PzVv3lyfffZZocampaWpf//+CgwMlL+/vyIiIq5595nz58+rfv36stlsevPNN4tdNwAAAOAuLs2oX6pMmTIqU6aMW/bVvXt3bd68WVOmTFH9+vW1cOFCPfbYY8rNzVXv3r2vOO7ixYvq2LGjTp8+rXfeeUd2u10zZ85Up06dtHr1arVv377AcWPHjtVvv/3mltoBAAAAd3BbUHeXFStWKD4+3hHOpT8uWj1w4IBGjRqlnj17ytPTs8Cxs2fPVlJSktavX6+IiAjH2GbNmmn06NHauHFjvjGbNm3SjBkztGDBAsXExFy/EwMAAACKwOWlL+62dOlSBQQE5AvNAwYM0NGjRwsM25eObdCggSOkS5KXl5cef/xxbdq0SUeOHHHq//vvv2vgwIEaNmyYWrZs6d4TAQAAAFxguaCelJSksLAweXk5T/bnrXlPSkq66tiC1sbnbUtOTnbaPmnSJP3222+aPHmyq2UDAAAAbmW5pS/p6em6/fbb822vVKmSo/1qY/P6XWvsjh07NG3aNC1fvlxly5bVyZMni1RnWlpavjFXevgTAAAAUFSWC+qSZLPZitVW2LHZ2dkaOHCgevbsqQceeKBYNc6aNYv7xAMAAOC6sVxQr1y5coGz5nlPPy1oxryoY6dPn66ff/5Z//u//6vTp09LkjIyMiRJFy5c0OnTp1WuXLkrXrQqSUOHDs23jj4lJUVdu3a98skBAAAAhWS5oN6kSRPFxcUpOzvbaZ36zp07JUnh4eFXHZvX71KXj01KStKZM2dUr169fH3Hjh2rsWPHavv27WrevPkVj2W322W32wt1TgAAAEBRWe5i0m7duikzM1NffPGF0/bY2FgFBQWpdevWVx27e/dupzvDZGdna/78+WrdurWCgoIkSWPGjFFiYqLTKy4uTpL01FNPKTExUXXr1r0OZwcAAAAUjuVm1KOjoxUVFaUhQ4YoIyNDdevWVVxcnFatWqX58+c7lqMMGjRIsbGx2rdvn0JCQiRJAwcO1MyZMxUTE6MpU6bIbrdr1qxZ2rNnj1avXu04RsOGDdWwYUOn46ampkqS6tSpow4dOtyQcwUAAACuxHJBXZKWLFmiV155RePGjdOpU6fUsGFDxcXFqVevXo4+OTk5ysnJkTHGsc3X11cJCQkaPXq0hg8frnPnzql58+ZauXLlFZ9KCgAAAFiRzVyadOGS5ORkhYeHKykpSY0bNy7pcgAAAGAhRc2KllujDgAAAICgDgAAAFiSJdeoo/AWbTmkw7+eV3DFMoppWbOkywEAAICbMKN+k1u89bDeSdirxVsPl3QpAAAAcCOCOgAAAGBBBHUAAADAggjqAAAAgAUR1AEAAAALIqgDAAAAFkRQBwAAACyIoA4AAABYEEEdAAAAsCCCOgAAAGBBBHUAAADAggjqAAAAgAUR1AEAAAALIqgDAAAAFkRQBwAAACyIoA4AAABYEEEdAAAAsCCCOgAAAGBBBPWbWFZOrjIuZEmSMi5kKSsnt4QrAgAAgLsQ1G9CWTm5ejdhryJeT9CuY2clSbuOnVWb19fo3YS9BHYAAIBbgFdJF4CiycrJ1eB5W5S456Rsl7X9knlRb8f/RzsOndaHfVrI25O/wwAAAG5WJLmbzPtr9ylxz0lJkrmsLe/rNbvT9MHafTe0LgAAALgXQf0mkpWTq3kbUvPNpF/OJmnehgMsgQEAALiJEdRvIltSf9Uvmb/nm0m/nJF0MvOitqT+eiPKAgAAwHVAUL+JnDn/+3XtDwAAAOsgqN9EKpTxua79AQAAYB0E9ZtIy9oVFRjgU6g16lUCfNWydsUbURYAAACuA4L6TcTb00N9I2oXao1634gQbs8IAABwEyPJ3WSGdKij+xraJSnfzHre1/c1tOupDnVuaF0AAABwL4L6Tcbb00Mf9mmhkVH1FRjg69QWGOCrkVH1edgRAADALYAnk96EvD09NLxjPT3VoY4efu//tOvYWYVVL6evnm5HQAcAALhFkOpuYt6eHirv5y1JKu/nTUgHAAC4hZDsAAAAAAsiqAMAAAAWRFAHAAAALIigDgAAAFgQQR0AAACwIII6AAAAYEEEdQAAAMCCCOoAAACABRHUAQAAAAsiqAMAAAAWRFAHAAAALIigDgAAAFgQQR0AAACwIII6AAAAYEEEdQAAAMCCCOoAAACABRHUAQAAAAsiqAMAAAAWZMmgnpmZqREjRigoKEh+fn5q3ry5Pvvss0KNTUtLU//+/RUYGCh/f39FREQoISHBqU9GRoZee+01dejQQdWqVVNAQICaNGmiqVOn6sKFC9fjlK6bHi2C9WzHeurRIrikSwEAAIAbeZV0AQXp3r27Nm/erClTpqh+/fpauHChHnvsMeXm5qp3795XHHfx4kV17NhRp0+f1jvvvCO73a6ZM2eqU6dOWr16tdq3by9JOnjwoKZPn64+ffro+eefV0BAgL799ltNmDBB8fHxio+Pl81mu1Gn65KYljVLugQAAABcB5YL6itWrFB8fLwjnEtSZGSkDhw4oFGjRqlnz57y9PQscOzs2bOVlJSk9evXKyIiwjG2WbNmGj16tDZu3ChJCg0NVWpqqsqWLesYe99996ls2bIaNWqUvvvuO7Vr1+46nykAAABwZZZb+rJ06VIFBAQoJibGafuAAQN09OhRR9i+0tgGDRo4QrokeXl56fHHH9emTZt05MgRSVLZsmWdQnqeVq1aSZIOHTrkjlMBAAAAis1yQT0pKUlhYWHy8nKe7G/atKmj/Wpj8/oVNDY5Ofmqx16zZo0kqXHjxkWqGQAAAHA3yy19SU9P1+23355ve6VKlRztVxub16+oY3/88UdNmzZN3bp1KzDsXy4tLU0nT5502paSknLNcQAAAEBhWC6oS7rqhZzXusizOGNTU1PVpUsX1axZUx9//HGhapw1a5YmTpxYqL4AAABAUVkuqFeuXLnAme9Tp05JUoEz5q6MPXDggCIjI+Xl5aWEhISr7v9SQ4cOzbeOPiUlRV27di3UeAAAAOBqLBfUmzRpori4OGVnZzutU9+5c6ckKTw8/Kpj8/pd6kpjDxw4oA4dOsgYo7Vr1yo4uPD3Irfb7bLb7YXuDwAAABSF5S4m7datmzIzM/XFF184bY+NjVVQUJBat2591bG7d+92ujNMdna25s+fr9atWysoKMix/eDBg+rQoYNycnK0Zs0ahYSEuP9kAAAAgGKy3Ix6dHS0oqKiNGTIEGVkZKhu3bqKi4vTqlWrNH/+fMc91AcNGqTY2Fjt27fPEbIHDhyomTNnKiYmRlOmTJHdbtesWbO0Z88erV692nGMtLQ0RUZG6tixY5o9e7bS0tKUlpbmaA8ODi7S7DoAAADgbpYL6pK0ZMkSvfLKKxo3bpxOnTqlhg0bKi4uTr169XL0ycnJUU5Ojowxjm2+vr5KSEjQ6NGjNXz4cJ07d07NmzfXypUrHU8llaSffvpJP//8syTp8ccfz3f88ePHa8KECdfvBAEAAIBrsJlLky5csm3bNrVo0ULLli1T3bp1S7ocAAAAWEjejUe2bt2qO++885r9LTmjfrPKe6Ipd34BAADAlRw6dKhQQZ0ZdTc6ffq01q1bp5o1a8rX1/eGHDPvLzNm8YH/4ucCcMbPBOCspH4mLl68qEOHDql9+/a67bbbrtmfGXU3uu222/TII4+UyLHr1q2rxo0bl8ixAavi5wJwxs8E4KwkfiYKM5Oex3K3ZwQAAABAUAcAAAAsiaAOAAAAWBBB/SZXpUoVjR8/XlWqVCnpUgDL4OcCcMbPBODsZvmZ4K4vAAAAgAUxow4AAABYEEEdAAAAsCCCOgAAAGBBBPUb6N1335XNZlN4eHixxh89elQTJkzQjh078rVNmDBBNpvNxQoB9/rxxx81YMAAhYaGys/PTwEBAbrzzjs1bdo0nTp1qqTLK5K1a9fKZrNp7dq1JV0KbiFz586VzWZzvPz8/FStWjVFRkbq9ddfV1paWkmXCFjG999/r5iYGFWvXl0+Pj6qVq2aevTooQ0bNjj1W79+vSZMmKDTp0/n20ft2rXVpUuXG1Sx6wjqN9Ann3wiSUpOTtbGjRuLPP7o0aOaOHFigUH9L3/5S743KlCSPvroI7Vo0UKbN2/WqFGjtGrVKi1dulQxMTH64IMPNGjQoJIuEbCMOXPmaMOGDYqPj9fMmTPVvHlzTZ06VWFhYVq9enVJlweUuBkzZqht27Y6fPiwpk2bptWrV+vNN9/UkSNH1K5dO7333nuOvuvXr9fEiRMLDOo3G6+SLqC02LJli3744Qc9+OCD+vrrrzV79my1bt3abfsPDg5WcHCw2/YHuGLDhg0aMmSIoqKitGzZMvn6+jraoqKiNHLkSK1ataoEKwSsJTw8XC1btnR8/eijj+q5555Tu3bt1L17d+3du1dVq1YtcOy5c+fk7+9/o0oFbrjvvvtOI0aMUOfOnbV06VJ5ef03vvbq1UvdunXTs88+qzvuuENt27YtwUr/kJOTo+zsbKfPvuJiRv0GmT17tiRpypQpatOmjT777DOdO3fOqc+RI0c0ePBg1axZUz4+PgoKClKPHj104sQJrV27VnfddZckacCAAY5/Jp0wYYKkgpe+5Obmatq0aWrYsKF8fX1lt9vVt29fHT582Klfhw4dFB4ers2bN+uee+6Rv7+/br/9dk2ZMkW5ubnX6TuCW9nf/vY32Ww2/eMf/yjwF5WPj48efvhhSUV/n27YsEFt2rRRmTJlVLt2bc2ZM0eS9PXXX+vOO++Uv7+/mjRpUuAfAnv37lXv3r1lt9vl6+ursLAwzZw5M1+/3bt3q1OnTvL391dgYKCeeuopnT171qnP5MmT5eXlpUOHDuUbP3DgQFWuXFkXLlwo/DcNuEytWrX01ltv6ezZs/rwww8lSf3791dAQIB27typ+++/X+XKlVPHjh0lSfHx8XrkkUcUHBwsPz8/1a1bV08++aR++eUXp/3mfV78+OOPiomJUYUKFVSpUiU9//zzys7O1p49e9SpUyeVK1dOtWvX1rRp05zGX7hwQSNHjlTz5s0dYyMiIvTll1/emG8MSp3XX39dNptN77//vlNIlyQvLy/NmjVLNptNU6ZM0YQJEzRq1ChJUmhoqCMvXb5scdWqVbrzzjtVpkwZNWzY0LHq4VLHjx/Xk08+qeDgYPn4+Cg0NFQTJ05Udna2o09qaqpsNpumTZumV199VaGhofL19VViYqJ7Tt7gujt37pypUKGCueuuu4wxxnz88cdGkpk7d66jz+HDh0316tVNYGCgefvtt83q1avN559/bgYOHGh27dplzpw5Y+bMmWMkmb/+9a9mw4YNZsOGDebQoUPGGGPGjx9vLv+/c/DgwUaSefrpp82qVavMBx98YKpUqWJq1qxpTp486ejXvn17U7lyZVOvXj3zwQcfmPj4eDN06FAjycTGxt6A7xBuJdnZ2cbf39+0bt26UP2L+j5t0KCBmT17tvnmm29Mly5djCQzceJE06RJExMXF2dWrFhh7r77buPr62uOHDniGJ+cnGwqVKhgmjRpYubNm2f+9a9/mZEjRxoPDw8zYcIER7/jx48bu91uatSoYebMmWNWrFhh/vznP5tatWoZSSYxMdEYY8yJEyeMr6+veeWVV5zOJz093ZQpU8aMGjXKhe8iSou83+ubN28usD0zM9N4enqajh07GmOM6devn/H29ja1a9c2r7/+uklISDDffPONMcaY999/37z++uvmq6++MuvWrTOxsbGmWbNmpkGDBub333937DPv86JBgwZm8uTJJj4+3owePdrxc9iwYUPz7rvvmvj4eDNgwAAjyXzxxReO8adPnzb9+/c3n376qVmzZo1ZtWqVeeGFF4yHhwefGXC7wn6mtGrVyvj7+5vU1FQzfPhwI8ksWbLEkZfOnDljjDEmJCTEBAcHm0aNGpl58+aZb775xsTExBhJZt26dY79HTt2zNSsWdOEhISYDz/80KxevdpMnjzZ+Pr6mv79+zv67d+/30gyNWrUMJGRkWbx4sXmX//6l9m/f79bzp+gfgPMmzfPSDIffPCBMcaYs2fPmoCAAHPPPfc4+gwcONB4e3ubn3766Yr72bx5s5Fk5syZk6/t8qC+a9cuI8kMHTrUqd/GjRuNJPPyyy87trVv395IMhs3bnTq26hRI/PAAw8U6VyB48ePG0mmV69e1+xbnPfpli1bHNvS09ONp6enKVOmjFMo37Fjh5Fk3n33Xce2Bx54wAQHBzt+Wed5+umnjZ+fnzl16pQxxpgXX3zR2Gw2s2PHDqd+UVFRTkHdmD9Ck91uNxcvXnRsmzp1qvHw8HDbL2nc2q4V1I0xpmrVqiYsLMwY88d7TpL55JNPrrrf3Nxck5WVZQ4cOGAkmS+//NLRlvd58dZbbzmNad68uSPc5MnKyjJVqlQx3bt3v+KxsrOzTVZWlhk0aJC54447rloXUFSF/Uzp2bOnkWROnDhh3njjDSOpwN/DISEhxs/Pzxw4cMCx7fz586ZSpUrmySefdGx78sknTUBAgFM/Y4x58803jSSTnJxsjPlvUK9Tp47TH8TuwtKXG2D27NkqU6aMevXqJUkKCAhQTEyMvv32W+3du1eStHLlSkVGRiosLMwtx8z7J5f+/fs7bW/VqpXCwsKUkJDgtL1atWpq1aqV07amTZvqwIEDbqkHKEhR36fVq1dXixYtHF9XqlRJdrtdzZs3V1BQkGN73s9R3vv3woULSkhIULdu3eTv76/s7GzHq3Pnzrpw4YK+//57R02NGzdWs2bNnI7du3fvfPU/++yzSktL06JFiyT9sYzn/fff14MPPqjatWsX4zsC5GcKeID4o48+mm9bWlqannrqKdWsWVNeXl7y9vZWSEiIJGnXrl35+l9+54uwsDDZbDZFR0c7tnl5ealu3br5PgsWLVqktm3bKiAgwHGs2bNnF3gc4EbI+zkpzB3wmjdvrlq1ajm+9vPzU/369Z3e5//85z8VGRmpoKAgp8+MvJ+PdevWOe3z4Ycflre3tztOxQlB/TpLSUnRv//9bz344IMyxuj06dM6ffq0evToIem/d4I5efKkWy8GTU9Pl/RHsLlcUFCQoz1P5cqV8/Xz9fXV+fPn3VYTSofAwED5+/tr//791+xb1PdppUqV8vXz8fHJt93Hx0eSHGvE09PTlZ2drRkzZsjb29vp1blzZ0lyrONNT09XtWrV8h2noG133HGH7rnnHsc693/+859KTU3V008/ffUTBwrpt99+U3p6utMfov7+/ipfvrxTv9zcXN1///1asmSJRo8erYSEBG3atMnxB2hBv8sL+rnx9/eXn59fvu2XXm+xZMkS/elPf1KNGjU0f/58bdiwQZs3b9bAgQO5LgNuV9jPlNTUVPn7+xf4OXG5wmSeEydOaPny5fk+Mxo3bixJ+a79KOhzzB2468t19sknn8gYo8WLF2vx4sX52mNjY/Xqq6+qSpUq+S6ec0Xem/DYsWP5/gA4evSoAgMD3XYs4FKenp7q2LGjVq5cqcOHD1/1D9Ab9T6tWLGiPD091adPHw0bNqzAPqGhoY6ajh8/nq+9oG2S9MwzzygmJkbbtm3Te++9p/r16ysqKsotdQNff/21cnJy1KFDB8e2gmYMk5KS9MMPP2ju3Lnq16+fY3tKSorba5o/f75CQ0P1+eefO9Vy8eJFtx8L8PT0VGRkpFatWnXFz5TDhw9r69atio6Olqenp1uOGxgYqKZNm+q1114rsP3SP56lws3kFwcz6tdRTk6OYmNjVadOHSUmJuZ7jRw5UseOHdPKlSsVHR2txMRE7dmz54r7y7t7RmFmue+77z5Jf/xCvdTmzZu1a9cux10CgOvhpZdekjFGTzzxhH7//fd87VlZWVq+fPkNe5/6+/srMjJS27dvV9OmTdWyZct8r7w/GiIjI5WcnKwffvjBaR8LFy4scN/dunVTrVq1NHLkSK1evVpDhw7l4WNwi4MHD+qFF15QhQoV9OSTT161b9577vK7LOXdLcadbDabfHx8nN7nx48f564vuG7yPlOGDh2qnJwcp7acnBwNGTJExhi99NJLkoqWl66kS5cuSkpKUp06dQr8zLg8qF8vzKhfRytXrtTRo0c1depUp9mQPOHh4Xrvvfc0e/Zsvffee1q5cqXuvfdevfzyy2rSpIlOnz6tVatW6fnnn1fDhg1Vp04dlSlTRgsWLFBYWJgCAgIUFBRU4JulQYMGGjx4sGbMmCEPDw9FR0crNTVVY8eOVc2aNfXcc8/dgO8ASquIiAi9//77Gjp0qFq0aKEhQ4aocePGysrK0vbt2/WPf/xD4eHhWrp06Q17n77zzjtq166d7rnnHg0ZMkS1a9fW2bNnlZKSouXLl2vNmjWSpBEjRuiTTz7Rgw8+qFdffVVVq1bVggULtHv37gL36+npqWHDhunFF19U2bJl8623BwojKSnJsQY2LS1N3377rebMmSNPT08tXbpUVapUuer4vM+IMWPGyBijSpUqafny5YqPj3d7rV26dNGSJUs0dOhQ9ejRQ4cOHdLkyZNVvXp1x3VXgDu1bdtW06dP14gRI9SuXTs9/fTTqlWrlg4ePKiZM2dq48aNmj59utq0aSNJatKkiaQ/fu/369dP3t7eatCggcqVK1foY06aNEnx8fFq06aNnnnmGTVo0EAXLlxQamqqVqxYoQ8++ODGPL/G7ZenwqFr167Gx8fHpKWlXbFPr169jJeXlzl+/Lg5dOiQGThwoKlWrZrx9vY2QUFB5k9/+pM5ceKEo39cXJxp2LCh8fb2NpLM+PHjjTEF354xJyfHTJ061dSvX994e3ubwMBA8/jjjztu6Zinffv2pnHjxvlq69evnwkJCSn+NwCl3o4dO0y/fv1MrVq1jI+Pjylbtqy54447zLhx4xw/F66+T0NCQsyDDz6Yb7skM2zYMKdt+/fvNwMHDjQ1atQw3t7epkqVKqZNmzbm1Vdfder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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n", + "bmb.interpret.plot_predictions(model, idata, \"style\", ax=ax);" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting other model parameters\n", "\n", - "
\n", - " \n", - " 100.00% [8000/8000 00:02<00:00 Sampling 4 chains, 25 divergences]\n", - "
\n", - " " + "`plot_predictions` also has the argument `target` where `target` determines what parameter of the response distribution is plotted as a function of the explanatory variables. This argument is useful in distributional models, i.e., when the response distribution contains a parameter for location, scale and or shape. The default of this argument is `mean` and passing a parameter into `target` only works when the argument `pps=False` because when `pps=True` the posterior predictive distribution is plotted and thus, can only refer to the outcome variable (instead of any of the parameters of the response distribution). For this example, we will simulate our own dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Auto-assigning NUTS sampler...\n", + "Initializing NUTS using jitter+adapt_diag...\n", + "Multiprocess sampling (4 chains in 4 jobs)\n", + "NUTS: [Intercept, x, alpha_Intercept, alpha_x]\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n", + "There were 25 divergences after tuning. Increase `target_accept` or reparameterize.\n" + ] + } ], - "text/plain": [ - "" + "source": [ + "rng = np.random.default_rng(121195)\n", + "N = 200\n", + "a, b = 0.5, 1.1\n", + "x = rng.uniform(-1.5, 1.5, N)\n", + "shape = np.exp(0.3 + x * 0.5 + rng.normal(scale=0.1, size=N))\n", + "y = rng.gamma(shape, np.exp(a + b * x) / shape, N)\n", + "data_gamma = pd.DataFrame({\"x\": x, \"y\": y})\n", + "\n", + "formula = bmb.Formula(\"y ~ x\", \"alpha ~ x\")\n", + "model = bmb.Model(formula, data_gamma, family=\"gamma\")\n", + "idata = model.fit(random_seed=1234)" ] }, - "metadata": {}, - "output_type": "display_data" + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " Formula: y ~ x\n", + " alpha ~ x\n", + " Family: gamma\n", + " Link: mu = inverse\n", + " alpha = log\n", + " Observations: 200\n", + " Priors: \n", + " target = mu\n", + " Common-level effects\n", + " Intercept ~ Normal(mu: 0.0, sigma: 2.5037)\n", + " x ~ Normal(mu: 0.0, sigma: 2.8025)\n", + " target = alpha\n", + " Common-level effects\n", + " alpha_Intercept ~ Normal(mu: 0.0, sigma: 1.0)\n", + " alpha_x ~ Normal(mu: 0.0, sigma: 1.0)\n", + "------\n", + "* To see a plot of the priors call the .plot_priors() method.\n", + "* To see a summary or plot of the posterior pass the object returned by .fit() to az.summary() or az.plot_trace()" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model we defined uses a `gamma` distribution parameterized by `alpha` and `mu` where `alpha` utilizes a log link and `mu` goes through an inverse link. Therefore, we can plot either: (1) the `mu` of the response distribution (which is the default), or (2) `alpha` of the response distribution as a function of the explanatory variable $x$. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# First, the mean of the response (default)\n", + "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n", + "bmb.interpret.plot_predictions(model, idata, \"x\", ax=ax);" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below, instead of plotting the default target, `target=mean`, we set `target=alpha` to visualize how the model parameter `alpha` varies as a function of the `x` predictor." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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bAschL4P3o48+ir/4i7+A1WrFa6+9hu9+97sIBAJ45JFHRr2eRCKZ1OeAZNjfuHHjoGMtLS3YsGHDuOsmIiKi0hMXEjjb7cX5Hi83w5kGkZiA91rteO+SfdAJqotnluMPFs+ApVydw+omJi+D95w5czBnzhwAwFe/+lUAwA9+8AN84xvfQFVV1bDXMZvNw65qO51OABh2NXwgi8UCi8WSSdlERERUYjqdQZy+4uKW79MgLiTwQbsTb1+wIhC9dv/eUKlD05JqzDZpc1jd5ORl8L7e7bffjv/9v/83Ll++PGLwXrp0Kc6cOTPkeP+x+vr6aa2RiIiISocvHMPHHS50u8O5LqXoJEQRn3a6cfR8H1zBWPp4TYUaf7CkGjdZ9GN2MuSrggjeb7/9NqRSKebNmzfiZe69915s2bIFp06dwooVKwAkxwkePHgQK1asQE1NTbbKJSIioiIlJESc7fbgfI8XAsdyT6mEKOJstxdHzvfB5oukj5t0Snxl8Qwsra2AtEADd7+8Ct5/9Vd/hfLyctx+++2YMWMG7HY7Dh8+jJdffhnf//7306vdmzdvxoEDB9Da2oq6ujoAwEMPPYQ9e/Zg48aN2L17NywWC/bu3YuLFy/iyJEjubxZREREVAS63CF81O5kW8kUE0URF3t9eOt8H3o81/6CUKaS465FFnxxrgkyaWEH7n55FbxXrlyJffv24cCBA3C73dDr9WhoaMDPf/5zbNq0KX05QRAgCAIG7v2jUqlw9OhRbN26FQ8//DCCwSBuueUWvP7669y1koiIiCbNH4njo3Yn20qmmCiKaLUF8Na5XnS6QunjWqUMqxdUYcUNZijlBbHlzLgVxM6VucKdK4mIiEqXkBBxrtuLcz0etpVMsQ5HAP9+rm/QBkNqhRR3zK/Cl280Q6WQTfhrrrvZkvUJJxPNinm14k1ERESUDzqdQXzS6YY/zJncU+mqK4gj5/vwed+1DYaUMim+dKMZd9xUCa2yuKNpcd86IiIionESEiLa7AFc6PVyE5wp1usJ48j5Ppzr8aaPyaUSrLjBhNULLdCrSiOSlsatJCIiIhpBOCagxerH530+hGPsKZlKvd4wfnfBiuYuT/qYTCLB8rlGrFloQYVGkcPqso/Bm4iIiEqSLxzDhV4f2mwBxLnl5JQaLnBLANw6x4i7Fllg0ilzV1wOMXgTERFRSbH6wrjQ40OXOwSOmJhafQMCd/9dKwFwy2wD7lpoQWWZKpfl5RyDNxERERU9URRx1RXC+R4v7P5orsspOgzc48PgTUREREXNFYjiRKsDnlBs7AvThDBwTwyDNxERERWt8z1e/L7TDbZwT62RAndDKnBXMXAPi8GbiIiIik4oKuDkZcegLcgpc93uEN6+aMXZ7mtjARm4x4/Bm4iIiIpKlzuEk60OROIcDThVrjiDeOeiFRd6feljDNwTx+BNRERERUFIiDh9xYVLA3ZFpMxctvvxzgUbWmzX7lOpJNnDvXoBA/dEMXgTERFRwXMHozjewhMop4Ioimix+vH2RSvaHcH0cZlUgtvmGLFqQVXJzuHOFIM3ERERFbSLvT582umCwM6SjIiiiAu9Prx90YqrrlD6uFwqwe03mHDnTVUlt9PkVGPwJiIiooIUjiVPoOx28wTKTCREEWe7vXj7ghW93mv3pVIuReMNZnx5vhllagbuqcDgTURERAWnyx3CqcsOhGNc5p6suJDAJ51uvPu5DY7AtU2F1AopvnRjJb40zwytilFxKvHeJCIiooLhCcXwyRUXV7kzEIkJ+KDdifda7PCF4+njWqUMd8yvROM8M9QKWQ4rLF4M3kRERJT3wjEBzV0etFj93AxnkvyRON5vteP96/5SUKFR4M6bKrG8zgSlXJrDCosfgzcRERHlrURCxMU+H5q7PIgJTNyT4QpG8d4lOz7qcA66D6vKVFh9UxWWza6AXMrAnQ0M3kRERJSXrjiC+PSqG/4B7RA0fn3eMN793IbfX3UP+ivBLKMGaxZUYdHMckglktwVWIIYvImIiCivOPwRnL7ihs0XyXUpBUcURVxxBvHu5zacH7DLJADcZNFj1YIqzKvUQcLAnRMM3kRERJQXApE4ft/pHrRpC41PQhRxrtuL/7hkQ+eAGdwSAEtqK7D6pirUGjW5K5AAMHgTERFRjoVjAi72+nCx14c4z5yckJiQwMcdLhxvsQ8aCSiTSvCF2QasuqkKldzWPW/kVfD+3e9+h4MHD+LEiRPo7OyEwWDA8uXLsX37dtx2222jXnf//v148MEHh/1cT08Pqqurp6NkIiIimiRvOIaLvT602QIM3BMUiMRx8rID7192IBgV0sc1ChlW3GDCyhu56U0+yqvg/dOf/hQOhwN/8zd/g8WLF8Nms+HZZ59FY2Mj3nzzTdx9991jfo19+/Zh0aJFg46ZzebpKpmIiIgmyOoL40KPD13uEETm7Qlx+CN4r8WO01dcgyaUGLUKfHl+JW6rM0Il5wzufJVXwXvPnj2wWCyDjjU1NWH+/Pn44Q9/OK7gXV9fj+XLl09XiURERDQJoiii0xnC+V4vHP7o2FegQa44g/iPSzac6/Zi4O8qNQY17rypCvU1FZBJecJkvsur4H196AYAvV6PxYsXo7OzMwcVERERUSbiQgKttgAu9HoRiAhjX4HShISIcz1eHG+x44pz8AmnC2bocedNnFBSaDIO3u+++y5+9KMf4fz58wiFQoM+J5FI0NramtHX93g8OH369LhWuwHgnnvugc1mQ0VFBdasWYMnn3wS9fX1GdVAREREExOKCrjY50OL1Y9oPDH2FSgtHBPwYbsT7192wB2MpY/LJBI0zDbgjpsqUV2uzmGFNFkZBe/33nsPa9euxZo1a3D+/Hk0NTXB5/Ph/fffx7x58/DlL3854wK/853vIBAIYNu2baNerrq6Gtu2bUNjYyPKy8tx5swZ7N69G42NjTh+/DgaGhpGvb7VaoXNZht0rKWlJeP6iYiISoknGMP5Xi/a7QFu7T5BzkAUJ1rt+KjDNeiXFY1ChttvMKFxnhkVGp4wWcgyCt5PPPEEHnzwQfz0pz+FQqHAU089hVtvvRWfffYZmpqacN9992VU3OOPP45Dhw7hxz/+8ZhTTZqamtDU1JT+96pVq7B+/XosXboU27dvx6uvvjrq9ffu3YudO3dmVC8REVGpsnrDONfjRbc7nOtSCoooimh3BHG8xY7zPYP7tyv1Knx5vhlfmG2EUs4t3YtBRsG7ubkZjzzySLq3SBCSvVvLli3D448/jieffBJ/9Ed/NKmvvXPnTjz11FP4+7//e3z3u9+d1NeYO3cu7rjjDpw8eXLMy27ZsgUbN24cdKylpQUbNmyY1PcmIiIqdjxhcvKEhIgzXR4cb7Gjyz24VXd+lR5fnm/GTTPKuKV7kckoeAeDQej1ekilUqhUKtjt9vTnFi1ahHPnzk3q6+7cuRM7duzAjh078Oijj2ZSIkRRhFQ69m+JFotl2JM7iYiIaLC4kECbPYDzvT74w/Fcl1NQ/JE4Pmp34uRlB7wD7juZVIJbZhnwpflmzKzgDpPFKqPgPWfOHPT19QEAFi9ejN/+9rf4wz/8QwDAsWPHJjU/e9euXdixYwcee+wxPPHEE5mUh7a2Nhw/fhzr1q3L6OsQERFR8qS/S31+fN7nQ4QnTE5IlzuE91sd+Oyqe9BmQTqlDCvmmbHiBhM3vCkBGQXvNWvW4J133sGf/Mmf4C//8i+xZcsWnD9/HiqVCv/+7/+O//7f//uEvt6zzz6L7du3o6mpCevXrx/SItLY2AgA2Lx5Mw4cOIDW1lbU1dUBANatW4dVq1Zh2bJl6ZMrn376aUgkEuzatSuTm0lERFTSQlEBzd0e7jA5QUJCxNluD95vdaDjunGA1eVqfOlGMxpmG6CQsX+7VGQUvHfu3Amn0wkA+Na3voVgMIhDhw5BIpHgscceG3MSyfVee+01AMAbb7yBN954Y8jnxdT2VoIgQBCE9L8BYOnSpXj55ZfxzDPPIBQKwWKx4O6778bjjz+OBQsWTPYmEhERlaxEQsTFPh/OdHkQFxi4x8sfiePDdidOXddOIgGwuKYcX7qxEnPNWs7fLkESUeRmrSM5e/Ys6uvr0dzcjCVLluS6HCIioqzp8YTwcYcL3hB7uMcr2U5ix2dXPYP+MtA/DnDFDSYYtMocVljc1t1sgSXL880nmhXzaudKIiIiyi1/JI7THS5cdYXGvjAhnkjgbLcXJ9lOQuOQcfB+77338Itf/AIdHR3D7lx59OjRTL8FERERTbO4kMD5Hh/O93jZxz0O7mAUH7Q78WG7C4EI20lofDIK3vv27cPmzZthMpmwYMECqFSqQZ9nFwsREVH+63QGcfqKC4GIkOtS8lpCFNFq8+PkZScuXLfZjVYpw/I6ExrnsZ2ERpZR8H766afxta99DQcOHBgSuomIiCi/eUIxnO5wocfD3SZHE4oK+PiKC6cuO+AIDN4oaLZRg8Z5ZtTXVrCdhMaUUfDu6OjAj3/8Y4ZuIiKiAhKKCjjf68XnvT6wq2RkXe4QTl124PdX3YgNmOqikEnQMMuAFfPMqDVwsxsav4yC980335zeQIeIiIjyl5AQ0eUK4bLdjx5PGOwGHV40nsCZLg8+aHOg87oTTM06JRrnmXHrHCM0SlmOKqRCllHw/uEPf4hHHnkEa9asQW1t7VTVRERERFPE7o+gzR5AhyOIKHebHFGfN4wP2p345IoL4di1+0kC4OaZ5Vgxz4Qbq/SQ8mRJysCEg/d//s//edC/PR4PFixYgFtuuWXIFvESiQSvvvpqZhUSERHRhISiAtrsAbTZA/CEYrkuJ2/FhASauzz4oN2JDsfgUYB6lRzL64y4nbO3aQpNOHh/9tlng0bjyGQyWCwWdHd3o7u7e9BlOUKHiIgoO/pbSVrtfvSylWRUVl8YH7Y5cfqKG6HY4Eku86v0+OINJtw8swxyKU+WpKk14eDd3t4+DWUQERHRZHhCMbRYfWizs5VkNHEhgbM9XnzQ5kSbPTDoczqlDLfVGfHFuSaY9RwYQdOHO1cSEREVGFEUcdUVwiWrD72eSK7LyWtWXxgft7vw8RUXgtHBq9vzKnW4/QYTFs8sh5yjACkLMg7egiDgl7/8Jd5++204HA6YzWbcdddd2LhxI+Ry5noiIqKpEo4JaLH60Wrzc7ObUfRPJvmo3TlkG3etUoZb5xhx+1wTKsu4uk3ZlVEyttvtaGpqwunTpyGXy2E2m+FwOPDCCy/gmWeewZtvvonKysqpqpWIiKgk2XwRXOrz4YozyLnbIxBFEV3uED5qd+H3V92IXNd2c0OlDl+ca8KSmnJudEM5k1Hw/tu//VtcvHgRhw4dwte+9jXIZDIIgoCXX34Z3/rWt/C3f/u3+PnPfz5VtRIREZWMuJBAuyOIFqsPzgAnk4wkGI3j0043Pmp3odc7eAfOMpUct9YZcVudEZXs3aY8kFHwfu211/DUU0/h/vvvTx+TyWR44IEHYLVasWPHjkzrIyIiKimRuIALPT5csvp5suQIEqKINnsAH7U7cbbbi/iAPwNIACysLsPyOhMWVpdBJuWENcofGQVvURSxZMmSYT9XX18PkbOMiIiIxiUcE3Ch14fP+3yIC/z5ORxXMIrTV1w43eGCKzj4rwAmnRLL64z4whwjKjSKHFVINLqMgve6detw5MgRrFu3bsjn3nrrLaxZsyaTL09ERFT0GLhHF40ncK7Hg487XLhsC2DgPSSTSrCkphxfnGvCDZU67ipJeS+j4P3444/jvvvugyAIeOCBB1BdXY3e3l4cOnQIv/nNb/Cb3/wGTqczfXmTyZRxwURERMUgHBNwvseLS33+Qa0SlPyLeqcziI+vuPDZVc+QEyVrKtS4tc6IW2YZoFVxghoVjoyerbfeeisA4Nlnn8X//J//M328v8XktttuG3R5QeDoIyIiKm3hmIBzPV60MHAP4Q3F8EmnG6c7XLD5B88n1ypluGW2AbfVGTGzQpOjCokyk1Hw3r59O7eFJyIiGgcG7uHFhAQu9PpwusOFz/t8g1pJpBJgwYwy3FZnxMJqbuFOhS+j4M2pJURERKNzB6O4ZPWjzRZg4E4RRRFXnEGcvuLGmS43wrHBrSSWMhVuqzPiltkGlKl5oiQVDzZGERERTbG4kMAVZxCXrH44/NFcl5M3HP4IPu1045NON5yBwfeLWiHFslkG3DbHiFlGDf+iTkVpwsH7//yf/zOhy//5n//5RL8FERFRQfKEYmix+tBmD3IGd0ooKuBMlwefXHEN2b69v5XkC3OMWFRdxh0lqehNOHh/85vfHPdlJRLJhIL37373Oxw8eBAnTpxAZ2cnDAYDli9fju3btw85UXM4VqsVW7duxb/+678iGAyioaEBTz31FNauXTvuGoiIiCZCSCQncFyy+mHzRca+QgkQEiIu9flwutONCz3eIS02tQYNvjDHgGWzDNBzKgmVkAk/29va2qajDgDAT3/6UzgcDvzN3/wNFi9eDJvNhmeffRaNjY148803cffdd4943UgkgrVr18LtduO5556DxWLBnj170NTUhCNHjmD16tXTVjcREZUebziGllTv9vXj7kpRf9/2p51unOnyIBgdPMmsXC3HLbON+MIcA2aUq3NUJVFuTTh419XVTUcdAIA9e/bAYrEMOtbU1IT58+fjhz/84ajB+8UXX0RzczNOnDiBlStXAgDuuusuNDQ0YOvWrTh16tS01U1ERKUhkRBx1RVCi82HXg9XtwHA6gvj951u/P6qZ0jftkImQX1NBb4wx4h5Vdzghiiv/r5zfegGAL1ej8WLF6Ozs3PU677yyitYuHBhOnQDgFwux6ZNm/Doo4+iq6sLtbW1U14zEREVP38kjharH5dt/iETOEqRNxTDZ1fd+PSqG93u8KDPSQDMt+jRMNuAJTXlUMlluSmSSkK5Ro6ZFRrUGNSo1KtyXc6YMg7ely5dws9+9jOcP38eoVBo0OckEgmOHj2a0df3eDw4ffr0qKvdANDc3Iw777xzyPFly5YBAM6ePcvgTURE4yaK/avbfvR6whBLfBJgOCbgbLcHn3a6h2zdDgCzjBo0zDJg2awKjgCkaaOQSTCjXI0agxozKzTQFdg5AhlV29zcjMbGRtTW1qKlpQXLli2D3W5HV1cXZs+ejRtvvDHjAr/zne8gEAhg27Zto17O4XAMuyV9/zGHwzHq9a1WK2w226BjLS0tE6yWiIgKXTAaR6s1gFabf0ifcqmJCQlc7PXh91fduNjrG3KSpFmnRMNsA26ZbSiI1UbKLpkUMGiVMOuUMOmUUClkCEUFhGMCQjEBwaiQ/nc4JmCkMfcmnQIzKzSYaVCjUqeCVFq4LUsZBe9HH30U/+k//Se8/PLLUCqVePHFF3Hrrbfit7/9LR566CE89dRTGRX3+OOP49ChQ/jxj388rqkmo838HGse6N69e7Fz584J10hERIVPFEV0e8JosfrR7Q6V9Op2PJFAq9WP31/14FyPd8hYRJ1KjmWzKnDLLAPnbVOaVAJUaBQw6ZQw65Uw6VQwaBTjDsmiKCISTyAUFRCMJQO5XCpBdYUaakXxtCtlFLxPnz6NvXv3QprawjWRSL44169fj0ceeQQ/+MEPcOzYsUl97Z07d+Kpp57C3//93+O73/3umJc3m83Drmo7nU4AGHY1fKAtW7Zg48aNg461tLRgw4YN4y+aiIgKijsYRZs9gCvOIAKR0l3dTogi2uwBfHbVjeYuL0KxwfeFSi7F4pnlaJhtwI1VesgKeMWRpkaZWo5KvSodtI1aZUbPC4lEArVCBrVCBuMU1plvMgreLpcLJpMJUqkUCoUCLpcr/bnly5fjySefnNTX3blzJ3bs2IEdO3bg0UcfHdd1li5dijNnzgw53n+svr5+1OtbLJZhT+4kIqLiEozG0W4Pot0RgDsYy3U5OSOKIjpdIfz+qhvNVz3wReKDPi+XSrBoZjmW1VZgITe3KXkGrQKWMhUsZWpUlamgURbPKnQ2ZRS8a2trYbfbAQDz58/Hu+++i6985SsAgM8++wx6vX7CX3PXrl3YsWMHHnvsMTzxxBPjvt69996LLVu24NSpU1ixYgUAIB6P4+DBg1ixYgVqamomXAsRERWHmJBApzMZtvu8kZJtJRFFEd3uMM50eXCmyw3Xdb949O8kuWxWBW6uLoeqiP7ET+MnlQBGnRKWMhWqUm+cTjM1Mgred9xxB06cOIENGzbg61//Op544gn09PRAqVRi//792LRp04S+3rPPPovt27ejqakJ69evx8mTJwd9vrGxEQCwefNmHDhwAK2trem54g899BD27NmDjRs3Yvfu3bBYLNi7dy8uXryII0eOZHIziYioACUSInq8YbTbA+hyhYacGFgq+vvXm7s8ONM1dNa2BMC8Kh2WzUqO/9MqC2tKBGVOrZDCqE22jFjK1KjUKyHnXzimRUavrm3btqG7uxsA8Hd/93fo7e3FoUOHIJFI8LWvfQ3PPPPMhL7ea6+9BgB444038MYbbwz5vJhaohAEAYIgpP8NACqVCkePHsXWrVvx8MMPIxgM4pZbbsHrr7/OXSuJiEpIIBLHhV4f2u2lu6OkKIro8fSvbA8N2wAwx6TFslkVWFrL8X+lQiJJ9mYbtUoYtAoYtcnebLaNZI9EFEv1D25jO3v2LOrr69Hc3IwlS5bkuhwiIhqFJxTD+R4v2u2BEceSFbOBYbu5ywPHCGG7vrYC9TXlMGiVOaiSskUmRXKySCpgG7QKGDQKrmRPsYlmRf49iYiICpozEMXZbg+uukpvDGB/G8nZ1Mr2cGF7tlGDpbUVqK+tYNguAQatAjdW6XFDpQ5KOUN2vmHwJiKigtTnDeNctxc9nvDYFy4iCVFEpzOIs91enO32DDlBEmDYLjVyqQSzTVrMt+hRVcaNjPIZgzcRERWUq65k6HT4h67uFishIaLdEcDZbg/OdXvhDceHXGbWgLBtZNguCUatAjda9Jhr5up2oWDwJiKivJdIiOhwBnGu2wtPqDRmb8cTCVy2BdDcldxB8vrt6yUA6sxaLKmpwBL2bJcMuVSCOrMWN1r0qNRzdbvQMHgTEVHeEUURrmAMNl8k+eYPIxQt/gkl0XgCl6w+nOv24nyvF+HY4NsslQDzKvVYUluOxTPLOY2kRMilEph0StSZtZhbqeNmRgWMwZuIiHIuLiTgCEQHBO0I4kJpnCkZjMRxvteHcz1etFh9iF13u2VSCeZX6VFfW46bq8uhVfFHd7HTKKWo0qtRWaZMbsuuVUKawXbslD/46iUioqwLxwTYfBFYU0HbHYyW1AhAVzCK8z1enO32osMxdPyhQibBghllWFJTgUXVZVBzB8miJZEke7Ur9SpU6pO7ROr4y1XR4iNLRERZ4QvH0OkM4aorCHsJnRgJJFtn+nwRnOtO9mt3u4dOYtEqZVhUXY4lNeW4sUrPk+WKlEImQWWZClWpkG3WcZfIUsLgTURE08YZiOKqK4irrhDcw4y9K2ZCQkSHM4ALPT6c7/EOO2PboFFgcU2yX7vOrIOM7QRFR6+Wp0K2ElV6NSq07MsvZQzeREQ0ZURRhM0XQWcqbAciwthXKiLhmIBLVj/O93hxsdeHUGzo7a8uV+PmmcmV7ZkVakgkDNvFQioBTDrloBVttgnRQAzeRESUkf6tyq84g+hyhRCJF//0kYHcwSjO9/pwoceLy7YAhOu2z+wf+3fzzOTKtpkj4IqCQiaBIbUVu1GrQIVGCZNOyb9a0KgYvImIaFJCUQEtVj9abf4hM6aLmSiK6HaHcb7Xi/M9w++cqZRJcdMMPW6eWY6FM8p4slwBk0qAMrUCBm3/mxIGjYKPKU0KnzVERDQhPZ4QLvX50e0OlcwkkkhcQKs1gAu9Xlzs88E3zM6R5Wo5bp5ZjptnluMGzlrOW3KpBAq5BAqZFEqZFAp56r1MCoUsdTx1rEKjQIVGwVF+NGUYvImIaEzhmIDLtgBabH74hwmdxcgZiOJirxcXen24bA9AGOa3jJkVyX7tm6vLUWNgv3a+kUqAGeVqzDZpUWvQQCWXMkRTTjF4ExHRiKy+MFr6/Oh0BSEUeeu2kBBxxRlMh22rLzLkMnKpBDdW6bGwugyLqsu4TXsekkqA6go15pi0qDVqoJLz5EbKHwzeREQ0iC8cQ7c7jFabv+hHAPojcVzq8+Finw+X+vzDTiEpV8uxqLocC6vLOF87T8mkwMwKTXplm48R5SsGbyKiEheKCuj1htGXeivmEYAJUUSXK4SLfT583udDlyuE6xtIJABmGTVYWF2ORdVlHPmXp+RSCWYakivbNQYNe+qpIDB4ExGVmHBMgNUbQZ8vGbS9oeLu2Q5G4vjc6sfnqbA93AQWlVyKmyx6LKoux4LqMug5sSKvSCVAhUYBky45ss+oU8Ko5eg+Kjz8n4WIqMhF4gLs/ih6PWFYvWG4irx9JCGK6HaH8HmfDxd7fbg6zKo2kNzIZsGMMiysLsMck5YhLk9IJUjNxlamg7aBIZuKBIM3EVEREUURnlAMdn8Udn8Edn+k6Fe0gWRf+iWrH5f6fLhkHX6uuEouxXyLHgtmlGHBjDJUaLh1d7bJpIBKLoNKLoVKIYVKLoNSLoVKLoVWKYNRy5BNxY3Bm4iogEXjCTgCEdh914J2TCj+4drxRAIdjmA6aA+3iQ0AzChXYWEqaM8xayGXsg94uihkEpRrFChXK1CmlkOvkqfDtSoVruXsw6YSx+BNRFRA+k+EtHrDsPuj8ISKu22knyiKcASi6aB92RZAdJj5hmqFFDdW6bHAUoabZug57m+KSSWATiVHuSYZrsvVCpRrku/VCo7tIxoLgzcRUR6LxJMnQvZPHSmFtpF+oaiAVpsfLVY/Wmx+OAPRIZeRAKg1anCTpQwLZugxy8he7akklQCWchVmG7WwlKtRppJzAxqiDORd8Pb5fNi1axc+/fRTfPLJJ7Db7XjiiSewY8eOMa+7f/9+PPjgg8N+rqenB9XV1VNcLRHR1IoJCVh9keRoP0/xnwg5UDyRwBVnMBm0rf5hR/0BQJlanl7Rnl+lh5YTSKaUTJrc7ZEb0BBNvbz738rhcOD5559HQ0MDNmzYgBdeeGHCX2Pfvn1YtGjRoGNms3mqSiQimjKJhAh7IIJeTxi9njCcgSiG2Zm8KImiCKsvkg7abfbh20fkUgnmmnW4aYYeN1nKMKNcxbnaU0wuk6CmQoPZJg1nYhNNo7wL3nV1dXC5XJBIJLDb7ZMK3vX19Vi+fPk0VEdElDl/JI4edwg9nmT7SCmcDNnPG4ql20dabX54w8O3zsysUGO+RY/5Fj3mmnUMgtNAKZei1pAM2zMrNGzRIcqCvAveXMUgomITFxLo80XSYds3QtgsRqGogMv2ZMhutQZg80eGvVyFRoH5VcmgfaNFzw1sMiSRABqFDBqlDNrUm0YhT75PvemV7Ncmyrai/J/tnnvugc1mQ0VFBdasWYMnn3wS9fX1uS6LiEqIKxBFtyeEXk8YNl+kZNpHYkIC7Y4AWq0BtNr86HYP36etlEsxr1KXXtWu0rN9ZKIUMgkqNApUaBQo1yigV8lTIVsOtULK+5MoDxVV8K6ursa2bdvQ2NiI8vJynDlzBrt370ZjYyOOHz+OhoaGEa9rtVphs9kGHWtpaZnukomowCUSItyhGFzBKFyBKFzB5MfxEmkfERIirrqCaLUlg/YVZxDCML9lyKQSzDFpcWOVDjdWcfrIRKgV0nS4rkjNya7QKKBR8qRHokJTVMG7qakJTU1N6X+vWrUK69evx9KlS7F9+3a8+uqrI15379692LlzZzbKJKICFY0n4A4mw7UzEIU7mJyjXSqr2UAyaHe7Q7hsD+CyzY8OR3DYEyIlAGoMmnTQrjProJSzT3ssaoUUVWUqWMrUMOqSAZtTRYiKR1EF7+HMnTsXd9xxB06ePDnq5bZs2YKNGzcOOtbS0oINGzZMY3VElM984Rj6vJHkZjWBKPwl1JvdLyGK6PGEcdmW3LSm3RFAJD40aANApV6VDtrzqnTQKov+R0zGdCpZKmirUFWm5jb2REWuJP5XFEUR0jG2CbZYLLBYLFmqiIjyUSAST87P9kZg9YURiAi5LinrEqKIPm8Yl20BXLYH0Gb3IxwbPmgbtQrMq9JjXqUO86r0DI3jUKaWw1KmgqVcDUuZCjqeREpUUor+Fd/W1objx49j3bp1uS6FiPJMOCagz5ucn93ni5TuirY7jDZ7co52uyOIUGz4XzgqNIp0yJ5XpYOR27GPSquUwahTwqhVwKhVoqpMxW3ViUpcXgbv119/HYFAAD6fDwBw7tw5/OpXvwIAfPWrX4VWq8XmzZtx4MABtLa2oq6uDgCwbt06rFq1CsuWLUufXPn0009DIpFg165dObs9RJR7QkJM9WdH4QzEYPNF4AmVzq6Q/fp7tNvsgVTQHrl1pFwtH7SibdQqOCljBGVqOUw6JQxaBUw6JYxaJUM2EQ2Rl8H729/+Njo6OtL/Pnz4MA4fPgwguYI9d+5cCIIAQRAgitfOalq6dClefvllPPPMMwiFQrBYLLj77rvx+OOPY8GCBVm/HUSUG3EhkZ4u4gwkp42U2kmQ/eJCAl0DgvZIJ0MCyRXtGyp1uMGsww2VOpj1Sgbt60gkQLk6Ga5NOiWMuuRqNjf4IaLxyMvg3d7ePuZl9u/fj/379w869o//+I/TUxAR5a1IXIAnGIMzHbJj8IZjEEswZANAJCagwxlEuyOAdnsQV11BxEf4jcOoTQXtSh1uqOSK9nB0KhlMOiXMOhXM+mTYZsgmosnKy+BNRHS9REKENxyDOxhLz832BGMIRkvvBMiB/JE42u0BdDiS/dkjbVgDACadckDQZo/29ZRyKcw6Jcx6Jcx6Fcw6tosQ0dRi8CaivBOOCakNaWJwh6JwB2PwlmiryECiKMIZiCZXtFMnQtpH2IJdAmBGuRp1Zi3mVuow16zj1JEB5FIJjKl2kf6wXabm/UNE04vBm4hyKi4k4AxE4QhE4fBH4QhESnKM33D6T4TscAbR4Uj2Z/sjw09ekUqAWoMGN6RCdp1Zx50NU6SSZP+6SXdtJdvAthoiygEGbyLKGlEU4QnFYPdH4fBH4Eid9Fiq/djXC0UFXHEG0eFMhuyrriBiI2w9r5RJMcekRV2lFnPNOsw2arkzZIpcJkFNhQaVZckVbZNWCTn7sokoDzB4E9G0CccE2HzJgG33ReAMRhEfIUiWmoFtI1ccQVxxBtHnDY/Yn12ulmOOWYc6kxZ1Zi1mVmggk3LFtp9SLkWtQYPZJg3vGyLKWwzeRDQlEgkR7lAMdn8Edl8ENj9bRgaKxpNj/a44AqlV7eCIJ4b292fPMWtTQVvHiSPDUCukmGXUYrZJgxllakgZtokozzF4E9GkhGNCMmT7U6vZgeiIY+tKTX9LTYczuZJ9xRFEjyc04smhCpkEs4zJlew6kw5zTFr2Z49Ap5JhllGD2UYtqspU/GWEiAoKgzcRjSkSF+AKxOAIROAKJGdml+L26iPpX83udAbR6Qqi0xmEd5T7x6hVYLYpuZo9x6RDdYWarRHXUcql0Cpl0ChkUCtk0KvkmGlQo1KvynVpRESTxuBNRIP0j/Jz+KPpnR/ZMnKNKIpw+KO4kgrYnc4ger3hEVezZVIJag0azDFpk29mLcpLfGydWiGFQauARiGHRikbFLA1qY/5iwgRFSMGb6ISFY4J8Efi8Ifj8EfiyV0fgwzZ1wtG47jqGriaHUIoNvJ9VKFRYLZRk17RrjFoSnqihlYpS87L1ia3VzfplNAq+aOHiEoT//cjKlKJhIhAND4oXA/8eKQxdaUsJiTQ4w6h0xXCVVcQV10hOALRES+vkElQa0ie3DfbqMVsk7akN6nRq+WDArZRy50fiYgGYvAmKgK+1FbqrmAUrmAMnlAMgUic87FHkRBF2HwRXB0Qskc7ARIAKvUqzDFpMMuYbBuZUV7avdn987JnGTWYaVBDJWfIJiIaDYM3UQGJCQm4gzG4g1G4QzG4Asn3nI09OlEU4Q7GcNWdDNldrhC63CFE4okRr6NTyjDbpMUsYzJozzJq2CKBZH92rUGDWSYtqkv8Fw8iooniTxGiPCSKIrzhODyp1Wt3KLmSzUki4+MNx9CVWsnucodw1RUacWY20N8yci1gzzZquaX4AHq1PPULiAZVeo7wIyKaLAZvohwLROJwh2LwBJMB2xOMwRuOQRh5MZYGCETi6HKH0gG7yzX6KD+pJLk5Ta0hGbBnmTSwlHHldiCJJDnysP8XEYNWmeuSiIiKAoM3UZaEY0Jy9bp/FTsYhScU40mOE+CPxNGdCtldrhC63SG4Q7ERLy9Bsi97llGDWqMGswwaVFdooJSX7pSR62mVMlRoFTBoFDBolTBoFCjXKPiLCBHRNGDwJppi4ZgAb6i/RSSWbhcZrZ+YhvKFY6mQHU6Hbc8oIRu4tkqbbBvRoMag4VSNFIVMgor+cJ0K2hVaBU+IJCLKIgZvokkKRuPwhuLwhZOtIf0r2eEYA/ZE9G+v3u0Oo9uTXMXudodGbRcBkiG7xqBB7YA3raq0/0uTSgCtSo5ytRxlagXK1XKUaxQoU8t5YigRUR7g/8REo4gJCfjCcXhDyXDd/7EvHEd8tLlzNKyEKMLpj6LLE0KPO5QO26Od+AgkQ3Z/uK4xalBbUdohWyGTwKhVomxAsC5TK1CmkkPKFhEiorxVuj+5iFJiQiK9qYwvnFzB9kfi8IZjCEW5ej1ZcSEBqy+CHs+1gN3jCSM6RsuNWafEzAGr2DUGdUmv1sqlEhi0Cpj1Sph1Kpj0ypLfcp6IqFCV7k8zKin926P7wsmdG32RWDpsszUkc8FoHD2eMHrcyXDd4wnD6guPuhmNVAJYytSoMagxsyLZjz2zQl3SPdlSCWDQKmDSqWDSKWHWKVGhUXAVm4ioSDB4U9EIx4RBK9b9H/vC3B59qiREEa5ANB2ue1Kr2GOd9CiXSlBdoUZNKmDXGNSYUa6GQlba00U0Simq9GpUlilRqVfBqFVymggRURFj8KaCwnCdPZGYgF5vMmD3esPoTb0fq1VEq5SlV69nViTfV+pVJR8oJRKgQqNAVZkKVXoVKstU0JdwnzoRUSnKu//1fT4fdu3ahU8//RSffPIJ7HY7nnjiCezYsWNc17dardi6dSv+9V//FcFgEA0NDXjqqaewdu3a6S2cpkw0nkiH6f5g7U29Z7ieegNXsQcGbGcgOuZ1+/uxayrU6aBdppZzZ0MAcpkElfrkSnZVmQpmnYrzw4mISlzeBW+Hw4Hnn38eDQ0N2LBhA1544YVxXzcSiWDt2rVwu9147rnnYLFYsGfPHjQ1NeHIkSNYvXr1NFZO4yWKIoJRAYFoHIGIgMB1K9ecdz19ApE4er1h9KUCdp83jD5vBNExtslUyqWoLlejOhWwq8uTb6oS7sceSCWXwqhLzsg2apUwahWo0HDLeSIiGizvgnddXR1cLhckEgnsdvuEgveLL76I5uZmnDhxAitXrgQA3HXXXWhoaMDWrVtx6tSp6SqbBkgkRARjyUDtj8QRjCRPbAxGk/8ORYVRT7qjzMWEBGy+SHr1us+bfO8bYzY2AJh0SlSXpwJ2ahXboFVAyhAJANCr5TBqFcmArUuG7FKeukJEROOXdz8tMlkheuWVV7Bw4cJ06AYAuVyOTZs24dFHH0VXVxdqa2unosySF4omw3R/uB74cTAqQGSwzgohIcIRiKDPG0mtXodh9UbgCETG/OVGrUiuYs9IrWRzFXsopVyKqjIVLGUqmPXJ1exSPyGUiIgmL++Cdyaam5tx5513Djm+bNkyAMDZs2cZvMcpEhfSbSDJlpBkO0j/MW4ek10JUYQ7GEuH6z5vGFZfBFZfBMIYj4VMIkFVmQrVFamQXa7CjHI1WyGGoUoF7RnlaljKVDBoeR8REdHUKarg7XA4YDKZhhzvP+ZwOEa8rtVqhc1mG3SspaVlagvMI9F4Ir1C3R+s/f1BO8IJIbmSEEV4gjH0+ZIr11ZfKmCPow8bSO7wOKN/FbtcjRkValRxosiIVHJpMmSXq1JBW5nrkoiIqIgVVfAGRm9VGe1ze/fuxc6dO6ejpGkXjgmIxBOIxAVE44nkm5BAJJZ8338sEu+/XAJxBuuc6l/Btg4I2H3eCGy+8QXscrU8HbBnpFawq8pUUMnZJjIShUySOvkxeRJklV6FCi13gCQiouwpquBtNpuHXdV2Op0AMOxqeL8tW7Zg48aNg461tLRgw4YNU1rjRIiiiHAsgUA0eUJiMCogmPo4kPo4HBMwjpxGOSIkRDgDUdh811pDbL7xB2ydUgbLgHA9oywZtjVKBuzR6NVyGDTJEyANWgWMOiVnZhMRUc4V1U+ipUuX4syZM0OO9x+rr68f8boWiwUWi2XaapuI8z1efN7n4/SPAhITErD7I+m2kP6g7fBHIYzjTFOdSg5LmQozylWwlPW3PqgZFsegkElQoUmuYBu0imTI5gmQRESUp4rqp/q9996LLVu24NSpU1ixYgUAIB6P4+DBg1ixYgVqampyXOH4JPushVyXQcMIROLJFWv/tZVrmz8CVyCK8fyOVKaSoyrVT9wfsGeUqaFjwB6VXCpBuUaRCtnX3nOMHxERFZK8/Kn1+uuvIxAIwOfzAQDOnTuHX/3qVwCAr371q9Bqtdi8eTMOHDiA1tZW1NXVAQAeeugh7NmzBxs3bsTu3bthsViwd+9eXLx4EUeOHMnZ7aHC0r+T46BwnQrYwejYvxBJABi0CljK1OlRdJYyFarK2CIyHnq1HGadMh2wKzQKlKnZi01ERIUvL4P3t7/9bXR0dKT/ffjwYRw+fBgA0NbWhrlz50IQBAiCAHHAn/FVKhWOHj2KrVu34uGHH0YwGMQtt9yC119/nbtW0hDBSBw2fwR2fwQ2XxT21MeOQHTMEX0AIJMmtwSv0qtQ2b+CXaZCpZ5bg4+XUi6FWa+EWaeEWa+CWaeEmnPEiYioSOVl8G5vbx/zMvv378f+/fuHHJ8xYwYOHDgw9UVRQYoLCTgCqVDti8Duj6bD9nhWrwFAo5ClVqxTb/rke6NOyd0cJ0AmBQxaJSr1Sph1Kpj0SpRzJZuIiEpIXgZvoonoH83Xv2Jt90fhSH3sDsbG1XstlSS3Sq/UJ1es+8N1VZmK/deTpFPJUKVXJVey9UqYtEpIOU+ciIhKGBMFFYSEKMIXjsPhT04KsQeSAdvuj8A5ztYQIDmerzK1al2ZCteVehVMOiU3mcmAXCaBOfWLi1mffM+WESIiosEYvClviKIIb3+4DvSvWkfhDEThCETGvZumQiZJr1xX6vvDYDJs8+TGzEkkQLlakQ7YlXolt58nIiIaBwZvyqqEKMITiiXDtD8KZ6A/ZE8sXMskEph0ygHh71rILlPLGQKnUP+UEZMueRKkUcc52URERJPB4E1TLp5IwB2IJQN1Klg7/VE4AlG4guNvC5FKAKP2WvvCwMkXBi1bQ6aDRimFSadK3ddKGLWcMkJERDRVGLxpUkJRId0C4gokQ7UzmGwL8YzzhEYguXJt0CoYrrNMq5ShTC2HXiVHmVqBMrUcZr2SG9IQERFNI/6UpWEJiWRLiCsVpq9/C8XGv7OmQiZJjo9LtSqYUuPkzDolKrQKjuSbJlqlLBWsr4Xr/rAtZ6sIERFR1jF4lyhRFBGICnClVqpdqUDdH7Q9oRjG2RECIBnyTKk+4HTA1iVXsctU7LmeTnKZBEZtcqdHo1YBg1YJg0bBcE1ERJRnGLyLWDgmwBWMwhVIrly7UgHbFYzBGYwiGk+M+2tJJUCFRgGzLrlxjHlAyDZxt8Gs0alkMGqV6aBt0HI7dSIiokLB4F3AInEB7mB/qI6lQnU0HbYn0g4CXFu1NmqV171XsN86y+RSCSq0Chg0Chh1qZCtUXIreiIiogLG4J3H+les+8P19e/Hu+V5P0WqJcGoTY6EM2kVyWCt4/SKXNKr5TBoFKlWESVXsYmIiIoUg3ceevG9Nrx90YpwbPytIAAgk0pgTIW35JsiHaqNOiV0Shl7rXNIKZfCoFGkV7INqZDNmdhERESlgcE7Tw0XuuVSCQzaa60fA98btUro1XJOCMkDcqkE5Ro5KjTX+rANGiV3zSQiIipxDN55qL62HM5AdMCkiuR7PaeD5JX+gF2mVqBcnQzYFVoFp7gQERHRsBi889CKG8wwalW5LoOQnOaiS83CLtcoUD5gJjY3myEiIqKJYHKgkieRXNvJ8dpGM8n3eqUcUk5zISIioinA4E0l4/pwrVfJUa5WQK+Wc1QiERERTTsGbyoqcpkE5epkoE62hlzbKp07ORIREVEuMXhTQdKpZKlwfe3kxnIN+66JiIgofzGlUN6SSTEoVF9bxebqNRERERUeBm/KObVCmgrYqckhqXDN8YlERERUTBi8adpJUiP59CoZ9KrkPPL+kxv1ajl3biQiIqKSwOBNU0IulSTDdepExrLUx3qVHDqO5CMiIiLKv+Dt9/vx2GOP4Ze//CWcTicWLVqE//E//gf+63/9r6Neb//+/XjwwQeH/VxPTw+qq6uno9ySIpdJ0oG6TH1t5ZqbyRARERGNLe/S0n333YcPP/wQu3fvxoIFC/CLX/wC999/PxKJBB544IExr79v3z4sWrRo0DGz2Txd5RYdtUKabgEpUynSq9ZlajnUClmuyyMiIiIqWHkVvP/t3/4Nb731VjpsA8Bdd92Fjo4OfP/738ef/umfQiYbPfzV19dj+fLl2Si34EgkgEImhVIuZb81ERERUZblVfB+5ZVXoNfrsXHjxkHHH3zwQTzwwAM4deoUvvSlL+WouvwilQBalRw6pQxapRxKuRQquRQKmRQKmQRKuRTKVMhWDHhPRERERLmRV8G7ubkZN998M+TywWUtW7Ys/fmxgvc999wDm82GiooKrFmzBk8++STq6+vH/N5WqxU2m23QsZaWlgnegqmRXJlOnqyoGxCu9So5tCoZdEo5NEq2fRAREREVkrwK3g6HA/PmzRty3GQypT8/kurqamzbtg2NjY0oLy/HmTNnsHv3bjQ2NuL48eNoaGgY9Xvv3bsXO3fuzOwGTJEvzDbitjpTrssgIiIioimUV8EbwKgbpoz2uaamJjQ1NaX/vWrVKqxfvx5Lly7F9u3b8eqrr476fbds2TKkxaWlpQUbNmwYX+FTiKP3iIiIiIpPXgVvs9k87Kq20+kEcG3le7zmzp2LO+64AydPnhzzshaLBRaLZUJfn4iIiIhovPLqbLulS5fi/PnziMfjg46fOXMGAMbVq309URQhlebVzSQiIiKiEpRXifTee++F3+/Hr3/960HHDxw4gJqaGqxYsWJCX6+trQ3Hjx9HY2PjVJZJRERERDRhedVq8od/+If4yle+gm9/+9vwer2YP38+XnrpJbzxxhs4ePBgeob35s2bceDAAbS2tqKurg4AsG7dOqxatQrLli1Ln1z59NNPQyKRYNeuXbm8WURERERE+RW8AeA3v/kNtm3bhu3bt6e3jH/ppZcGbRkvCAIEQYAoiuljS5cuxcsvv4xnnnkGoVAIFosFd999Nx5//HEsWLAgFzeFiIiIiChNIg5MrzTI2bNnUV9fj+bmZixZsiTX5RARERFRHploVsyrHm8iIiIiomLF4E1ERERElAUM3kREREREWcDgTURERESUBXk31SSfRCIRAMmt44mIiIiIBurPiP2ZcSwM3qPo7OwEAGzYsCG3hRARERFR3urs7MStt9465uU4TnAUbrcbx44dw+zZs6FSqab1e7W0tGDDhg34l3/5F8yfP39avxeNjo9FfuHjkT/4WOQXPh75g49F/sj2YxGJRNDZ2YnVq1fDYDCMeXmueI/CYDDgj//4j7P6PefPn8+Z4XmCj0V+4eORP/hY5Bc+HvmDj0X+yOZjMZ6V7n48uZKIiIiIKAsYvImIiIiIsoDBm4iIiIgoCxi880RVVRWeeOIJVFVV5bqUksfHIr/w8cgffCzyCx+P/MHHIn/k+2PBqSZERERERFnAFW8iIiIioixg8CYiIiIiygIGbyIiIiKiLGDwJiIiIiLKAgbvHPD5fNi6dSv+4A/+AFVVVZBIJNixY8e4r79//35IJJJh33p7e6ev8CKV6eMBAFarFd/85jdRWVkJrVaLlStX4ujRo9NTcJHz+/343ve+h5qaGqjVatxyyy34v//3/47runxtTE4m9zmf+1Nrso8Fn/vTI9OfD3x9TJ1MHot8en1wy/gccDgceP7559HQ0IANGzbghRdemNTX2bdvHxYtWjTomNlsnooSS0qmj0ckEsHatWvhdrvx3HPPwWKxYM+ePWhqasKRI0ewevXqaaq8ON1333348MMPsXv3bixYsAC/+MUvcP/99yORSOCBBx4Y19fga2NiJnuf87k/9TJ9/vO5P7Uy+fnA18fUmorslBevD5GyLpFIiIlEQhRFUbTZbCIA8Yknnhj39fft2ycCED/88MNpqrC0ZPp47NmzRwQgnjhxIn0sFouJixcvFm+//fapLreo/fa3vxUBiL/4xS8GHf/KV74i1tTUiPF4fNTr87UxcZnc53zuT61MHgs+96dHJj8f+PqYWpk8Fvn0+mCrSQ70/3mD8kOmj8crr7yChQsXYuXKleljcrkcmzZtwgcffICurq6pKLMkvPLKK9Dr9di4ceOg4w8++CC6u7tx6tSpHFVWvDK5z/ncn1p8/uefTH4+8PUxtYolOzF4F7B77rkHMpkMJpMJ9913H5qbm3NdUklqbm7GsmXLhhzvP3b27Nlsl1SwmpubcfPNN0MuH9wF139fjvc5ztfG+GVyn/O5P7Wm4vnP537+4Osj/+TD64M93gWouroa27ZtQ2NjI8rLy3HmzBns3r0bjY2NOH78OBoaGnJdYklxOBwwmUxDjvcfczgc2S6pYDkcDsybN2/I8fHel3xtTFwm9zmf+1Mrk8eCz/38w9dH/sin1weDd4beeecd3HXXXeO67CeffIJbbrkl4+/Z1NSEpqam9L9XrVqF9evXY+nSpdi+fTteffXVjL9HocrF4wFg1D9/FcOfxiZjso9FJvclXxuTk8l9zuf+1Jrs/cnnfn7i6yM/5NPrg8E7QwsXLsQ//dM/jeuyc+bMmbY65s6dizvuuAMnT56ctu9RCHLxeJjN5mFXLpxOJwAMu+JRCibzWEzHfcnXxugyuc/53J9aU31/8rmfW3x95LdcvT4YvDM0c+ZM/MVf/EWuywAAiKIIqbS02/Zz8XgsXboUZ86cGXK8/1h9fX1W68kXk3ksli5dipdeegnxeHxQn2um9yVfGyPL5D7nc39qTcfzn8/93OHrI//l4vXBV2ORaGtrw/Hjx9HY2JjrUkrOvffeiwsXLgyaOBCPx3Hw4EGsWLECNTU1OayusNx7773w+/349a9/Pej4gQMHUFNTgxUrVkz4a/K1MbpM7nM+96fWVD//+dzPLb4+8lvOXh+5nWZYuv7t3/5NPHz4sPjP//zPIgBx48aN4uHDh8XDhw+LgUAgfbmHHnpIlMlkYnt7e/rY2rVrxZ07d4qvvPKKePToUfF//a//JdbU1IhlZWXimTNncnFzCl4mj0c4HBaXLFkizp49Wzx06JD41ltviffee68ol8vFd955Jxc3p6B95StfEY1Go/j888+Lv/vd78S//Mu/FAGIBw8eHHQ5vjamznjucz73s2OyjwWf+9NnPD8f+PrIjsk+Fvn0+mDwzpG6ujoRwLBvbW1t6ct94xvfGHLse9/7nrh48WKxrKxMlMvlYk1Njbhp0ybx4sWL2b8hRSKTx0MURbG3t1f88z//c9FkMolqtVpsbGwU33rrrezeiCLh8/nE//bf/ptYXV0tKpVKcdmyZeJLL7005HJ8bUyd8dznfO5nx2QfCz73p894fj7w9ZEdk30s8un1IRFFUZzOFXUiIiIiImKPNxERERFRVjB4ExERERFlAYM3EREREVEWMHgTEREREWUBgzcRERERURYweBMRERERZQGDNxERERFRFjB4ExERERFlAYM3EREREVEWMHgTEREREWUBgzcRERERURYweBMRERERZQGDNxERjSgcDuMLX/gC5s+fD4/Hkz7e29uL6upqrFmzBoIg5LBCIqLCweBNREQjUqvV+OUvfwmr1YqHHnoIAJBIJPD1r38doijipZdegkwmy3GVRESFQZ7rAoiIKL/ddNNNeOGFF/Cnf/qneO655+B0OvHOO+/gjTfewMyZM3NdHhFRwZCIoijmuggiIsp/W7ZswQsvvABBEPDoo49i165duS6JiKigMHgTEdG4fPTRR/jiF78IpVKJq1evoqqqKtclEREVFAZvIiIaUyAQwPLly5FIJNDX14fVq1fj1VdfzXVZREQFhSdXEhHRmL71rW/hypUr+M1vfoMXX3wR/+///T/84z/+Y67LIiIqKAzeREQ0qhdeeAEHDx7Enj17sGTJEvyX//Jf8N3vfhd/93d/hw8++CDX5RERFQy2mhAR0YjOnDmDFStW4Gtf+xr279+fPh6JRPDlL38ZDocDn3zyCQwGQ85qJCIqFAzeRERERERZwFYTIiIiIqIsYPAmIiIiIsoCBm8iIiIioixg8CYiIiIiygIGbyIiIiKiLGDwJiIiIiLKAgZvIiIiIqIsYPAmIiIiIsoCBm8iIiIioixg8CYiIiIiygIGbyIiIiKiLGDwJiIiIiLKAgZvIiIiIqIs+P+4ddHkWSCo3QAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Second, another param. of the distribution: alpha\n", + "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n", + "bmb.interpret.plot_predictions(model, idata, \"x\", target='alpha', ax=ax);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Last updated: Wed Aug 16 2023\n", + "\n", + "Python implementation: CPython\n", + "Python version : 3.11.0\n", + "IPython version : 8.13.2\n", + "\n", + "pandas : 2.0.1\n", + "matplotlib: 3.7.1\n", + "bambi : 0.10.0.dev0\n", + "arviz : 0.15.1\n", + "numpy : 1.24.2\n", + "\n", + "Watermark: 2.3.1\n", + "\n" + ] + } + ], + "source": [ + "%load_ext watermark\n", + "%watermark -n -u -v -iv -w" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "bambinos", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.0" + }, + "orig_nbformat": 4 }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 2 seconds.\n", - "There were 25 divergences after tuning. Increase `target_accept` or reparameterize.\n" - ] - } - ], - "source": [ - "rng = np.random.default_rng(121195)\n", - "N = 200\n", - "a, b = 0.5, 1.1\n", - "x = rng.uniform(-1.5, 1.5, N)\n", - "shape = np.exp(0.3 + x * 0.5 + rng.normal(scale=0.1, size=N))\n", - "y = rng.gamma(shape, np.exp(a + b * x) / shape, N)\n", - "data_gamma = pd.DataFrame({\"x\": x, \"y\": y})\n", - "\n", - "formula = bmb.Formula(\"y ~ x\", \"alpha ~ x\")\n", - "model = bmb.Model(formula, data_gamma, family=\"gamma\")\n", - "idata = model.fit(random_seed=1234)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - " Formula: y ~ x\n", - " alpha ~ x\n", - " Family: gamma\n", - " Link: mu = inverse\n", - " alpha = log\n", - " Observations: 200\n", - " Priors: \n", - " target = mu\n", - " Common-level effects\n", - " Intercept ~ Normal(mu: 0.0, sigma: 2.5037)\n", - " x ~ Normal(mu: 0.0, sigma: 2.8025)\n", - " target = alpha\n", - " Common-level effects\n", - " alpha_Intercept ~ Normal(mu: 0.0, sigma: 1.0)\n", - " alpha_x ~ Normal(mu: 0.0, sigma: 1.0)\n", - "------\n", - "* To see a plot of the priors call the .plot_priors() method.\n", - "* To see a summary or plot of the posterior pass the object returned by .fit() to az.summary() or az.plot_trace()" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The model we defined uses a `gamma` distribution parameterized by `alpha` and `mu` where `alpha` utilizes a log link and `mu` goes through an inverse link. Therefore, we can plot either: (1) the `mu` of the response distribution (which is the default), or (2) `alpha` of the response distribution as a function of the explanatory variable $x$. " - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# First, the mean of the response (default)\n", - "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n", - "plot_predictions(model, idata, \"x\", ax=ax);" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Below, instead of plotting the default target, `target=mean`, we set `target=alpha` to visualize how the model parameter `alpha` varies as a function of the `x` predictor." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Second, another param. of the distribution: alpha\n", - "fig, ax = plt.subplots(figsize=(7, 3), dpi=120)\n", - "plot_predictions(model, idata, \"x\", target='alpha', ax=ax);" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Last updated: Wed Aug 16 2023\n", - "\n", - "Python implementation: CPython\n", - "Python version : 3.11.0\n", - "IPython version : 8.13.2\n", - "\n", - "pandas : 2.0.1\n", - "matplotlib: 3.7.1\n", - "bambi : 0.10.0.dev0\n", - "arviz : 0.15.1\n", - "numpy : 1.24.2\n", - "\n", - "Watermark: 2.3.1\n", - "\n" - ] - } - ], - "source": [ - "%load_ext watermark\n", - "%watermark -n -u -v -iv -w" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "bambinos", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.0" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} + "nbformat": 4, + "nbformat_minor": 2 + } \ No newline at end of file diff --git a/docs/notebooks/plot_slopes.ipynb b/docs/notebooks/plot_slopes.ipynb index d44e39d49..6b2571a07 100644 --- a/docs/notebooks/plot_slopes.ipynb +++ b/docs/notebooks/plot_slopes.ipynb @@ -6,7 +6,7 @@ "source": [ "# Plot Slopes\n", "\n", - "The Bambi's sub-package `plots` features a set of functions to help interpret complex regression models. This sub-package is inspired by the R package [marginaleffects](https://vincentarelbundock.github.io/marginaleffects/articles/predictions.html#conditional-adjusted-predictions-plot). In this notebook we will discuss two functions `slopes` and `plot_slopes`. These two functions allow the modeler to easier interpret slopes, either by a inspecting a summary output or plotting them.\n", + "Bambi's sub-package `interpret` features a set of functions to help interpret complex regression models. The sub-package is inspired by the R package [marginaleffects](https://vincentarelbundock.github.io/marginaleffects/articles/predictions.html#conditional-adjusted-predictions-plot). In this notebook we will discuss two functions `slopes` and `plot_slopes`. These two functions allow the modeler to easier interpret slopes, either by a inspecting a summary output or plotting them.\n", "\n", "Below, it is described why estimating the slope of the prediction function is useful in interpreting generalized linear models (GLMs), how this methodology is implemented in Bambi, and how to use `slopes` and `plot_slopes`. It is assumed that the reader is familiar with the basics of GLMs. If not, refer to the Bambi [Basic Building Blocks](https://bambinos.github.io/bambi/notebooks/how_bambi_works.html#Link-functions) example." ] @@ -95,15 +95,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import arviz as az\n", "import pandas as pd\n", "\n", - "import bambi as bmb\n", - "from bambi.plots import slopes, plot_slopes" + "import bambi as bmb" ] }, { @@ -125,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -222,7 +221,7 @@ "5 1 1.10 40.874001 1 14 0.40874 3.5" ] }, - "execution_count": 3, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -237,7 +236,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -333,7 +332,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -348,7 +347,7 @@ } ], "source": [ - "fig, ax = plot_slopes(\n", + "fig, ax = bmb.interpret.plot_slopes(\n", " well_model,\n", " well_idata,\n", " wrt={\"arsenic\": 1.3},\n", @@ -369,7 +368,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -531,13 +530,13 @@ "8 -0.096476 " ] }, - "execution_count": 5, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "slopes(\n", + "bmb.interpret.slopes(\n", " well_model,\n", " well_idata,\n", " wrt={\"arsenic\": 1.5},\n", @@ -566,7 +565,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -689,13 +688,13 @@ "5 -0.093209 " ] }, - "execution_count": 6, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "multiple_values = slopes(\n", + "multiple_values = bmb.interpret.slopes(\n", " well_model,\n", " well_idata,\n", " wrt={\"arsenic\": [1.5, 2.0, 2.5]},\n", @@ -726,14 +725,20 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Modeling the probability that switch==0\n", + "Modeling the probability that switch==0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "Auto-assigning NUTS sampler...\n", "Initializing NUTS using jitter+adapt_diag...\n", "Multiprocess sampling (4 chains in 4 jobs)\n", @@ -809,7 +814,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -939,7 +944,7 @@ "arsenic:educ4 2348.0 1.0 " ] }, - "execution_count": 8, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -962,7 +967,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -977,7 +982,7 @@ } ], "source": [ - "fig, ax = plot_slopes(\n", + "fig, ax = bmb.interpret.plot_slopes(\n", " well_model_interact,\n", " well_idata_interact,\n", " wrt=\"arsenic\",\n", @@ -997,7 +1002,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1012,7 +1017,7 @@ } ], "source": [ - "fig, ax = plot_slopes(\n", + "fig, ax = bmb.interpret.plot_slopes(\n", " well_model,\n", " well_idata,\n", " wrt=\"arsenic\",\n", @@ -1041,7 +1046,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1223,13 +1228,13 @@ "9 -0.099035 -0.129517 -0.073222 " ] }, - "execution_count": 11, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "unit_level = slopes(\n", + "unit_level = bmb.interpret.slopes(\n", " well_model_interact,\n", " well_idata_interact,\n", " wrt=\"arsenic\",\n", @@ -1422,7 +1427,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1471,13 +1476,13 @@ "0 arsenic dydx -0.111342 -0.134846 -0.088171" ] }, - "execution_count": 13, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "slopes(\n", + "bmb.interpret.slopes(\n", " well_model_interact,\n", " well_idata_interact,\n", " wrt=\"arsenic\",\n", @@ -1527,7 +1532,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1748,14 +1753,14 @@ "17 arsenic dydx 4.25 -0.176623 -0.244273 -0.107141" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# average by educ4\n", - "slopes(\n", + "bmb.interpret.slopes(\n", " well_model_interact,\n", " well_idata_interact,\n", " wrt=\"arsenic\",\n", @@ -1766,7 +1771,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1932,14 +1937,14 @@ "[2997 rows x 7 columns]" ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# average by both educ4 and dist100\n", - "slopes(\n", + "bmb.interpret.slopes(\n", " well_model_interact,\n", " well_idata_interact,\n", " wrt=\"arsenic\",\n", @@ -1957,7 +1962,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1972,7 +1977,7 @@ } ], "source": [ - "fig, ax = plot_slopes(\n", + "fig, ax = bmb.interpret.plot_slopes(\n", " well_model_interact,\n", " well_idata_interact,\n", " wrt=\"arsenic\",\n", @@ -2007,7 +2012,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -2056,13 +2061,13 @@ "0 arsenic eyex -0.525124 -0.652708 -0.396082" ] }, - "execution_count": 18, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "slopes(\n", + "bmb.interpret.slopes(\n", " well_model_interact,\n", " well_idata_interact,\n", " wrt=\"arsenic\",\n", @@ -2074,7 +2079,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -2123,13 +2128,13 @@ "0 arsenic eydx -0.286753 -0.351592 -0.220459" ] }, - "execution_count": 19, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "slopes(\n", + "bmb.interpret.slopes(\n", " well_model_interact,\n", " well_idata_interact,\n", " wrt=\"arsenic\",\n", @@ -2141,7 +2146,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -2190,13 +2195,13 @@ "0 arsenic dyex -0.167616 -0.201147 -0.134605" ] }, - "execution_count": 20, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "slopes(\n", + "bmb.interpret.slopes(\n", " well_model_interact,\n", " well_idata_interact,\n", " wrt=\"arsenic\",\n", @@ -2215,7 +2220,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -2230,7 +2235,7 @@ } ], "source": [ - "fig, ax = plot_slopes(\n", + "fig, ax = bmb.interpret.plot_slopes(\n", " well_model_interact,\n", " well_idata_interact,\n", " wrt=\"arsenic\",\n", @@ -2253,7 +2258,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -2265,7 +2270,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -2312,7 +2317,7 @@ "\n", "
\n", " \n", - " 100.00% [8000/8000 04:30<00:00 Sampling 4 chains, 0 divergences]\n", + " 100.00% [8000/8000 05:18<00:00 Sampling 4 chains, 0 divergences]\n", "
\n", " " ], @@ -2327,7 +2332,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 271 seconds.\n" + "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 319 seconds.\n" ] } ], @@ -2348,7 +2353,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -2363,7 +2368,7 @@ } ], "source": [ - "fig, ax = plot_slopes(\n", + "fig, ax = bmb.interpret.plot_slopes(\n", " well_model_interact,\n", " well_idata_interact,\n", " wrt=\"educ4\",\n", @@ -2382,7 +2387,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -2397,9 +2402,7 @@ } ], "source": [ - "from bambi.plots import plot_comparison\n", - "\n", - "fig, ax = plot_comparison(\n", + "fig, ax = bmb.interpret.plot_comparisons(\n", " well_model_interact,\n", " well_idata_interact,\n", " contrast=\"educ4\",\n", @@ -2418,7 +2421,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -2433,7 +2436,7 @@ } ], "source": [ - "fig, ax = plot_slopes(\n", + "fig, ax = bmb.interpret.plot_slopes(\n", " well_model_interact,\n", " well_idata_interact,\n", " wrt={\"educ4\": [1, 4]},\n", @@ -2445,14 +2448,14 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Last updated: Tue Aug 01 2023\n", + "Last updated: Wed Aug 16 2023\n", "\n", "Python implementation: CPython\n", "Python version : 3.11.0\n", diff --git a/pyproject.toml b/pyproject.toml index a5fde9420..bf62d10ef 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -58,7 +58,7 @@ packages = [ "bambi.data", "bambi.defaults", "bambi.families", - "bambi.plots", + "bambi.interpret", "bambi.priors", "bambi.terms", ] diff --git a/tests/test_plots.py b/tests/test_plots.py index 257ac792e..8191e50eb 100644 --- a/tests/test_plots.py +++ b/tests/test_plots.py @@ -6,7 +6,7 @@ import pytest import bambi as bmb -from bambi.plots import plot_comparisons, plot_predictions, plot_slopes +from bambi.interpret import plot_comparisons, plot_predictions, plot_slopes @pytest.fixture(scope="module")