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Project-OLS.Rmd
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Project-OLS.Rmd
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---
title: "Estimation strategy"
author: "Sumit Meghlani 64801343"
date: "3/25/2022"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(tibble)
library(dplyr)
library(stargazer)
library(tidyverse)
library(fixest)
library(etable)
library(modelsummary)
library(kableExtra)
library(FactoMineR)
```
```{r functions}
#' @param df dataframe (decide if rows have been randomized)
#' randomized outside of function if mix R/python methods
#' @param K number of folds for cross-validation
#' @param nperfmeas number of performance measures used to compare methods
#' @param seed random number of seed for reproducibility
#' @return matrix of performance measures by fold, vector of averages
crossValidationCont = function(df,K,nperfmeas=6,seed)
{ set.seed(seed)
# apply transformations
df <- transform(df)
n = nrow(df)
nhold = round(n/K) # size of holdout set
iperm = sample(n)
comperf50meas = matrix(0,K,nperfmeas)
comperf80meas = matrix(0,K,nperfmeas)
resperf50meas = matrix(0,K,nperfmeas)
resperf80meas = matrix(0,K,nperfmeas)
for(k in 1:K)
{ indices = (((k-1)*nhold+1):(k*nhold))
if( k==K ) indices = (((k-1)*nhold+1):n)
indices = iperm[indices]
# split between commercial and residential
commdf <- ClassSplit(df)[[1]]%>% medianImp()
resdf <- ClassSplit(df)[[2]]%>% medianImp()
# commercial
commtrain = commdf[-indices,]
commholdout = commdf[indices,]
PredfestcomModel <- feols(site_eui~minavg+maxavg+energy_star_rating+floor_area+facility_type+Year_Factor+State_Factor+cooling_degree_days+heating_degree_days+precipitation_inches+days_above_110F+days_below_30F,commtrain)
restrain = resdf[-indices,]
resholdout = resdf[indices,]
PredfestresModel <- feols(site_eui~minavg+maxavg+energy_star_rating+facility_type+Year_Factor+State_Factor+cooling_degree_days+heating_degree_days+precipitation_inches+days_above_110F+days_below_30F,restrain)
#pred = myPredict(obj, newdata=holdout) # could include 50% and 80% intervals
comperf50meas[k,] = probCheck(PredfestcomModel,commholdout,0.51)$summary
comperf80meas[k,] = probCheck(PredfestcomModel,commholdout,0.80)$summary
resperf50meas[k,] = probCheck(PredfestresModel,resholdout,0.51)$summary
resperf80meas[k,] = probCheck(PredfestresModel,resholdout,0.80)$summary
#perfMeas(pred, holdout$y)
}
# 50
comavgperf50meas = apply(comperf50meas,2,mean)
resavgperf50meas = apply(resperf50meas,2,mean)
# 80
comavgperf80meas = apply(comperf80meas,2,mean)
resavgperf80meas = apply(resperf80meas,2,mean)
list(comperfmeas50byfold=comperf50meas,resperfmeas50byfold = resperf50meas, comavgperf50meas=comavgperf50meas,resavgperf50meas = resavgperf50meas,comperfmeas80byfold=comperf80meas,resperfmeas80byfold = resperf80meas, comavgperf80meas=comavgperf80meas,resavgperf80meas = resavgperf80meas)
}
# prob matrix
probCheck <- function(model,df,level){
problist <- predict(model,df,interval='predi',level=level)[,-2] %>% na.omit()
return(intervalScore(problist,na.omit(df$site_eui),level))
}
```
```{r functions}
statebins <- function(df,ntrain){
weather= rep("M",ntrain)
weather[df$State_Factor == 'State_4']='C';weather[df$State_Factor == 'State_6']='C';weather[df$State_Factor == 'State_1']='H';weather[df$State_Factor == 'State_11']='H';
weather = as.factor(weather); df$State_Factor = weather
return(df)
}
transform <- function(df,subset = TRUE){
if (subset==TRUE){
df <- df
ntrain <- nrow(df)/2
df <- df[sample(nrow(df),ntrain),]
df <- statebins(df,ntrain)
}
# transformations
df$floor_area <- log(df$floor_area)
df$site_eui <- log(df$site_eui)
# days_data
daysDatNames <- df %>% select(contains('days')) %>% names()
# creating auxillarydf
#auxDf <- df %>% select(c(daysDatNames,'avg_temp',"cooling_degree_days","heating_degree_days","precipitation_inches","snowfall_inches","snowdepth_inches","building_class"))
# removing missing values
#df <- df %>% select(-c('id','direction_peak_wind_speed' ,'max_wind_speed' ,'days_with_fog',"cooling_degree_days","heating_degree_days"))
# removed percip,snow,days
#df <- df %>% select(-names(df)[45:57])
# min and max names
minNames <- df %>% select(contains("min")) %>% names()
maxNames <- df%>% select(contains("max")) %>% names()
avgNames <- df%>% select(contains("_avg_")) %>% names()
# create a minimum and maximum averagese.
df <- df %>% mutate(minavg = (january_min_temp+february_min_temp+march_min_temp+october_min_temp+november_min_temp+december_min_temp)/4)
df <- df %>% mutate(maxavg = (april_max_temp+may_max_temp+june_max_temp+july_max_temp+august_max_temp+september_max_temp)/4)
#return(list(df,auxDf))
return(df)
}
medianImp <- function(df){
df$year_built[is.na(df$year_built)]= summary(df$year_built)[[3]]
df$energy_star_rating[is.na(df$energy_star_rating)] = summary(df$energy_star_rating)[[3]]
return(df)
}
ClassSplit <- function(df){
commdf <- df %>% filter(building_class=='Commercial')%>% select(-'building_class')
resDf <- df %>% filter(building_class=="Residential") %>% select(-'building_class')
return(list(commdf,resDf))
}
plot_results <- function(df,model){
preds <- predict(model,df)
plot(density(preds));
lines(density(df$site_eui),col=2,type='h',main='predictions vs actual');
}
intervalScore = function(predObj,actual,level)
{ n = nrow(predObj)
alpha = 1-level
ilow = (actual<predObj[,2]) # underestimation
ihigh = (actual>predObj[,3]) # overestimation
sumlength = sum(predObj[,3]-predObj[,2]) # sum of lengths of prediction intervals
sumlow = sum(predObj[ilow,2]-actual[ilow])*2/alpha
sumhigh = sum(actual[ihigh]-predObj[ihigh,3])*2/alpha
avglength = sumlength/n
IS = (sumlength+sumlow+sumhigh)/n # average length + average under/over penalties
browser()
cover = mean(actual>= predObj[,2] & actual<=predObj[,3])
summ = c(level,avglength,IS,cover)
# summary with level, average length, interval score, coverage rate
imiss = which(ilow | ihigh)
list(summary=summ, imiss=imiss)
}
```
## loading in the dataset
```{r}
# load the data
bigdf <- read.csv('train.csv')
# use this to omit na values
# bigdf <- na.omit(bigdf)
ntrain = nrow(bigdf)
df = bigdf[sample(nrow(bigdf),ntrain),]
# to use the full dataset
# log transformation for floor_area
# going to use max of summer months [Apr - Sep], min of winter months [Oct - Mar]
```
```{r}
df <- transform(df=bigdf)
# ClassSplit the main df
commDf <- ClassSplit(df)[[1]] %>% medianImp()
resDf <- ClassSplit(df)[[2]] %>% medianImp()
```
### Main Model regression equation
**$e_{t,J} = \alpha_{t,J} + \beta_1 minavg_{t,J} + \beta_2 maxavg_{t,J} + \beta_3 ESA_{t,J} +\beta_4 yb_{t,J} +\beta_5 elev_{t,J}+\beta_6 floar_{t,J}+\beta_7 factyp_{t,J}+ \epsilon_{t,J}$**
# running fest
Fest is a package in R used for fixed effects estimation.
```{r}
#running fixest
#-- cant be used for predictions as standard errors harder to compute with fixed effects
festcomModel <- feols(site_eui~minavg+maxavg+energy_star_rating+facility_type|Year_Factor+State_Factor|avg_temp~cooling_degree_days+heating_degree_days+precipitation_inches+days_above_110F+days_below_30F,commDf)
festresModel <- feols(site_eui~+minavg+maxavg+energy_star_rating+facility_type|Year_Factor+State_Factor|avg_temp~cooling_degree_days+heating_degree_days+precipitation_inches+days_above_110F+days_below_30F,resDf)
```
```{r}
# - plotting density predictions
plot_results(commDf,festcomModel)
plot_results(resDf,festresModel)
```
# interval score implementation
```{r}
# running models to predict
PredfestcomModel <- feols(site_eui~minavg+maxavg+energy_star_rating+facility_type+Year_Factor+State_Factor|avg_temp~cooling_degree_days+heating_degree_days+precipitation_inches+days_above_110F+days_below_30F,commDf)
PredfestresModel <- feols(site_eui~+minavg+maxavg+energy_star_rating+facility_type+Year_Factor+State_Factor+cooling_degree_days+heating_degree_days+precipitation_inches+days_above_110F+days_below_30F,resDf)
```
```{r}
# 0.5 predictions
comModel50 <- probCheck(PredfestcomModel,commDf,0.51)
resModel50 <- probCheck(PredfestresModel,resDf,0.51)
#0.8 predictions
comModel80 <- probCheck(PredfestcomModel,commDf,0.95)
resModel80 <- probCheck(PredfestresModel,resDf,0.95)
REcomreg <- rbind(comModel50$summary,comModel80$summary)
colnames(REcomreg)=c("level","avgleng","IS","cover")
print(REcomreg)
REresreg <- rbind(resModel50$summary,resModel80$summary)
colnames(REresreg)=c("level","avgleng","IS","cover")
print(REresreg)
```
```{r residential feeffects echo=FALSE}
# running fixed effects one by one
feList = list()
all_FEs = c("State_Factor","Year_Factor")
for (i in 0:2){
feList[[i+1]] <- feols(log(site_eui)~+minavg+maxavg+energy_star_rating+year_built+ELEVATION+floor_area+i(facility_type)+avg_temp,resDf,fixef = all_FEs[0:i])
}
etable(feList,title='Residential Regression Table')
cm <- c('(Intercept)'='Intercept','minavg'='Minimum Average (F)','maxavg'='Maximum Average(F)','energy_star_rating'='Energy Star Rating','year_built'='Year Built','floor_area'='Floor Area','avg_temp'='Average Temp (aux)')
tb <- modelsummary::modelsummary(feList,output="kableExtra",stars=TRUE,title = 'Regression Table',coef_map = cm,gof_omit = "BIC|Log.Lik.|R2 Adj.|R2 Within| R2 Pseudo|AIC")
```
#crossValidation
```{r}
crossValidationCont(bigdf,5,nperfmeas = 4,seed=123)
```
#Principal Component Analysis
Cannot do PRincipal Componenet Analysis for the life of me, if someone figures this out. Thanks in advance.
```{r}
#test <- tibble(commDf$facility_type)
#princomp(na.omit(commDf[,3]),na.action=na.exclude, cor = TRUE)
#testmfa = MFA(base=test,group=c(54),type = c("n"),ncp=5,name.group = c("facility_type"))
```
# see below the residential coefs plot
```{r residential coefficient estimate plots}
plotDf <- sort(resfestModel$coefficients)
qplot(x=plotDf,y = names(plotDf))+geom_bar(stat="identity", position="dodge")+labs(title='Residential Regression Coefficients',x = '%',y='Features')
```
```{r commercial fest for loop echo=FALSE}
feList = list()
all_FEs = c("State_Factor","Year_Factor")
for (i in 0:2){
feList[[i+1]] <- feols(log(site_eui)~+minavg+maxavg+energy_star_rating+year_built+ELEVATION+floor_area+i(facility_type)+avg_temp,commDf,fixef = all_FEs[0:i])
}
etable(feList,drop='=',tex=TRUE,title='Commercial Regression Table')
```
Models to run interval score on
- festModel
- resfestModel
```{r}
plot(density(predict(festModel,commDf)))
lines(density(log(commDf$site_eui)),col=2,type='h')
```