Merge pull request #137 from adibender/devel

CRAN release

.travis.yml

@@ -11,6 +11,17 @@ jobs:
     - Rscript -e 'remotes::install_cran("pammtools", dependencies = TRUE)'
     - Rscript -e 'remotes::install_cran("TBFmultinomial", dependencies = TRUE)'
     - Rscript -e 'remotes::install_github("jimhester/covr")'
+    - Rscript -e 'remotes::install_cran("pkgdown")'
+    - Rscript -e 'remotes::install_cran("scam")'
+    - Rscript -e 'remotes::install_cran("RColorBrewer")'
+    - Rscript -e 'remotes::install_cran("timereg")'
+    - Rscript -e 'remotes::install_cran("grid")'
+    - Rscript -e 'remotes::install_cran("gridExtra")'
+    - Rscript -e 'remotes::install_cran("coxme")'
+    - Rscript -e 'remotes::install_cran("knitr")'
+    - Rscript -e 'remotes::install_cran("rmarkdown")'
+    - Rscript -e 'remotes::install_cran("pec")'
+    - Rscript -e 'remotes::install_cran("devtools")'
 # whitelist
 branches:
   only:

DESCRIPTION

@@ -1,14 +1,14 @@
 Package: pammtools
 Title: Piece-Wise Exponential Additive Mixed Modeling Tools for Survival Analysis
-Version: 0.2.0
-Date: 2020-01-30
+Version: 0.2.1
+Date: 2020-02-07
 Authors@R: c(
     person("Andreas", "Bender", , "[email protected]", role = c("aut", "cre"), comment=c(ORCID = "0000-0001-5628-8611")),
     person("Fabian", "Scheipl", , "[email protected]", role = c("aut")))
 Description: The Piece-wise exponential (Additive Mixed) Model
     (PAMM; Bender and Scheipl (2018) <doi: 10.1177/1471082X17748083>) is a
     powerful model class for the analysis of survival (or time-to-event) data,
-    based on Generalized Additive (Mixed) Models (GA(M)Ms). It offers intuitive specification and robust estimation of complex survival models with stratified baseline hazards, random effects, time-varying effects, time-dependent covariates and cumulative effects (Bender and others (2018) <doi:10.1093/biostatistics/kxy003>.
+    based on Generalized Additive (Mixed) Models (GA(M)Ms). It offers intuitive specification and robust estimation of complex survival models with stratified baseline hazards, random effects, time-varying effects, time-dependent covariates and cumulative effects (Bender and others (2018) <doi: 10.1093/biostatistics/kxy003>.
     pammtools provides tidy workflow for survival analysis with PAMMs,
     including data simulation, transformation and other functions for data
     preprocessing and model post-processing as well as visualization.
@@ -30,17 +30,7 @@ Imports:
     mvtnorm,
     pec
 Suggests:
-    RColorBrewer,
-    scam,
-    coxme,
-    testthat,
-    timereg,
-    knitr,
-    rmarkdown,
-    grid,
-    gridExtra,
-    covr,
-    pkgdown
+    testthat
 License: MIT + file LICENSE
 LazyData: true
 URL: https://github.com/adibender/pammtools

NEWS.md

@@ -1,9 +1,10 @@
-# pammtools 0.2.0
+# pammtools 0.2.1
 * Adds a new interface for model estimation called `pamm`, which is a thin wrapper
 around `mgcv::gam` with some arguments pre-set.
-* Adds S3 methods `predict.pamm` and `predictSurvProb.pamm`
-* Adds support for model evaluation using package **`pec`**
+* Adds S3 method `predict.pammpredictSurvProb.pamm`
+* Adds support and vignette for model evaluation using package **`pec`**
 * Fixed bug when CIs were calculated simulation based and model contained factor variables
+* Removed unnecessary dependencies in Imports/Suggests
 
 # pammtools 0.1.15
 * Interface for specification of data transformation in `as_ped` changed. The vertical bar `|` is no longer necessary to indicate concurrent or cumulative effects
@@ -32,12 +33,12 @@ Allows to calculate (log-)hazard ratios.
 * Added new vignette linking to tutorial paper (online only)
 
 # pammtools 0.1.9
-* maintainance update: fixes CRAN issues due to new RNG
+* maintenance update: fixes CRAN issues due to new RNG
 
 # pammtools 0.1.8
 
 ## documentation
-* Updates to cummulative effect vignette
+* Updates to cumulative effect vignette
 * Updates to time-dependent covariate vignette (+ data transformation)
 * Update citation information
 

R/add-functions.R

@@ -128,7 +128,7 @@ preproc_reference <- function(reference, cnames, n_rows) {
 #' Add (cumulative) hazard based on the provided data set and model.
 #' If \code{ci=TRUE} confidence intervals (CI) are also added. Their width can
 #' be controlled via the \code{se_mult} argument. The method by which the
-#' CI are calculated can be specifie by \code{ci_type}.
+#' CI are calculated can be specified by \code{ci_type}.
 #' This is a wrapper around
 #' \code{\link[mgcv]{predict.gam}}. When \code{reference} is specified, the
 #' (log-)hazard ratio is calculated.

R/predict.R

@@ -3,7 +3,7 @@
 #' @inheritParams pec::predictSurvProb
 #' @importFrom pec predictSurvProb
 #' @importFrom purrr map
-#' @param times A vector of times for which predictions should be generated.
+#'
 #' @export
 predictSurvProb.pamm <- function(
   object,

R/rpexp.R

@@ -1,6 +1,7 @@
 #' Draw random numbers from piece-wise exponential distribution.
 #'
-#' This is a copy of the same function from \code{\link[msm]{rpexp}}.
+#' This is a copy of the same function from \code{rpexp} from package
+#' \pkg{msm}.
 #' Copied here to reduce dependencies.
 #'
 #' @inheritParams msm::rpexp

README.md

@@ -26,7 +26,7 @@ devtools::install_github("adibender/pammtools")
 
 **`pammtools`** facilitates the estimation of Piece-wise exponential Additive
 Mixed Models (PAMMs) for time-to-event data.
-PAMMs can be represeted as generalized additive models and can therefore
+PAMMs can be represented as generalized additive models and can therefore
 be estimated using GAM software (e.g. **`mgcv`**), which, compared to other packages for survival analysis, often offers
 more flexibility w.r.t. to the specification of covariate effects
 (e.g. non-linear, time-varying effects, cumulative effects, etc.).

cran-comments.md

@@ -1,5 +1,5 @@
 ## Test environments
-* Local R 3.6.1
+* Local Ubuntu Linux R 3.6.2
 * R release and devel with Travis CI
 * Appveyor (patched and devel)
 * win-builder

tests/testthat/test-cumulative-coefficients.R

@@ -6,14 +6,14 @@ test_that("Cumulative coefficients work", {
   df$x1 <- as.factor(rep(letters[1:3], each = nrow(df) / 3L))
 
   ## aalen model
-  library(timereg)
-  mod       <- aalen(Surv(days, status) ~ x1 + age, data = df)
-  cumu_coef_aalen <- get_cumu_coef(
-    mod,
-    df,
-    terms = c("(Intercept)", "x1"))
-  expect_data_frame(cumu_coef_aalen, nrows = 42L, ncols = 6L)
-  expect_equal(unique(cumu_coef_aalen$variable), c("(Intercept)", "x1b", "x1c"))
+  # library(timereg)
+  # mod       <- aalen(Surv(days, status) ~ x1 + age, data = df)
+  # cumu_coef_aalen <- get_cumu_coef(
+  #   mod,
+  #   df,
+  #   terms = c("(Intercept)", "x1"))
+  # expect_data_frame(cumu_coef_aalen, nrows = 42L, ncols = 6L)
+  # expect_equal(unique(cumu_coef_aalen$variable), c("(Intercept)", "x1b", "x1c"))
 
   ## pam
   ped <- as_ped(df, formula = Surv(days, status)~ x1 + age)

tests/testthat/test-predict-functions.R

@@ -10,7 +10,7 @@ context("Predict functions")
     engine = "bam", method = "fREML", discrete = TRUE)
 
   ## predictSurvProb (pec) generic
-  spmat <- predictSurvProb(pam, tumor[21:23,], times = c(90, 500, 1217))
+  spmat <- predictSurvProb.pamm(pam, tumor[21:23,], times = c(90, 500, 1217))
   expect_identical(
     round(spmat, 2),
     matrix(
@@ -24,8 +24,8 @@ context("Predict functions")
     )
   )
 
-  expect_error(predictSurvProb(pam, tumor[21:23,], times = c(90, 500, 2000)))
-  spmat2 <- predictSurvProb(pam2, tumor[21:23,], times = c(90, 500, 1217))
+  expect_error(predictSurvProb.pamm(pam, tumor[21:23,], times = c(90, 500, 2000)))
+  spmat2 <- predictSurvProb.pamm(pam2, tumor[21:23,], times = c(90, 500, 1217))
   expect_identical(round(spmat, 2), round(spmat2, 2))
 
   }

vignettes/model-evaluation.Rmd

@@ -26,7 +26,6 @@ library(ggplot2)
 theme_set(theme_bw())
 library(survival)
 library(pec)
-library(obliqueRSF)
 ```
 
 In this vignette we illustrate how to obtain model performance measures for