sandbox/cent_recompute_truth.R
In benkeser/cvtmleAUC: Cross-validated TML estimates of cross-validated area under the receiver operating characteristic curve
#! /usr/bin/env Rscript
# get environment variables
MYSCRATCH <- Sys.getenv('MYSCRATCH')
RESULTDIR <- Sys.getenv('RESULTDIR')
STEPSIZE <- as.numeric(Sys.getenv('STEPSIZE'))
TASKID <- as.numeric(Sys.getenv('SLURM_ARRAY_TASK_ID'))
# set defaults if nothing comes from environment variables
MYSCRATCH[is.na(MYSCRATCH)] <- '.'
RESULTDIR[is.na(RESULTDIR)] <- '.'
STEPSIZE[is.na(STEPSIZE)] <- 1
TASKID[is.na(TASKID)] <- 0
# get command lines arguments
args <- commandArgs(trailingOnly = TRUE)
if(length(args) < 1){
stop("Not enough arguments. Please use args 'listsize', 'prepare', 'run <itemsize>' or 'merge'")
}
ns <- c(50, 100, 250, 500)
bigB <- 500
K <- c(5, 10, 20, 40)
wrappers <- c("glm_wrapper", "randomforest_wrapper")
# wrappers <- c("glmnet_wrapper")
p <- 10
# TO DO:
# Add a replicate argument for repeated cross-validation estimators
parm <- expand.grid(seed = 1:bigB,
n = ns, K = K,
wrapper = wrappers,
stringsAsFactors = FALSE)
# load('~/cvtmleauc/out/allOut_new.RData')
# redo_idx <- which(is.na(out$est_dcvtmle))
# parm <- parm[redo_idx,]
# parm <- parm[1,,drop=FALSE]
# source in simulation Functions
source("~/cvtmleauc/makeData.R")
# load drinf
# library(glmnet)
# devtools::install_github("benkeser/cvtmleAUC", dependencies = TRUE)
library(cvtmleAUC, lib.loc = "/home/dbenkese/R/x86_64-pc-linux-gnu-library/3.4")
# library(np, lib.loc = "/home/dbenkese/R/x86_64-pc-linux-gnu-library/3.4")
# library(cvAUC)
# library(SuperLearner)
# library(data.table)
# library(glmnet)
# get the list size #########
if (args[1] == 'listsize') {
cat(nrow(parm))
}
# execute prepare job ##################
if (args[1] == 'prepare') {
parm_red <- parm[parm$K == parm$K[1] & parm$wrapper == parm$wrapper[1],]
for(i in 1:nrow(parm_red)){
set.seed(parm_red$seed[i])
dat <- makeData(n = parm_red$n[i], p = p)
save(dat, file=paste0("~/cvtmleauc/scratch/dataList",
"_n=",parm_red$n[i],
"_seed=",parm_red$seed[i],".RData"))
}
print(paste0('initial datasets saved to: ~/cvtmleauc/scratch/dataList ... .RData'))
}
# execute parallel job #################################################
if (args[1] == 'run') {
if (length(args) < 2) {
stop("Not enough arguments. 'run' needs a second argument 'id'")
}
id <- as.numeric(args[2])
print(paste(Sys.time(), "arrid:" , id, "TASKID:",
TASKID, "STEPSIZE:", STEPSIZE))
for (i in (id+TASKID):(id+TASKID+STEPSIZE-1)) {
print(paste(Sys.time(), "i:" , i))
print(parm[i,])
print(sessionInfo())
# load data
# for(i in seq_len(nrow(parm_red))){
# cat(i, "\n")
do.one <- function(i){
ns <- c(50, 100, 250, 500)
bigB <- 500
K <- c(5, 10, 20, 40)
wrappers <- c("glm_wrapper", "randomforest_wrapper")
# wrappers <- c("glmnet_wrapper")
p <- 10
# TO DO:
# Add a replicate argument for repeated cross-validation estimators
parm <- expand.grid(seed = 1:bigB,
n = ns, K = K,
wrapper = wrappers,
stringsAsFactors = FALSE)
parm_red <- parm[parm$K == parm$K[1] & parm$wrapper == "randomforest_wrapper",]
load(paste0("~/cvtmleauc/scratch/dataList_",
"n=",parm_red$n[i],
"_seed=",parm_red$seed[i],
".RData"))
# # get estimates of dcvauc
# options(np.messages = FALSE)
# n_replicates <- 20
# fit_dcv <- vector(mode = "list", length = n_replicates)
# fit_cv <- vector(mode = "list", length = n_replicates)
# for(j in seq_len(n_replicates)){
# set.seed(j)
# fit_dcv[[j]] <- cvtn_cvtmle(Y = dat$Y, X = dat$X, K = parm$K[i],
# learner = parm$wrapper[i], nested_cv = TRUE,
# nested_K = 39)
# set.seed(j)
# # get estimates of cvtn
# fit_cv[[j]] <- cvtn_cvtmle(Y = dat$Y, X = dat$X, K = parm$K[i],
# learner = parm$wrapper[i], nested_cv = FALSE,
# prediction_list = fit_dcv$prediction_list[1:parm$K[i]])
# }
# get the truth
set.seed(parm_red$seed[i])
big_n <- 1e5
big_data <- makeData(n = big_n, p = 10)
# fit on full data
fit_full <- do.call(randomforest_wrapper, args = list(train = list(X = dat$X, Y = dat$Y),
test = list(X = big_data$X, Y = big_data$Y)))
bigquantile_full <- quantile(fit_full$psi_nBn_testx[big_data$Y == 1], p = 0.05, type = 8)
big_testneg_full <- mean(fit_full$psi_nBn_testx <= bigquantile_full)
true_parameter <- big_testneg_full
for(k in c(5, 10, 20, 40)){
tryCatch({
load(paste0("~/cvtmleauc/out/outtn_",
"n=", parm_red$n[i],
"_seed=",parm_red$seed[i],
"_K=",k,
"_wrapper=randomforest_wrapper",
".RData"))
out[length(out)] <- true_parameter
save(out, file = paste0("~/cvtmleauc/out/outtn_",
"n=", parm_red$n[i],
"_seed=",parm_red$seed[i],
"_K=",k,
"_wrapper=randomforest_wrapper",
".RData.tmp"))
file.rename(paste0("~/cvtmleauc/out/outtn_",
"n=", parm_red$n[i],
"_seed=",parm_red$seed[i],
"_K=",k,
"_wrapper=randomforest_wrapper",
".RData.tmp"),
paste0("~/cvtmleauc/out/outtn_",
"n=", parm_red$n[i],
"_seed=",parm_red$seed[i],
"_K=",k,
"_wrapper=randomforest_wrapper",
".RData"))
}, error = function(e){ })
}
}
library(parallel)
cl <- makeCluster(12)
# future::plan("cluster", workers = cl)
clusterEvalQ(cl, library("randomForest"))
clusterEvalQ(cl, library("cvtmleAUC"))
clusterEvalQ(cl,source("~/cvtmleauc/makeData.R"))
# plan('multisession')
parSapply(cl = cl, X = seq_len(2000), FUN = do.one)
# bootstrap estimate
set.seed(parm$seed[i])
fit_boot <- cvtmleAUC:::boot_corrected_cvtn(Y = dat$Y, X = dat$X, learner = parm$wrapper[i])
# c together output
out <- c( # cvtmle estimates of dcvauc
fit_dcv[[1]]$est_cvtmle, fit_dcv[[1]]$se_cvtmle,
# iterations of cvtmle for dcv
# fit_dcv[[1]]$iter,
# initial plug-in estimate of dcv
fit_dcv[[1]]$est_init,
# one-step estimate of dcv
fit_dcv[[1]]$est_onestep, fit_dcv[[1]]$se_onestep,
# estimating eqn estimate of dcv
fit_dcv[[1]]$est_esteq, fit_dcv[[1]]$se_esteq,
# cvtmle estimate of cv
fit_cv[[1]]$est_cvtmle, fit_cv[[1]]$se_cvtmle,
# iterations of cvtmle for cv
# fit_cv[[1]]$iter,
fit_cv[[1]]$est_init,
# one-step estimate of cv
fit_cv[[1]]$est_onestep, fit_cv[[1]]$se_onestep,
# estimating eqn estimate of cv
fit_cv[[1]]$est_esteq, fit_cv[[1]]$se_esteq,
# full sample split estimate of cv
fit_dcv[[1]]$est_empirical, fit_dcv[[1]]$se_empirical)
# now add in MC averaged results for M = 5, 10, 20
for(M in c(5, 10, 20)){
avg_dcv <- .getMCAveragedResults(fit_dcv[1:M], logit = FALSE)
avg_cv <- .getMCAveragedResults(fit_cv[1:M], logit = FALSE)
out <- c(out,
avg_dcv$est_cvtmle, avg_dcv$se_cvtmle,
# initial plug-in estimate of dcv
avg_dcv$est_init,
# one-step estimate of dcv
avg_dcv$est_onestep, avg_dcv$se_onestep,
# estimating eqn estimate of dcv
avg_dcv$est_esteq, avg_dcv$se_esteq,
# cvtmle estimate of cv
avg_cv$est_cvtmle, avg_cv$se_cvtmle,
# iterations of cvtmle for cv
avg_cv$est_init,
# one-step estimate of cv
avg_cv$est_onestep, avg_cv$se_onestep,
# estimating eqn estimate of cv
avg_cv$est_esteq, avg_cv$se_esteq,
# full sample split estimate of cv
avg_dcv$est_empirical, avg_dcv$se_empirical)
}
out <- c(out, fit_boot[[1]], true_parameter)
# save output
save(out, file = paste0("~/cvtmleauc/out/outtn_",
"n=", parm$n[i],
"_seed=",parm$seed[i],
"_K=",parm$K[i],
"_wrapper=",parm$wrapper[i],
".RData.tmp"))
file.rename(paste0("~/cvtmleauc/out/outtn_",
"n=", parm$n[i],
"_seed=",parm$seed[i],
"_K=",parm$K[i],
"_wrapper=",parm$wrapper[i],
".RData.tmp"),
paste0("~/cvtmleauc/out/outtn_",
"n=", parm$n[i],
"_seed=",parm$seed[i],
"_K=",parm$K[i],
"_wrapper=",parm$wrapper[i],
".RData"))
}
}
# merge job ###########################
if (args[1] == 'merge') {
ns <- c(50, 100, 250, 500)
bigB <- 500
K <- c(5,10,20,40)
wrappers <- c("glm_wrapper", "randomforest_wrapper")
# wrappers <- c("glmnet_wrapper")
p <- 10
# TO DO:
# Add a replicate argument for repeated cross-validation estimators
parm <- expand.grid(seed = 1:bigB,
n = ns, K = K,
wrapper = wrappers,
stringsAsFactors = FALSE)
rslt <- matrix(NA, nrow = nrow(parm), ncol = 66 + 4)
for(i in 1:nrow(parm)){
tmp_1 <- tryCatch({
load(paste0("~/cvtmleauc/out/outtn_",
"n=", parm$n[i],
"_seed=",parm$seed[i],
"_K=",parm$K[i],
"_wrapper=",parm$wrapper[i],
".RData"))
out
}, error=function(e){
rep(NA, 66)
})
rslt[i,] <- c(parm$seed[i], parm$n[i], parm$K[i], parm$wrapper[i], tmp_1)
}
# # format
out <- data.frame(rslt, stringsAsFactors = FALSE)
repeat_names <- c("est_dcvtmle", "se_dcvtmle",
"est_dinit", "est_donestep", "se_donestep",
"est_desteq","se_desteq","est_cvtmle","se_cvtmle",
"est_init", "est_onestep", "se_onestep",
"est_esteq","se_esteq","est_emp","se_emp")
repeat_names2 <- c("est_dcvtmle", "se_dcvtmle","est_dinit",
"est_donestep", "se_donestep",
"est_desteq","se_desteq","est_cvtmle","se_cvtmle",
"est_init", "est_onestep", "se_onestep",
"est_esteq","se_esteq","est_emp","se_emp")
sim_names <- c("seed","n","K","wrapper",
paste0(repeat_names, 1),
paste0(repeat_names2, 5),
paste0(repeat_names2, 10),
paste0(repeat_names2, 20),
"est_bootstrap",
"truth")
colnames(out) <- sim_names
out[,c(1:3,5:ncol(out))] <- apply(out[,c(1:3,5:ncol(out))], 2, function(y){
as.numeric(as.character(y))})
save(out, file=paste0('~/cvtmleauc/out/allOut_cvtn.RData'))
}
# local editing
if(FALSE){
setwd("~/Dropbox/R/cvtmleauc/sandbox/")
load("allOut_cvtn.RData")
# load("~/cvtmleauc/out/allOut_new.RData")
get_sim_rslt <- function(out, parm, wrapper, truth = "truth",
estimators = c("dcvtmle1","donestep1",
"cvtmle1","onestep1","emp1",
"dcvtmle5","donestep5",
"cvtmle5","onestep5","emp5",
"dcvtmle10","donestep10",
"cvtmle10","onestep10","emp10",
"dcvtmle20","donestep20",
"cvtmle20","onestep20","emp20",
"bootstrap"
), ...){
b <- v <- m <- co <- NULL
for(i in seq_len(length(parm[,1]))){
x <- out[out$n == parm$n[i] & out$K == parm$K[i] & out$wrapper == wrapper,]
b <- rbind(b, colMeans(x[,paste0("est_",estimators)] - x[,truth], na.rm = TRUE))
v <- rbind(v, apply(x[,paste0("est_",estimators)], 2, var, na.rm = TRUE))
m <- rbind(m, colMeans((x[,paste0("est_",estimators)] - as.numeric(x[,truth]))^2, na.rm = TRUE))
# coverage
# coverage <- rep(NA, length(estimators))
# ct <- 0
# for(est in estimators){
# ct <- ct + 1
# coverage[ct] <- mean(x[,paste0("est_",est)] - 1.96 * x[,paste0("se_",est)] < x[,truth] &
# x[,paste0("est_",est)] + 1.96 * x[,paste0("se_",est)] > x[,truth], na.rm = TRUE)
# }
# co <- rbind(co, coverage)
}
parm <- cbind(parm, b, v, m) #,
# co)
colnames(parm) <- c("n", "K", paste0("bias_", estimators),
paste0("var_", estimators),
paste0("mse_", estimators)) #,
# paste0("cov_", estimators))
return(parm)
}
parm <- expand.grid(n = c(50, 100, 250, 500),
K = c(5, 10, 20, 40))
glm_rslt <- get_sim_rslt(out, parm, wrapper = "glm_wrapper")
randomforest_rslt <- get_sim_rslt(out, parm, wrapper = "randomforest_wrapper")
#---------------------------------
# Bias plots
#---------------------------------
# top row = glm bias ~ K for each n
# bottom row = random forest bias ~ K for each n
layout(matrix(1:8, nrow = 2, ncol = 4, byrow = TRUE))
par(mar = c(1.6, 0.6, 0.6, 0.6), mgp = c(2.1, 0.5, 0),
oma = c(2.1, 5.1, 2.1, 2.1))
for(n in c(50, 100, 250, 500)){
tmp <- glm_rslt[glm_rslt$n == n, ]
tmp5 <- tmp[tmp$K == 5,]
tmp10 <- tmp[tmp$K == 10,]
tmp20 <- tmp[tmp$K == 20,]
tmp40 <- tmp[tmp$K == 40,]
est <- c("bias_dcvtmle1","bias_donestep1","bias_emp1","bias_bootstrap")
grbg <- t(abs(as.matrix(rbind(tmp5[,est],tmp10[,est],tmp20[,est],tmp40[,est]))))
colnames(grbg) <- c(5,10,20,40)
if(n == 50){leg.text <- c("CVTMLE","CVOS","Empirical","Bootstrap")}else{leg.text <- FALSE}
barplot(grbg, legend.text = leg.text, args.legend = list(x = "topright"),
beside=TRUE, log = "y", yaxt = "n", ylim = c(0.0001, 200))
if(n == 50){
axis(side = 2)
mtext(outer = FALSE, side = 2, line = 2, "Absolute bias", cex = 0.75)
mtext(outer = FALSE, side = 2, line = 4, "Logistic Regression", cex = 1)
}else{
axis(side = 2, labels = FALSE)
}
mtext(outer = FALSE, side = 3, line = 0.5, paste0("n = ", n))
}
for(n in c(50, 100, 250, 500)){
tmp <- randomforest_rslt[randomforest_rslt$n == n, ]
tmp5 <- tmp[tmp$K == 5,]
tmp10 <- tmp[tmp$K == 10,]
tmp20 <- tmp[tmp$K == 20,]
tmp40 <- tmp[tmp$K == 40,]
est <- c("bias_dcvtmle1","bias_donestep1","bias_emp1","bias_bootstrap")
grbg <- t(abs(as.matrix(rbind(tmp5[,est],tmp10[,est],tmp20[,est],tmp40[,est]))))
colnames(grbg) <- c(5,10,20,40)
barplot(grbg,
beside=TRUE, log = "y", yaxt = "n", ylim = c(0.0001, 200))
mtext(side = 1, outer = FALSE, line = 2, "Number of CV Folds", cex =0.75)
if(n == 50){
axis(side = 2)
mtext(outer = FALSE, side = 2, line = 2, "Absolute bias", cex = 0.75)
mtext(outer = FALSE, side = 2, line = 4, "Random Forest", cex = 1)
}else{
axis(side = 2, labels = FALSE)
}
}
#----------------------------------
# Variance plots
#----------------------------------
layout(matrix(1:8, nrow = 2, ncol = 4, byrow = TRUE))
par(mar = c(1.6, 0.6, 0.6, 0.6), mgp = c(2.1, 0.5, 0),
oma = c(2.1, 5.1, 2.1, 2.1))
for(n in c(50, 100, 250, 500)){
tmp <- glm_rslt[glm_rslt$n == n, ]
tmp5 <- tmp[tmp$K == 5,]
tmp10 <- tmp[tmp$K == 10,]
tmp20 <- tmp[tmp$K == 20,]
tmp40 <- tmp[tmp$K == 40,]
est <- c("var_dcvtmle1","var_donestep1","var_emp1","var_bootstrap")
grbg <- t(as.matrix(rbind(tmp5[,est],tmp10[,est],tmp20[,est],tmp40[,est])))
colnames(grbg) <- c(5,10,20,40)
if(n == 50){leg.text <- c("CVTMLE","CVOS","Empirical","Bootstrap")}else{leg.text <- FALSE}
barplot(grbg, legend.text = leg.text, args.legend = list(x = "topright"),
beside=TRUE, log = "y", yaxt = "n", ylim = c(0.0001, 50))
if(n == 50){
axis(side = 2)
mtext(outer = FALSE, side = 2, line = 2, "Variance", cex = 0.75)
mtext(outer = FALSE, side = 2, line = 4, "Logistic Regression", cex = 1)
}else{
axis(side = 2, labels = FALSE)
}
mtext(outer = FALSE, side = 3, line = 0.5, paste0("n = ", n))
}
for(n in c(50, 100, 250, 500)){
tmp <- randomforest_rslt[randomforest_rslt$n == n, ]
tmp5 <- tmp[tmp$K == 5,]
tmp10 <- tmp[tmp$K == 10,]
tmp20 <- tmp[tmp$K == 20,]
tmp40 <- tmp[tmp$K == 40,]
est <- c("var_dcvtmle1","var_donestep1","var_emp1","var_bootstrap")
grbg <- t(as.matrix(rbind(tmp5[,est],tmp10[,est],tmp20[,est],tmp40[,est])))
colnames(grbg) <- c(5,10,20,40)
barplot(grbg,
beside=TRUE, log = "y", yaxt = "n", ylim = c(0.0001, 50))
mtext(side = 1, outer = FALSE, line = 2, "Number of CV Folds", cex =0.75)
if(n == 50){
axis(side = 2)
mtext(outer = FALSE, side = 2, line = 2, "Variance", cex = 0.75)
mtext(outer = FALSE, side = 2, line = 4, "Random Forest", cex = 1)
}else{
axis(side = 2, labels = FALSE)
}
}
#----------------------------------
# MSE plots
#----------------------------------
layout(matrix(1:8, nrow = 2, ncol = 4, byrow = TRUE))
par(mar = c(1.6, 0.6, 0.6, 0.6), mgp = c(2.1, 0.5, 0),
oma = c(2.1, 5.1, 2.1, 2.1))
for(n in c(50, 100, 250, 500)){
tmp <- glm_rslt[glm_rslt$n == n, ]
tmp5 <- tmp[tmp$K == 5,]
tmp10 <- tmp[tmp$K == 10,]
tmp20 <- tmp[tmp$K == 20,]
tmp40 <- tmp[tmp$K == 40,]
est <- c("mse_dcvtmle1","mse_donestep1","mse_emp1","mse_bootstrap")
grbg <- t(as.matrix(rbind(tmp5[,est],tmp10[,est],tmp20[,est],tmp40[,est])))
colnames(grbg) <- c(5,10,20,40)
if(n == 50){leg.text <- c("CVTMLE","CVOS","Empirical","Bootstrap")}else{leg.text <- FALSE}
barplot(grbg, legend.text = leg.text, args.legend = list(x = "topright"),
beside=TRUE, log = "y", yaxt = "n", ylim = c(0.0001, 50))
if(n == 50){
axis(side = 2)
mtext(outer = FALSE, side = 2, line = 2, "Variance", cex = 0.75)
mtext(outer = FALSE, side = 2, line = 4, "Logistic Regression", cex = 1)
}else{
axis(side = 2, labels = FALSE)
}
mtext(outer = FALSE, side = 3, line = 0.5, paste0("n = ", n))
}
for(n in c(50, 100, 250, 500)){
tmp <- randomforest_rslt[randomforest_rslt$n == n, ]
tmp5 <- tmp[tmp$K == 5,]
tmp10 <- tmp[tmp$K == 10,]
tmp20 <- tmp[tmp$K == 20,]
tmp40 <- tmp[tmp$K == 40,]
est <- c("mse_dcvtmle1","mse_donestep1","mse_emp1","mse_bootstrap")
grbg <- t(as.matrix(rbind(tmp5[,est],tmp10[,est],tmp20[,est],tmp40[,est])))
colnames(grbg) <- c(5,10,20,40)
barplot(grbg,
beside=TRUE, log = "y", yaxt = "n", ylim = c(0.0001, 50))
mtext(side = 1, outer = FALSE, line = 2, "Number of CV Folds", cex =0.75)
if(n == 50){
axis(side = 2)
mtext(outer = FALSE, side = 2, line = 2, "Variance", cex = 0.75)
mtext(outer = FALSE, side = 2, line = 4, "Random Forest", cex = 1)
}else{
axis(side = 2, labels = FALSE)
}
}
make_side_by_side_bar_plots <- function(glm_rslt, randomforest_rslt,
est, est_labels,
yaxis_label,
xaxis_label = "Number CV Folds",
absolute_val = TRUE, ... ){
layout(matrix(1:8, nrow = 2, ncol = 4, byrow = TRUE))
par(mar = c(1.6, 0.6, 0.6, 0.6), mgp = c(2.1, 0.5, 0),
oma = c(2.1, 5.1, 2.1, 2.1))
for(n in c(50, 100, 250, 500)){
tmp <- glm_rslt[glm_rslt$n == n, ]
tmp5 <- tmp[tmp$K == 5,]
tmp10 <- tmp[tmp$K == 10,]
tmp20 <- tmp[tmp$K == 20,]
tmp40 <- tmp[tmp$K == 40,]
# est <- c("mse_dcvtmle1","mse_donestep1","mse_emp1","mse_bootstrap")
grbg <- t(as.matrix(rbind(tmp5[,est],tmp10[,est],tmp20[,est],tmp40[,est])))
if(absolute_val){
grbg <- abs(grbg)
}
colnames(grbg) <- c(5,10,20,40)
if(n == 50){leg.text <- est_labels}else{leg.text <- FALSE}
barplot(grbg, legend.text = leg.text, args.legend = list(x = "topright"),
beside=TRUE, log = "y", yaxt = "n", ... )
if(n == 50){
axis(side = 2)
mtext(outer = FALSE, side = 2, line = 2, yaxis_label, cex = 0.75)
mtext(outer = FALSE, side = 2, line = 4, "Logistic Regression", cex = 1)
}else{
axis(side = 2, labels = FALSE)
}
mtext(outer = FALSE, side = 3, line = 0.5, paste0("n = ", n))
}
for(n in c(50, 100, 250, 500)){
tmp <- randomforest_rslt[randomforest_rslt$n == n, ]
tmp5 <- tmp[tmp$K == 5,]
tmp10 <- tmp[tmp$K == 10,]
tmp20 <- tmp[tmp$K == 20,]
tmp40 <- tmp[tmp$K == 40,]
# est <- c("mse_dcvtmle1","mse_donestep1","mse_emp1","mse_bootstrap")
grbg <- t(as.matrix(rbind(tmp5[,est],tmp10[,est],tmp20[,est],tmp40[,est])))
if(absolute_val){
grbg <- abs(grbg)
}
colnames(grbg) <- c(5,10,20,40)
barplot(grbg, beside=TRUE, log = "y", yaxt = "n", ...)
mtext(side = 1, outer = FALSE, line = 2, xaxis_label, cex =0.75)
if(n == 50){
axis(side = 2)
mtext(outer = FALSE, side = 2, line = 2, yaxis_label, cex = 0.75)
mtext(outer = FALSE, side = 2, line = 4, "Random Forest", cex = 1)
}else{
axis(side = 2, labels = FALSE)
}
}
}
# bias
make_side_by_side_bar_plots(glm_rslt, randomforest_rslt,
est = c("bias_dcvtmle1","bias_donestep1",
"bias_emp1","bias_bootstrap"),
est_label = c("CVTMLE", "CVOS","Empirical","Bootstrap"),
ylim = c(0.0001,100), yaxis_label = "Absolute Bias")
# variance
make_side_by_side_bar_plots(glm_rslt, randomforest_rslt,
est = c("var_dcvtmle1","var_donestep1",
"var_emp1","var_bootstrap"),
est_label = c("CVTMLE", "CVOS","Empirical","Bootstrap"),
ylim = c(0.0001,50), yaxis_label = "Variance")
# mse
make_side_by_side_bar_plots(glm_rslt, randomforest_rslt,
est = c("mse_dcvtmle1","mse_donestep1",
"mse_emp1","mse_bootstrap"),
est_label = c("CVTMLE", "CVOS","Empirical","Bootstrap"),
ylim = c(0.0001,100), yaxis_label = "Mean squared-error")
# bias by CV Repeats for CVTMLE
make_side_by_side_bar_plots(glm_rslt, randomforest_rslt,
est = c("bias_dcvtmle1","bias_dcvtmle5",
"bias_dcvtmle10","bias_dcvtmle20"),
est_label = paste0("CVTMLE ", c(1,5,10,20)," repeats"),
ylim = c(0.0001,100), yaxis_label = "Absolute bias")
# variance by CV Repeats for CVTMLE
make_side_by_side_bar_plots(glm_rslt, randomforest_rslt,
est = c("var_dcvtmle1","var_dcvtmle5",
"var_dcvtmle10","var_dcvtmle20"),
est_label = paste0("CVTMLE ", c(1,5,10,20)," repeats"),
ylim = c(0.0001,0.01), yaxis_label = "Variance")
# mse by CV Repeats for CVTMLE
make_side_by_side_bar_plots(glm_rslt, randomforest_rslt,
est = c("mse_dcvtmle1","mse_dcvtmle5",
"mse_dcvtmle10","mse_dcvtmle20"),
est_label = paste0("CVTMLE ", c(1,5,10,20)," repeats"),
ylim = c(0.0001,0.01), yaxis_label = "Variance")
# compare mse of cvtmle with 40 folds to mse of empirical with 5 folds
make_mse_compare_one_repeat <- function(rslt, B, legend = FALSE){
cvt_n_glm <- rslt[,paste0("mse_dcvtmle",B)][rslt$K == 40][2:4]
cvo_n_glm <- rslt[,paste0("mse_donestep",B)][rslt$K == 40][2:4]
emp_n_glm <- rslt[,paste0("mse_emp",B)][rslt$K == 5][2:4]
plot(y = cvt_n_glm/emp_n_glm, x = 1:3, xaxt = "n", yaxt = "n", bty = "n",
xlim = c(1,3), ylim = c(0,2), type = "b",
xlab = "Sample size", ylab = "MSE / Empirical with K = 5")
axis(side = 1, at = 1:3, labels = c(100, 250, 500))
axis(side = 2)
abline(h = 1, lty = 3)
points(y = cvo_n_glm/emp_n_glm, x = 1:3, pch = 2, type = "b", lty = 2)
if(legend){
legend(x = "topleft", c("CVTMLE, K = 40", "CVOS, K = 40"), pch = 1:2,
bty = "n")
}
}
layout(matrix(1:8, nrow = 2, ncol = 4, byrow = TRUE))
for(b in c(1, 5, 10, 20)){
make_mse_compare_one_repeat(rslt = glm_rslt, b, legend = ifelse(b == 1, TRUE, FALSE))
mtext(side = 3, text = paste0("MC Repeats = ", b))
}
for(b in c(1, 5, 10, 20)){
make_mse_compare_one_repeat(rslt = randomforest_rslt, b, legend = ifelse(b == 1, TRUE, FALSE))
}
cvt_n_glm <- glm_rslt$mse_dcvtmle1[glm_rslt$K == 40]
cvt_n_glm <- glm_rslt$mse_dcvtmle1[glm_rslt$K == 40]
ratio_cvtmle_to_mse <-
#---------------------------------
# Bias plots
#---------------------------------
# top row = glm bias ~ K for each n
# bottom row = random forest bias ~ K for each n
layout(matrix(1:8, nrow = 2, ncol = 4, byrow = TRUE))
par(mar = c(1.6, 0.6, 0.6, 0.6), mgp = c(2.1, 0.5, 0),
oma = c(2.1, 5.1, 2.1, 2.1))
for(n in c(50, 100, 250, 500)){
tmp <- glm_rslt[glm_rslt$n == n, ]
tmp5 <- tmp[tmp$K == 5,]
tmp10 <- tmp[tmp$K == 10,]
tmp20 <- tmp[tmp$K == 20,]
tmp40 <- tmp[tmp$K == 40,]
est <- c("bias_dcvtmle1","bias_donestep1","bias_emp1")
grbg <- t(abs(as.matrix(rbind(tmp5[,est],tmp10[,est],tmp20[,est],tmp40[,est]))))
colnames(grbg) <- c(5,10,20,40)
barplot(grbg,
beside=TRUE, log = "y", yaxt = "n", ylim = c(0.0001, 200))
if(n == 50){
axis(side = 2)
mtext(outer = FALSE, side = 2, line = 2, "Absolute bias", cex = 0.75)
mtext(outer = FALSE, side = 2, line = 4, "Logistic Regression", cex = 1)
}else{
axis(side = 2, labels = FALSE)
}
mtext(outer = FALSE, side = 3, line = 0.5, paste0("n = ", n))
}
for(n in c(50, 100, 250, 500)){
tmp <- randomforest_rslt[randomforest_rslt$n == n, ]
tmp5 <- tmp[tmp$K == 5,]
tmp10 <- tmp[tmp$K == 10,]
tmp20 <- tmp[tmp$K == 20,]
tmp40 <- tmp[tmp$K == 40,]
est <- c("bias_dcvtmle1","bias_donestep1","bias_emp1")
grbg <- t(abs(as.matrix(rbind(tmp5[,est],tmp10[,est],tmp20[,est],tmp40[,est]))))
colnames(grbg) <- c(5,10,20,40)
barplot(grbg,
beside=TRUE, log = "y", yaxt = "n", ylim = c(0.0001, 200))
mtext(side = 1, outer = FALSE, line = 2, "Number of CV Folds", cex =0.75)
if(n == 50){
axis(side = 2)
mtext(outer = FALSE, side = 2, line = 2, "Absolute bias", cex = 0.75)
mtext(outer = FALSE, side = 2, line = 4, "Random Forest", cex = 1)
}else{
axis(side = 2, labels = FALSE)
}
}
#--------------------------------
# MSE plots
#--------------------------------
make_mse_compare_plot <- function(rslt, est1, est2, ns = c(100, 250, 500, 750),
Ks = c(5, 10, 20, 40),...){
# make matrix of relative MSE
n_ct <- 0
K_ct <- 0
rel_mse <- matrix(NA, length(ns), length(Ks))
for(n in ns){
n_ct <- n_ct + 1
for(K in Ks){
K_ct <- K_ct + 1
rel_mse[n_ct, K_ct] <- rslt[rslt$n == n & rslt$K == K, paste0("mse_",est1)] /
rslt[rslt$n == n & rslt$K == K, paste0("mse_",est2)]
}
K_ct <- 0
}
row.names(rel_mse) <- ns
colnames(rel_mse) <- Ks
superheat::superheat(X = rel_mse, X.text = round(rel_mse, 2), scale = FALSE,
pretty.order.rows = FALSE,
pretty.order.cols = FALSE, heat.col.scheme = "red",
row.title = "Sample size", column.title = "CV folds",
title = paste0("MSE(",est1,")/MSE(",est2,")"),
# plot_done()
legend.breaks = seq(min(rel_mse), max(rel_mse), by = 0.1), ...)
}
# CV TMLE vs. empirical
for(rslt in c("glm_rslt","randomforest_rslt","glmnet_rslt","stepglm_rslt")){
pdf(paste0("~/cvtmleauc/",rslt,"_perf.pdf"))
# comparing to emp
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "dcvtmle", est2 = "emp")
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "cvtmle", est2 = "emp")
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "desteq", est2 = "emp")
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "esteq", est2 = "emp")
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "donestep", est2 = "emp")
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "onestep", est2 = "emp")
# comparing to eachother
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "onestep", est2 = "cvtmle")
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "donestep", est2 = "dcvtmle")
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "onestep", est2 = "esteq")
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "donestep", est2 = "desteq")
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "esteq", est2 = "cvtmle")
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "desteq", est2 = "dcvtmle")
# comparing cv to dcv
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "onestep", est2 = "donestep")
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "cvtmle", est2 = "dcvtmle")
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "esteq", est2 = "desteq")
dev.off()
}
#--------------------------------
# CV TMLE vs. Empirical
#--------------------------------
pdf("mse_results.pdf")
superheat(X = rel_mse_cvtmle, X.text = round(rel_mse_cvtmle, 2), scale = FALSE,
pretty.order.rows = FALSE,
pretty.order.cols = FALSE, heat.col.scheme = "red",
row.title = "Sample size", column.title = "CV folds",
legend.breaks = c(0.7, 0.8, 0.9, 1),
title = "MSE CVTMLE / MSE Empirical")
#--------------------------------
# One step vs. Empirical
#--------------------------------
superheat(X = rel_mse_onestep, X.text = round(rel_mse_onestep, 2), scale = FALSE,
pretty.order.rows = FALSE,
pretty.order.cols = FALSE, heat.col.scheme = "red",
row.title = "Sample size", column.title = "CV folds",
legend.breaks = c(0.7, 0.8, 0.9, 1),
title = "MSE CV One step / MSE Empirical")
#--------------------------------
# CVTMLE vs. Onestep
#--------------------------------
superheat(X = rel_mse_tmlevonestep, X.text = round(rel_mse_tmlevonestep, 2),
scale = FALSE,
pretty.order.rows = FALSE,
pretty.order.cols = FALSE, heat.col.scheme = "red",
row.title = "Sample size", column.title = "CV folds",
legend.breaks = c(0.7, 0.8, 0.9, 1),
title = "MSE CV One step / MSE CVTMLE")
dev.off()
#--------------------------------
# Coverage plots
#--------------------------------
# make matrix of relative MSE
n_ct <- 0
K_ct <- 0
cov_cvtmle <- matrix(NA, 4, 4)
cov_onestep <- matrix(NA, 4, 4)
cov_tmlevonestep <- matrix(NA, 4, 4)
for(n in c(100, 250, 500, 750)){
n_ct <- n_ct + 1
for(K in c(5, 10, 20, 30)){
K_ct <- K_ct + 1
cov_cvtmle[n_ct, K_ct] <- parm$cov_cvtmle[parm$n == n & parm$K == K]
cov_onestep[n_ct, K_ct] <- parm$cov_onestep[parm$n == n & parm$K == K]
cov_tmlevonestep[n_ct, K_ct] <- parm$cov_empirical[parm$n == n & parm$K == K]
}
K_ct <- 0
}
row.names(cov_cvtmle) <- row.names(cov_onestep) <- row.names(cov_tmlevonestep) <- c(100, 250, 500, 750)
colnames(cov_cvtmle) <- colnames(cov_onestep) <- colnames(cov_tmlevonestep) <- c(5, 10, 20, 30)
#--------------------------------
# CV TMLE vs. Empirical
#--------------------------------
pdf("coverage_results.pdf")
superheat(X = cov_cvtmle, X.text = round(cov_cvtmle, 2), scale = FALSE,
pretty.order.rows = FALSE,
pretty.order.cols = FALSE, heat.col.scheme = "red",
row.title = "Sample size", column.title = "CV folds",
legend.breaks = c(0.7, 0.8, 0.9, 1),
title = "Coverage of nominal 95% CI CVTMLE")
#--------------------------------
# One step vs. Empirical
#--------------------------------
superheat(X = cov_onestep, X.text = round(cov_onestep, 2), scale = FALSE,
pretty.order.rows = FALSE,
pretty.order.cols = FALSE, heat.col.scheme = "red",
row.title = "Sample size", column.title = "CV folds",
legend.breaks = c(0.7, 0.8, 0.9, 1),
title = "Coverage of nominal 95% CI One step")
#--------------------------------
# CVTMLE vs. Onestep
#--------------------------------
superheat(X = cov_tmlevonestep, X.text = round(cov_tmlevonestep, 2),
scale = FALSE,
pretty.order.rows = FALSE,
pretty.order.cols = FALSE, heat.col.scheme = "red",
row.title = "Sample size", column.title = "CV folds",
legend.breaks = c(0.7, 0.8, 0.9, 1),
title = "Coverage of nominal 95% CI Empirical")
dev.off()
}
benkeser/cvtmleAUC documentation built on May 16, 2019, 2:30 a.m.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
#! /usr/bin/env Rscript
# get environment variables
MYSCRATCH <- Sys.getenv('MYSCRATCH')
RESULTDIR <- Sys.getenv('RESULTDIR')
STEPSIZE <- as.numeric(Sys.getenv('STEPSIZE'))
TASKID <- as.numeric(Sys.getenv('SLURM_ARRAY_TASK_ID'))
# set defaults if nothing comes from environment variables
MYSCRATCH[is.na(MYSCRATCH)] <- '.'
RESULTDIR[is.na(RESULTDIR)] <- '.'
STEPSIZE[is.na(STEPSIZE)] <- 1
TASKID[is.na(TASKID)] <- 0
# get command lines arguments
args <- commandArgs(trailingOnly = TRUE)
if(length(args) < 1){
stop("Not enough arguments. Please use args 'listsize', 'prepare', 'run <itemsize>' or 'merge'")
}
ns <- c(50, 100, 250, 500)
bigB <- 500
K <- c(5, 10, 20, 40)
wrappers <- c("glm_wrapper", "randomforest_wrapper")
# wrappers <- c("glmnet_wrapper")
p <- 10
# TO DO:
# Add a replicate argument for repeated cross-validation estimators
parm <- expand.grid(seed = 1:bigB,
n = ns, K = K,
wrapper = wrappers,
stringsAsFactors = FALSE)
# load('~/cvtmleauc/out/allOut_new.RData')
# redo_idx <- which(is.na(out$est_dcvtmle))
# parm <- parm[redo_idx,]
# parm <- parm[1,,drop=FALSE]
# source in simulation Functions
source("~/cvtmleauc/makeData.R")
# load drinf
# library(glmnet)
# devtools::install_github("benkeser/cvtmleAUC", dependencies = TRUE)
library(cvtmleAUC, lib.loc = "/home/dbenkese/R/x86_64-pc-linux-gnu-library/3.4")
# library(np, lib.loc = "/home/dbenkese/R/x86_64-pc-linux-gnu-library/3.4")
# library(cvAUC)
# library(SuperLearner)
# library(data.table)
# library(glmnet)
# get the list size #########
if (args[1] == 'listsize') {
cat(nrow(parm))
}
# execute prepare job ##################
if (args[1] == 'prepare') {
parm_red <- parm[parm$K == parm$K[1] & parm$wrapper == parm$wrapper[1],]
for(i in 1:nrow(parm_red)){
set.seed(parm_red$seed[i])
dat <- makeData(n = parm_red$n[i], p = p)
save(dat, file=paste0("~/cvtmleauc/scratch/dataList",
"_n=",parm_red$n[i],
"_seed=",parm_red$seed[i],".RData"))
}
print(paste0('initial datasets saved to: ~/cvtmleauc/scratch/dataList ... .RData'))
}
# execute parallel job #################################################
if (args[1] == 'run') {
if (length(args) < 2) {
stop("Not enough arguments. 'run' needs a second argument 'id'")
}
id <- as.numeric(args[2])
print(paste(Sys.time(), "arrid:" , id, "TASKID:",
TASKID, "STEPSIZE:", STEPSIZE))
for (i in (id+TASKID):(id+TASKID+STEPSIZE-1)) {
print(paste(Sys.time(), "i:" , i))
print(parm[i,])
print(sessionInfo())
# load data
# for(i in seq_len(nrow(parm_red))){
# cat(i, "\n")
do.one <- function(i){
ns <- c(50, 100, 250, 500)
bigB <- 500
K <- c(5, 10, 20, 40)
wrappers <- c("glm_wrapper", "randomforest_wrapper")
# wrappers <- c("glmnet_wrapper")
p <- 10
# TO DO:
# Add a replicate argument for repeated cross-validation estimators
parm <- expand.grid(seed = 1:bigB,
n = ns, K = K,
wrapper = wrappers,
stringsAsFactors = FALSE)
parm_red <- parm[parm$K == parm$K[1] & parm$wrapper == "randomforest_wrapper",]
load(paste0("~/cvtmleauc/scratch/dataList_",
"n=",parm_red$n[i],
"_seed=",parm_red$seed[i],
".RData"))
# # get estimates of dcvauc
# options(np.messages = FALSE)
# n_replicates <- 20
# fit_dcv <- vector(mode = "list", length = n_replicates)
# fit_cv <- vector(mode = "list", length = n_replicates)
# for(j in seq_len(n_replicates)){
# set.seed(j)
# fit_dcv[[j]] <- cvtn_cvtmle(Y = dat$Y, X = dat$X, K = parm$K[i],
# learner = parm$wrapper[i], nested_cv = TRUE,
# nested_K = 39)
# set.seed(j)
# # get estimates of cvtn
# fit_cv[[j]] <- cvtn_cvtmle(Y = dat$Y, X = dat$X, K = parm$K[i],
# learner = parm$wrapper[i], nested_cv = FALSE,
# prediction_list = fit_dcv$prediction_list[1:parm$K[i]])
# }
# get the truth
set.seed(parm_red$seed[i])
big_n <- 1e5
big_data <- makeData(n = big_n, p = 10)
# fit on full data
fit_full <- do.call(randomforest_wrapper, args = list(train = list(X = dat$X, Y = dat$Y),
test = list(X = big_data$X, Y = big_data$Y)))
bigquantile_full <- quantile(fit_full$psi_nBn_testx[big_data$Y == 1], p = 0.05, type = 8)
big_testneg_full <- mean(fit_full$psi_nBn_testx <= bigquantile_full)
true_parameter <- big_testneg_full
for(k in c(5, 10, 20, 40)){
tryCatch({
load(paste0("~/cvtmleauc/out/outtn_",
"n=", parm_red$n[i],
"_seed=",parm_red$seed[i],
"_K=",k,
"_wrapper=randomforest_wrapper",
".RData"))
out[length(out)] <- true_parameter
save(out, file = paste0("~/cvtmleauc/out/outtn_",
"n=", parm_red$n[i],
"_seed=",parm_red$seed[i],
"_K=",k,
"_wrapper=randomforest_wrapper",
".RData.tmp"))
file.rename(paste0("~/cvtmleauc/out/outtn_",
"n=", parm_red$n[i],
"_seed=",parm_red$seed[i],
"_K=",k,
"_wrapper=randomforest_wrapper",
".RData.tmp"),
paste0("~/cvtmleauc/out/outtn_",
"n=", parm_red$n[i],
"_seed=",parm_red$seed[i],
"_K=",k,
"_wrapper=randomforest_wrapper",
".RData"))
}, error = function(e){ })
}
}
library(parallel)
cl <- makeCluster(12)
# future::plan("cluster", workers = cl)
clusterEvalQ(cl, library("randomForest"))
clusterEvalQ(cl, library("cvtmleAUC"))
clusterEvalQ(cl,source("~/cvtmleauc/makeData.R"))
# plan('multisession')
parSapply(cl = cl, X = seq_len(2000), FUN = do.one)
# bootstrap estimate
set.seed(parm$seed[i])
fit_boot <- cvtmleAUC:::boot_corrected_cvtn(Y = dat$Y, X = dat$X, learner = parm$wrapper[i])
# c together output
out <- c( # cvtmle estimates of dcvauc
fit_dcv[[1]]$est_cvtmle, fit_dcv[[1]]$se_cvtmle,
# iterations of cvtmle for dcv
# fit_dcv[[1]]$iter,
# initial plug-in estimate of dcv
fit_dcv[[1]]$est_init,
# one-step estimate of dcv
fit_dcv[[1]]$est_onestep, fit_dcv[[1]]$se_onestep,
# estimating eqn estimate of dcv
fit_dcv[[1]]$est_esteq, fit_dcv[[1]]$se_esteq,
# cvtmle estimate of cv
fit_cv[[1]]$est_cvtmle, fit_cv[[1]]$se_cvtmle,
# iterations of cvtmle for cv
# fit_cv[[1]]$iter,
fit_cv[[1]]$est_init,
# one-step estimate of cv
fit_cv[[1]]$est_onestep, fit_cv[[1]]$se_onestep,
# estimating eqn estimate of cv
fit_cv[[1]]$est_esteq, fit_cv[[1]]$se_esteq,
# full sample split estimate of cv
fit_dcv[[1]]$est_empirical, fit_dcv[[1]]$se_empirical)
# now add in MC averaged results for M = 5, 10, 20
for(M in c(5, 10, 20)){
avg_dcv <- .getMCAveragedResults(fit_dcv[1:M], logit = FALSE)
avg_cv <- .getMCAveragedResults(fit_cv[1:M], logit = FALSE)
out <- c(out,
avg_dcv$est_cvtmle, avg_dcv$se_cvtmle,
# initial plug-in estimate of dcv
avg_dcv$est_init,
# one-step estimate of dcv
avg_dcv$est_onestep, avg_dcv$se_onestep,
# estimating eqn estimate of dcv
avg_dcv$est_esteq, avg_dcv$se_esteq,
# cvtmle estimate of cv
avg_cv$est_cvtmle, avg_cv$se_cvtmle,
# iterations of cvtmle for cv
avg_cv$est_init,
# one-step estimate of cv
avg_cv$est_onestep, avg_cv$se_onestep,
# estimating eqn estimate of cv
avg_cv$est_esteq, avg_cv$se_esteq,
# full sample split estimate of cv
avg_dcv$est_empirical, avg_dcv$se_empirical)
}
out <- c(out, fit_boot[[1]], true_parameter)
# save output
save(out, file = paste0("~/cvtmleauc/out/outtn_",
"n=", parm$n[i],
"_seed=",parm$seed[i],
"_K=",parm$K[i],
"_wrapper=",parm$wrapper[i],
".RData.tmp"))
file.rename(paste0("~/cvtmleauc/out/outtn_",
"n=", parm$n[i],
"_seed=",parm$seed[i],
"_K=",parm$K[i],
"_wrapper=",parm$wrapper[i],
".RData.tmp"),
paste0("~/cvtmleauc/out/outtn_",
"n=", parm$n[i],
"_seed=",parm$seed[i],
"_K=",parm$K[i],
"_wrapper=",parm$wrapper[i],
".RData"))
}
}
# merge job ###########################
if (args[1] == 'merge') {
ns <- c(50, 100, 250, 500)
bigB <- 500
K <- c(5,10,20,40)
wrappers <- c("glm_wrapper", "randomforest_wrapper")
# wrappers <- c("glmnet_wrapper")
p <- 10
# TO DO:
# Add a replicate argument for repeated cross-validation estimators
parm <- expand.grid(seed = 1:bigB,
n = ns, K = K,
wrapper = wrappers,
stringsAsFactors = FALSE)
rslt <- matrix(NA, nrow = nrow(parm), ncol = 66 + 4)
for(i in 1:nrow(parm)){
tmp_1 <- tryCatch({
load(paste0("~/cvtmleauc/out/outtn_",
"n=", parm$n[i],
"_seed=",parm$seed[i],
"_K=",parm$K[i],
"_wrapper=",parm$wrapper[i],
".RData"))
out
}, error=function(e){
rep(NA, 66)
})
rslt[i,] <- c(parm$seed[i], parm$n[i], parm$K[i], parm$wrapper[i], tmp_1)
}
# # format
out <- data.frame(rslt, stringsAsFactors = FALSE)
repeat_names <- c("est_dcvtmle", "se_dcvtmle",
"est_dinit", "est_donestep", "se_donestep",
"est_desteq","se_desteq","est_cvtmle","se_cvtmle",
"est_init", "est_onestep", "se_onestep",
"est_esteq","se_esteq","est_emp","se_emp")
repeat_names2 <- c("est_dcvtmle", "se_dcvtmle","est_dinit",
"est_donestep", "se_donestep",
"est_desteq","se_desteq","est_cvtmle","se_cvtmle",
"est_init", "est_onestep", "se_onestep",
"est_esteq","se_esteq","est_emp","se_emp")
sim_names <- c("seed","n","K","wrapper",
paste0(repeat_names, 1),
paste0(repeat_names2, 5),
paste0(repeat_names2, 10),
paste0(repeat_names2, 20),
"est_bootstrap",
"truth")
colnames(out) <- sim_names
out[,c(1:3,5:ncol(out))] <- apply(out[,c(1:3,5:ncol(out))], 2, function(y){
as.numeric(as.character(y))})
save(out, file=paste0('~/cvtmleauc/out/allOut_cvtn.RData'))
}
# local editing
if(FALSE){
setwd("~/Dropbox/R/cvtmleauc/sandbox/")
load("allOut_cvtn.RData")
# load("~/cvtmleauc/out/allOut_new.RData")
get_sim_rslt <- function(out, parm, wrapper, truth = "truth",
estimators = c("dcvtmle1","donestep1",
"cvtmle1","onestep1","emp1",
"dcvtmle5","donestep5",
"cvtmle5","onestep5","emp5",
"dcvtmle10","donestep10",
"cvtmle10","onestep10","emp10",
"dcvtmle20","donestep20",
"cvtmle20","onestep20","emp20",
"bootstrap"
), ...){
b <- v <- m <- co <- NULL
for(i in seq_len(length(parm[,1]))){
x <- out[out$n == parm$n[i] & out$K == parm$K[i] & out$wrapper == wrapper,]
b <- rbind(b, colMeans(x[,paste0("est_",estimators)] - x[,truth], na.rm = TRUE))
v <- rbind(v, apply(x[,paste0("est_",estimators)], 2, var, na.rm = TRUE))
m <- rbind(m, colMeans((x[,paste0("est_",estimators)] - as.numeric(x[,truth]))^2, na.rm = TRUE))
# coverage
# coverage <- rep(NA, length(estimators))
# ct <- 0
# for(est in estimators){
# ct <- ct + 1
# coverage[ct] <- mean(x[,paste0("est_",est)] - 1.96 * x[,paste0("se_",est)] < x[,truth] &
# x[,paste0("est_",est)] + 1.96 * x[,paste0("se_",est)] > x[,truth], na.rm = TRUE)
# }
# co <- rbind(co, coverage)
}
parm <- cbind(parm, b, v, m) #,
# co)
colnames(parm) <- c("n", "K", paste0("bias_", estimators),
paste0("var_", estimators),
paste0("mse_", estimators)) #,
# paste0("cov_", estimators))
return(parm)
}
parm <- expand.grid(n = c(50, 100, 250, 500),
K = c(5, 10, 20, 40))
glm_rslt <- get_sim_rslt(out, parm, wrapper = "glm_wrapper")
randomforest_rslt <- get_sim_rslt(out, parm, wrapper = "randomforest_wrapper")
#---------------------------------
# Bias plots
#---------------------------------
# top row = glm bias ~ K for each n
# bottom row = random forest bias ~ K for each n
layout(matrix(1:8, nrow = 2, ncol = 4, byrow = TRUE))
par(mar = c(1.6, 0.6, 0.6, 0.6), mgp = c(2.1, 0.5, 0),
oma = c(2.1, 5.1, 2.1, 2.1))
for(n in c(50, 100, 250, 500)){
tmp <- glm_rslt[glm_rslt$n == n, ]
tmp5 <- tmp[tmp$K == 5,]
tmp10 <- tmp[tmp$K == 10,]
tmp20 <- tmp[tmp$K == 20,]
tmp40 <- tmp[tmp$K == 40,]
est <- c("bias_dcvtmle1","bias_donestep1","bias_emp1","bias_bootstrap")
grbg <- t(abs(as.matrix(rbind(tmp5[,est],tmp10[,est],tmp20[,est],tmp40[,est]))))
colnames(grbg) <- c(5,10,20,40)
if(n == 50){leg.text <- c("CVTMLE","CVOS","Empirical","Bootstrap")}else{leg.text <- FALSE}
barplot(grbg, legend.text = leg.text, args.legend = list(x = "topright"),
beside=TRUE, log = "y", yaxt = "n", ylim = c(0.0001, 200))
if(n == 50){
axis(side = 2)
mtext(outer = FALSE, side = 2, line = 2, "Absolute bias", cex = 0.75)
mtext(outer = FALSE, side = 2, line = 4, "Logistic Regression", cex = 1)
}else{
axis(side = 2, labels = FALSE)
}
mtext(outer = FALSE, side = 3, line = 0.5, paste0("n = ", n))
}
for(n in c(50, 100, 250, 500)){
tmp <- randomforest_rslt[randomforest_rslt$n == n, ]
tmp5 <- tmp[tmp$K == 5,]
tmp10 <- tmp[tmp$K == 10,]
tmp20 <- tmp[tmp$K == 20,]
tmp40 <- tmp[tmp$K == 40,]
est <- c("bias_dcvtmle1","bias_donestep1","bias_emp1","bias_bootstrap")
grbg <- t(abs(as.matrix(rbind(tmp5[,est],tmp10[,est],tmp20[,est],tmp40[,est]))))
colnames(grbg) <- c(5,10,20,40)
barplot(grbg,
beside=TRUE, log = "y", yaxt = "n", ylim = c(0.0001, 200))
mtext(side = 1, outer = FALSE, line = 2, "Number of CV Folds", cex =0.75)
if(n == 50){
axis(side = 2)
mtext(outer = FALSE, side = 2, line = 2, "Absolute bias", cex = 0.75)
mtext(outer = FALSE, side = 2, line = 4, "Random Forest", cex = 1)
}else{
axis(side = 2, labels = FALSE)
}
}
#----------------------------------
# Variance plots
#----------------------------------
layout(matrix(1:8, nrow = 2, ncol = 4, byrow = TRUE))
par(mar = c(1.6, 0.6, 0.6, 0.6), mgp = c(2.1, 0.5, 0),
oma = c(2.1, 5.1, 2.1, 2.1))
for(n in c(50, 100, 250, 500)){
tmp <- glm_rslt[glm_rslt$n == n, ]
tmp5 <- tmp[tmp$K == 5,]
tmp10 <- tmp[tmp$K == 10,]
tmp20 <- tmp[tmp$K == 20,]
tmp40 <- tmp[tmp$K == 40,]
est <- c("var_dcvtmle1","var_donestep1","var_emp1","var_bootstrap")
grbg <- t(as.matrix(rbind(tmp5[,est],tmp10[,est],tmp20[,est],tmp40[,est])))
colnames(grbg) <- c(5,10,20,40)
if(n == 50){leg.text <- c("CVTMLE","CVOS","Empirical","Bootstrap")}else{leg.text <- FALSE}
barplot(grbg, legend.text = leg.text, args.legend = list(x = "topright"),
beside=TRUE, log = "y", yaxt = "n", ylim = c(0.0001, 50))
if(n == 50){
axis(side = 2)
mtext(outer = FALSE, side = 2, line = 2, "Variance", cex = 0.75)
mtext(outer = FALSE, side = 2, line = 4, "Logistic Regression", cex = 1)
}else{
axis(side = 2, labels = FALSE)
}
mtext(outer = FALSE, side = 3, line = 0.5, paste0("n = ", n))
}
for(n in c(50, 100, 250, 500)){
tmp <- randomforest_rslt[randomforest_rslt$n == n, ]
tmp5 <- tmp[tmp$K == 5,]
tmp10 <- tmp[tmp$K == 10,]
tmp20 <- tmp[tmp$K == 20,]
tmp40 <- tmp[tmp$K == 40,]
est <- c("var_dcvtmle1","var_donestep1","var_emp1","var_bootstrap")
grbg <- t(as.matrix(rbind(tmp5[,est],tmp10[,est],tmp20[,est],tmp40[,est])))
colnames(grbg) <- c(5,10,20,40)
barplot(grbg,
beside=TRUE, log = "y", yaxt = "n", ylim = c(0.0001, 50))
mtext(side = 1, outer = FALSE, line = 2, "Number of CV Folds", cex =0.75)
if(n == 50){
axis(side = 2)
mtext(outer = FALSE, side = 2, line = 2, "Variance", cex = 0.75)
mtext(outer = FALSE, side = 2, line = 4, "Random Forest", cex = 1)
}else{
axis(side = 2, labels = FALSE)
}
}
#----------------------------------
# MSE plots
#----------------------------------
layout(matrix(1:8, nrow = 2, ncol = 4, byrow = TRUE))
par(mar = c(1.6, 0.6, 0.6, 0.6), mgp = c(2.1, 0.5, 0),
oma = c(2.1, 5.1, 2.1, 2.1))
for(n in c(50, 100, 250, 500)){
tmp <- glm_rslt[glm_rslt$n == n, ]
tmp5 <- tmp[tmp$K == 5,]
tmp10 <- tmp[tmp$K == 10,]
tmp20 <- tmp[tmp$K == 20,]
tmp40 <- tmp[tmp$K == 40,]
est <- c("mse_dcvtmle1","mse_donestep1","mse_emp1","mse_bootstrap")
grbg <- t(as.matrix(rbind(tmp5[,est],tmp10[,est],tmp20[,est],tmp40[,est])))
colnames(grbg) <- c(5,10,20,40)
if(n == 50){leg.text <- c("CVTMLE","CVOS","Empirical","Bootstrap")}else{leg.text <- FALSE}
barplot(grbg, legend.text = leg.text, args.legend = list(x = "topright"),
beside=TRUE, log = "y", yaxt = "n", ylim = c(0.0001, 50))
if(n == 50){
axis(side = 2)
mtext(outer = FALSE, side = 2, line = 2, "Variance", cex = 0.75)
mtext(outer = FALSE, side = 2, line = 4, "Logistic Regression", cex = 1)
}else{
axis(side = 2, labels = FALSE)
}
mtext(outer = FALSE, side = 3, line = 0.5, paste0("n = ", n))
}
for(n in c(50, 100, 250, 500)){
tmp <- randomforest_rslt[randomforest_rslt$n == n, ]
tmp5 <- tmp[tmp$K == 5,]
tmp10 <- tmp[tmp$K == 10,]
tmp20 <- tmp[tmp$K == 20,]
tmp40 <- tmp[tmp$K == 40,]
est <- c("mse_dcvtmle1","mse_donestep1","mse_emp1","mse_bootstrap")
grbg <- t(as.matrix(rbind(tmp5[,est],tmp10[,est],tmp20[,est],tmp40[,est])))
colnames(grbg) <- c(5,10,20,40)
barplot(grbg,
beside=TRUE, log = "y", yaxt = "n", ylim = c(0.0001, 50))
mtext(side = 1, outer = FALSE, line = 2, "Number of CV Folds", cex =0.75)
if(n == 50){
axis(side = 2)
mtext(outer = FALSE, side = 2, line = 2, "Variance", cex = 0.75)
mtext(outer = FALSE, side = 2, line = 4, "Random Forest", cex = 1)
}else{
axis(side = 2, labels = FALSE)
}
}
make_side_by_side_bar_plots <- function(glm_rslt, randomforest_rslt,
est, est_labels,
yaxis_label,
xaxis_label = "Number CV Folds",
absolute_val = TRUE, ... ){
layout(matrix(1:8, nrow = 2, ncol = 4, byrow = TRUE))
par(mar = c(1.6, 0.6, 0.6, 0.6), mgp = c(2.1, 0.5, 0),
oma = c(2.1, 5.1, 2.1, 2.1))
for(n in c(50, 100, 250, 500)){
tmp <- glm_rslt[glm_rslt$n == n, ]
tmp5 <- tmp[tmp$K == 5,]
tmp10 <- tmp[tmp$K == 10,]
tmp20 <- tmp[tmp$K == 20,]
tmp40 <- tmp[tmp$K == 40,]
# est <- c("mse_dcvtmle1","mse_donestep1","mse_emp1","mse_bootstrap")
grbg <- t(as.matrix(rbind(tmp5[,est],tmp10[,est],tmp20[,est],tmp40[,est])))
if(absolute_val){
grbg <- abs(grbg)
}
colnames(grbg) <- c(5,10,20,40)
if(n == 50){leg.text <- est_labels}else{leg.text <- FALSE}
barplot(grbg, legend.text = leg.text, args.legend = list(x = "topright"),
beside=TRUE, log = "y", yaxt = "n", ... )
if(n == 50){
axis(side = 2)
mtext(outer = FALSE, side = 2, line = 2, yaxis_label, cex = 0.75)
mtext(outer = FALSE, side = 2, line = 4, "Logistic Regression", cex = 1)
}else{
axis(side = 2, labels = FALSE)
}
mtext(outer = FALSE, side = 3, line = 0.5, paste0("n = ", n))
}
for(n in c(50, 100, 250, 500)){
tmp <- randomforest_rslt[randomforest_rslt$n == n, ]
tmp5 <- tmp[tmp$K == 5,]
tmp10 <- tmp[tmp$K == 10,]
tmp20 <- tmp[tmp$K == 20,]
tmp40 <- tmp[tmp$K == 40,]
# est <- c("mse_dcvtmle1","mse_donestep1","mse_emp1","mse_bootstrap")
grbg <- t(as.matrix(rbind(tmp5[,est],tmp10[,est],tmp20[,est],tmp40[,est])))
if(absolute_val){
grbg <- abs(grbg)
}
colnames(grbg) <- c(5,10,20,40)
barplot(grbg, beside=TRUE, log = "y", yaxt = "n", ...)
mtext(side = 1, outer = FALSE, line = 2, xaxis_label, cex =0.75)
if(n == 50){
axis(side = 2)
mtext(outer = FALSE, side = 2, line = 2, yaxis_label, cex = 0.75)
mtext(outer = FALSE, side = 2, line = 4, "Random Forest", cex = 1)
}else{
axis(side = 2, labels = FALSE)
}
}
}
# bias
make_side_by_side_bar_plots(glm_rslt, randomforest_rslt,
est = c("bias_dcvtmle1","bias_donestep1",
"bias_emp1","bias_bootstrap"),
est_label = c("CVTMLE", "CVOS","Empirical","Bootstrap"),
ylim = c(0.0001,100), yaxis_label = "Absolute Bias")
# variance
make_side_by_side_bar_plots(glm_rslt, randomforest_rslt,
est = c("var_dcvtmle1","var_donestep1",
"var_emp1","var_bootstrap"),
est_label = c("CVTMLE", "CVOS","Empirical","Bootstrap"),
ylim = c(0.0001,50), yaxis_label = "Variance")
# mse
make_side_by_side_bar_plots(glm_rslt, randomforest_rslt,
est = c("mse_dcvtmle1","mse_donestep1",
"mse_emp1","mse_bootstrap"),
est_label = c("CVTMLE", "CVOS","Empirical","Bootstrap"),
ylim = c(0.0001,100), yaxis_label = "Mean squared-error")
# bias by CV Repeats for CVTMLE
make_side_by_side_bar_plots(glm_rslt, randomforest_rslt,
est = c("bias_dcvtmle1","bias_dcvtmle5",
"bias_dcvtmle10","bias_dcvtmle20"),
est_label = paste0("CVTMLE ", c(1,5,10,20)," repeats"),
ylim = c(0.0001,100), yaxis_label = "Absolute bias")
# variance by CV Repeats for CVTMLE
make_side_by_side_bar_plots(glm_rslt, randomforest_rslt,
est = c("var_dcvtmle1","var_dcvtmle5",
"var_dcvtmle10","var_dcvtmle20"),
est_label = paste0("CVTMLE ", c(1,5,10,20)," repeats"),
ylim = c(0.0001,0.01), yaxis_label = "Variance")
# mse by CV Repeats for CVTMLE
make_side_by_side_bar_plots(glm_rslt, randomforest_rslt,
est = c("mse_dcvtmle1","mse_dcvtmle5",
"mse_dcvtmle10","mse_dcvtmle20"),
est_label = paste0("CVTMLE ", c(1,5,10,20)," repeats"),
ylim = c(0.0001,0.01), yaxis_label = "Variance")
# compare mse of cvtmle with 40 folds to mse of empirical with 5 folds
make_mse_compare_one_repeat <- function(rslt, B, legend = FALSE){
cvt_n_glm <- rslt[,paste0("mse_dcvtmle",B)][rslt$K == 40][2:4]
cvo_n_glm <- rslt[,paste0("mse_donestep",B)][rslt$K == 40][2:4]
emp_n_glm <- rslt[,paste0("mse_emp",B)][rslt$K == 5][2:4]
plot(y = cvt_n_glm/emp_n_glm, x = 1:3, xaxt = "n", yaxt = "n", bty = "n",
xlim = c(1,3), ylim = c(0,2), type = "b",
xlab = "Sample size", ylab = "MSE / Empirical with K = 5")
axis(side = 1, at = 1:3, labels = c(100, 250, 500))
axis(side = 2)
abline(h = 1, lty = 3)
points(y = cvo_n_glm/emp_n_glm, x = 1:3, pch = 2, type = "b", lty = 2)
if(legend){
legend(x = "topleft", c("CVTMLE, K = 40", "CVOS, K = 40"), pch = 1:2,
bty = "n")
}
}
layout(matrix(1:8, nrow = 2, ncol = 4, byrow = TRUE))
for(b in c(1, 5, 10, 20)){
make_mse_compare_one_repeat(rslt = glm_rslt, b, legend = ifelse(b == 1, TRUE, FALSE))
mtext(side = 3, text = paste0("MC Repeats = ", b))
}
for(b in c(1, 5, 10, 20)){
make_mse_compare_one_repeat(rslt = randomforest_rslt, b, legend = ifelse(b == 1, TRUE, FALSE))
}
cvt_n_glm <- glm_rslt$mse_dcvtmle1[glm_rslt$K == 40]
cvt_n_glm <- glm_rslt$mse_dcvtmle1[glm_rslt$K == 40]
ratio_cvtmle_to_mse <-
#---------------------------------
# Bias plots
#---------------------------------
# top row = glm bias ~ K for each n
# bottom row = random forest bias ~ K for each n
layout(matrix(1:8, nrow = 2, ncol = 4, byrow = TRUE))
par(mar = c(1.6, 0.6, 0.6, 0.6), mgp = c(2.1, 0.5, 0),
oma = c(2.1, 5.1, 2.1, 2.1))
for(n in c(50, 100, 250, 500)){
tmp <- glm_rslt[glm_rslt$n == n, ]
tmp5 <- tmp[tmp$K == 5,]
tmp10 <- tmp[tmp$K == 10,]
tmp20 <- tmp[tmp$K == 20,]
tmp40 <- tmp[tmp$K == 40,]
est <- c("bias_dcvtmle1","bias_donestep1","bias_emp1")
grbg <- t(abs(as.matrix(rbind(tmp5[,est],tmp10[,est],tmp20[,est],tmp40[,est]))))
colnames(grbg) <- c(5,10,20,40)
barplot(grbg,
beside=TRUE, log = "y", yaxt = "n", ylim = c(0.0001, 200))
if(n == 50){
axis(side = 2)
mtext(outer = FALSE, side = 2, line = 2, "Absolute bias", cex = 0.75)
mtext(outer = FALSE, side = 2, line = 4, "Logistic Regression", cex = 1)
}else{
axis(side = 2, labels = FALSE)
}
mtext(outer = FALSE, side = 3, line = 0.5, paste0("n = ", n))
}
for(n in c(50, 100, 250, 500)){
tmp <- randomforest_rslt[randomforest_rslt$n == n, ]
tmp5 <- tmp[tmp$K == 5,]
tmp10 <- tmp[tmp$K == 10,]
tmp20 <- tmp[tmp$K == 20,]
tmp40 <- tmp[tmp$K == 40,]
est <- c("bias_dcvtmle1","bias_donestep1","bias_emp1")
grbg <- t(abs(as.matrix(rbind(tmp5[,est],tmp10[,est],tmp20[,est],tmp40[,est]))))
colnames(grbg) <- c(5,10,20,40)
barplot(grbg,
beside=TRUE, log = "y", yaxt = "n", ylim = c(0.0001, 200))
mtext(side = 1, outer = FALSE, line = 2, "Number of CV Folds", cex =0.75)
if(n == 50){
axis(side = 2)
mtext(outer = FALSE, side = 2, line = 2, "Absolute bias", cex = 0.75)
mtext(outer = FALSE, side = 2, line = 4, "Random Forest", cex = 1)
}else{
axis(side = 2, labels = FALSE)
}
}
#--------------------------------
# MSE plots
#--------------------------------
make_mse_compare_plot <- function(rslt, est1, est2, ns = c(100, 250, 500, 750),
Ks = c(5, 10, 20, 40),...){
# make matrix of relative MSE
n_ct <- 0
K_ct <- 0
rel_mse <- matrix(NA, length(ns), length(Ks))
for(n in ns){
n_ct <- n_ct + 1
for(K in Ks){
K_ct <- K_ct + 1
rel_mse[n_ct, K_ct] <- rslt[rslt$n == n & rslt$K == K, paste0("mse_",est1)] /
rslt[rslt$n == n & rslt$K == K, paste0("mse_",est2)]
}
K_ct <- 0
}
row.names(rel_mse) <- ns
colnames(rel_mse) <- Ks
superheat::superheat(X = rel_mse, X.text = round(rel_mse, 2), scale = FALSE,
pretty.order.rows = FALSE,
pretty.order.cols = FALSE, heat.col.scheme = "red",
row.title = "Sample size", column.title = "CV folds",
title = paste0("MSE(",est1,")/MSE(",est2,")"),
# plot_done()
legend.breaks = seq(min(rel_mse), max(rel_mse), by = 0.1), ...)
}
# CV TMLE vs. empirical
for(rslt in c("glm_rslt","randomforest_rslt","glmnet_rslt","stepglm_rslt")){
pdf(paste0("~/cvtmleauc/",rslt,"_perf.pdf"))
# comparing to emp
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "dcvtmle", est2 = "emp")
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "cvtmle", est2 = "emp")
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "desteq", est2 = "emp")
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "esteq", est2 = "emp")
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "donestep", est2 = "emp")
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "onestep", est2 = "emp")
# comparing to eachother
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "onestep", est2 = "cvtmle")
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "donestep", est2 = "dcvtmle")
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "onestep", est2 = "esteq")
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "donestep", est2 = "desteq")
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "esteq", est2 = "cvtmle")
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "desteq", est2 = "dcvtmle")
# comparing cv to dcv
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "onestep", est2 = "donestep")
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "cvtmle", est2 = "dcvtmle")
make_mse_compare_plot(eval(parse(text = rslt)), est1 = "esteq", est2 = "desteq")
dev.off()
}
#--------------------------------
# CV TMLE vs. Empirical
#--------------------------------
pdf("mse_results.pdf")
superheat(X = rel_mse_cvtmle, X.text = round(rel_mse_cvtmle, 2), scale = FALSE,
pretty.order.rows = FALSE,
pretty.order.cols = FALSE, heat.col.scheme = "red",
row.title = "Sample size", column.title = "CV folds",
legend.breaks = c(0.7, 0.8, 0.9, 1),
title = "MSE CVTMLE / MSE Empirical")
#--------------------------------
# One step vs. Empirical
#--------------------------------
superheat(X = rel_mse_onestep, X.text = round(rel_mse_onestep, 2), scale = FALSE,
pretty.order.rows = FALSE,
pretty.order.cols = FALSE, heat.col.scheme = "red",
row.title = "Sample size", column.title = "CV folds",
legend.breaks = c(0.7, 0.8, 0.9, 1),
title = "MSE CV One step / MSE Empirical")
#--------------------------------
# CVTMLE vs. Onestep
#--------------------------------
superheat(X = rel_mse_tmlevonestep, X.text = round(rel_mse_tmlevonestep, 2),
scale = FALSE,
pretty.order.rows = FALSE,
pretty.order.cols = FALSE, heat.col.scheme = "red",
row.title = "Sample size", column.title = "CV folds",
legend.breaks = c(0.7, 0.8, 0.9, 1),
title = "MSE CV One step / MSE CVTMLE")
dev.off()
#--------------------------------
# Coverage plots
#--------------------------------
# make matrix of relative MSE
n_ct <- 0
K_ct <- 0
cov_cvtmle <- matrix(NA, 4, 4)
cov_onestep <- matrix(NA, 4, 4)
cov_tmlevonestep <- matrix(NA, 4, 4)
for(n in c(100, 250, 500, 750)){
n_ct <- n_ct + 1
for(K in c(5, 10, 20, 30)){
K_ct <- K_ct + 1
cov_cvtmle[n_ct, K_ct] <- parm$cov_cvtmle[parm$n == n & parm$K == K]
cov_onestep[n_ct, K_ct] <- parm$cov_onestep[parm$n == n & parm$K == K]
cov_tmlevonestep[n_ct, K_ct] <- parm$cov_empirical[parm$n == n & parm$K == K]
}
K_ct <- 0
}
row.names(cov_cvtmle) <- row.names(cov_onestep) <- row.names(cov_tmlevonestep) <- c(100, 250, 500, 750)
colnames(cov_cvtmle) <- colnames(cov_onestep) <- colnames(cov_tmlevonestep) <- c(5, 10, 20, 30)
#--------------------------------
# CV TMLE vs. Empirical
#--------------------------------
pdf("coverage_results.pdf")
superheat(X = cov_cvtmle, X.text = round(cov_cvtmle, 2), scale = FALSE,
pretty.order.rows = FALSE,
pretty.order.cols = FALSE, heat.col.scheme = "red",
row.title = "Sample size", column.title = "CV folds",
legend.breaks = c(0.7, 0.8, 0.9, 1),
title = "Coverage of nominal 95% CI CVTMLE")
#--------------------------------
# One step vs. Empirical
#--------------------------------
superheat(X = cov_onestep, X.text = round(cov_onestep, 2), scale = FALSE,
pretty.order.rows = FALSE,
pretty.order.cols = FALSE, heat.col.scheme = "red",
row.title = "Sample size", column.title = "CV folds",
legend.breaks = c(0.7, 0.8, 0.9, 1),
title = "Coverage of nominal 95% CI One step")
#--------------------------------
# CVTMLE vs. Onestep
#--------------------------------
superheat(X = cov_tmlevonestep, X.text = round(cov_tmlevonestep, 2),
scale = FALSE,
pretty.order.rows = FALSE,
pretty.order.cols = FALSE, heat.col.scheme = "red",
row.title = "Sample size", column.title = "CV folds",
legend.breaks = c(0.7, 0.8, 0.9, 1),
title = "Coverage of nominal 95% CI Empirical")
dev.off()
}
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