Code_PAPER/Simulations/plotsthesis.R
In MartaBofillRoig/SurvBin: Two-sample statistics for binary and time-to-event outcomes
################################################################
# SIMULATIONS Statistics Binary and Survival outcomes
# Results
# Marta Bofill and Guadalupe Gómez
################################################################
rm(list = ls())
# Install and load packages
# install.packages("ggplot2")
# install.packages("gridExtra")
library("ggplot2")
library("gridExtra")
# Working directory
setwd("C:/Users/mbofi/Dropbox/C5/Scripts/GitKraken/survivalbinary/Code_PAPER/Extension_Simulation")
# setwd("C:/Users/Marta/Nextcloud/Gitkraken/SurvBin/Code_PAPER/Extension_Simulation")
################################################################
# Unified version results
# Under H1
################################################################
load("C:/Users/mbofi/Dropbox/C5/Scripts/GitKraken/survivalbinary/Code_PAPER/Extension_Simulation/results/RESULTS_PAPER_H1_case1.RData")
data$cases = 1
data$tstar = 0
data_1 = data
dim(data_1)
summary(data_1)
# Comparison eta=1, eta=0
summary(data_1[data_1$eta==0,])
summary(data_1[data_1$eta==1,])
# Consider only small sample sizes for the simulation study
data_1 = data_1[data_1$eta==1,]
#
load("C:/Users/mbofi/Dropbox/C5/Scripts/GitKraken/survivalbinary/Code_PAPER/Extension_Simulation/results/RESULTS_PAPER_H1_case2.RData")
data$cases = 2
data$tstar = 0
data_2 = data
dim(data_2)
summary(data_2)
# Comparison eta=1, eta=0
summary(data_2[data_2$eta==0,])
summary(data_2[data_2$eta==1,])
# Comparison n=500 and n=1000
summary(data_2[data_2$n==500,])
summary(data_2[data_2$n==1000,])
summary(data_2[data_2$n==500,]$Test_Power_Bonf-data_2[data_2$n==500,]$Test_Power_Boots)
summary(data_2[data_2$n==1000,]$Test_Power_Bonf-data_2[data_2$n==1000,]$Test_Power_Boots)
# Consider only small sample sizes for the simulation study
data_2 = data_2[data_2$n==500,]
data_2 = data_2[data_2$eta==1,]
#
load("C:/Users/mbofi/Dropbox/C5/Scripts/GitKraken/survivalbinary/Code_PAPER/Extension_Simulation/results/RESULTS_PAPER_H1_case3.RData")
data$cases = 3
data$tstar = 0
data_3 = data
dim(data_3)
summary(data_3)
# Comparison eta=1, eta=0
summary(data_3[data_3$eta==0,])
summary(data_3[data_3$eta==1,])
# Comparison n=500 and n=1000
summary(data_3[data_3$n==500,])
summary(data_3[data_3$n==1000,])
summary(data_3[data_3$n==500,]$Test_Power_Bonf-data_3[data_3$n==500,]$Test_Power_Boots)
summary(data_3[data_3$n==1000,]$Test_Power_Bonf-data_3[data_3$n==1000,]$Test_Power_Boots)
# Consider only small sample sizes for the simulation study
data_3 = data_3[data_3$n==500,]
data_3 = data_3[data_3$eta==1,]
#
load("C:/Users/mbofi/Dropbox/C5/Scripts/GitKraken/survivalbinary/Code_PAPER/Extension_Simulation/results/RESULTS_PAPER_H1_nPH.RData")
data$cases = 4
data_4 = data
dim(data_4)
summary(data_4)
# Comparison eta=1, eta=0
summary(data_4[data_4$eta==0,])
summary(data_4[data_4$eta==1,])
# Comparison n=500 and n=1000
summary(data_4[data_4$n==500,])
summary(data_4[data_4$n==1000,])
summary(data_4[data_4$n==500,]$Test_Power_Bonf-data_4[data_4$n==500,]$Test_Power_Boots)
summary(data_4[data_4$n==1000,]$Test_Power_Bonf-data_4[data_4$n==1000,]$Test_Power_Boots)
# Consider only small sample sizes for the simulation study
data_4 = data_4[data_4$n==500,]
data_4 = data_4[data_4$eta==1,]
data_4 = data_4[data_4$a==1,]
################################################################
################################################################
#
# Unified dataset for the paper
#
################################################################
################################################################
data_H1 = rbind(data_1,data_2,data_3,data_4)
data_H1$cases = as.factor(data_H1$cases)
summary(data_H1)
# General summary powers
summary(data_H1[,17:22])
# Summary per cases
summary(data_H1[data_H1$cases==1,17:20])
summary(data_H1[data_H1$cases==2,17:20])
summary(data_H1[data_H1$cases==3,17:20])
summary(data_H1[data_H1$cases==4,17:20])
# taub
summary(data_H1[data_H1$cases==1 & data_H1$taub==0.5,17:20])
summary(data_H1[data_H1$cases==1 & data_H1$taub==1,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$taub==0.5,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$taub==1,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$taub==0.5,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$taub==1,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$taub==0.5,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$taub==1,17:20])
# Theta
summary(data_H1[data_H1$cases==1 & data_H1$theta==0.001,17:20])
summary(data_H1[data_H1$cases==1 & data_H1$theta==2,17:20])
summary(data_H1[data_H1$cases==1 & data_H1$theta==3,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$theta==0.001,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$theta==2,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$theta==3,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$theta==0.001,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$theta==2,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$theta==3,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$theta==0.001,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$theta==2,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$theta==3,17:20])
# Rho and Gamma
summary(data_H1[data_H1$cases==1 & data_H1$gamma==1 & data_H1$rho==1,17:20])
summary(data_H1[data_H1$cases==1 & data_H1$gamma==0 & data_H1$rho==1,17:20])
summary(data_H1[data_H1$cases==1 & data_H1$gamma==1 & data_H1$rho==0,17:20])
summary(data_H1[data_H1$cases==1 & data_H1$gamma==0 & data_H1$rho==0,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$gamma==1 & data_H1$rho==1,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$gamma==0 & data_H1$rho==1,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$gamma==1 & data_H1$rho==0,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$gamma==0 & data_H1$rho==0,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$gamma==1 & data_H1$rho==1,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$gamma==0 & data_H1$rho==1,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$gamma==1 & data_H1$rho==0,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$gamma==0 & data_H1$rho==0,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$gamma==1 & data_H1$rho==1,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$gamma==0 & data_H1$rho==1,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$gamma==1 & data_H1$rho==0,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$gamma==0 & data_H1$rho==0,17:20])
# Gamma (late-effects)
summary(data_H1[data_H1$cases==1 & data_H1$gamma==1,17:20])
summary(data_H1[data_H1$cases==1 & data_H1$gamma==0,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$gamma==1,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$gamma==0,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$gamma==1,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$gamma==0,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$gamma==1,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$gamma==0,17:20])
# Rho (censoring)
summary(data_H1[data_H1$cases==1 & data_H1$rho==1,17:20])
summary(data_H1[data_H1$cases==1 & data_H1$rho==0,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$rho==1,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$rho==0,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$rho==1,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$rho==0,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$rho==1,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$rho==0,17:20])
# p0 (more/less effect binary)
summary(data_H1[data_H1$cases==1 & data_H1$p0==0.1,17:20])
summary(data_H1[data_H1$cases==1 & data_H1$p0==0.3,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$p0==0.1,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$p0==0.3,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$p0==0.1,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$p0==0.3,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$p0==0.1,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$p0==0.3,17:20])
# a
summary(data_H1[data_H1$cases==1 & data_H1$a==0.5,17:20])
summary(data_H1[data_H1$cases==1 & data_H1$a==1,17:20])
summary(data_H1[data_H1$cases==1 & data_H1$a==2,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$a==0.5,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$a==1,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$a==2,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$a==0.5,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$a==1,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$a==2,17:20])
# summary(data_H1[data_H1$cases==4 & data_H1$a==0.5,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$a==1,17:20])
# summary(data_H1[data_H1$cases==4 & data_H1$a==2,17:20])
################################################################
# Plot per case
################################################################
data_H1 = subset(data_H1,data_H1$omegab==0.5)
colors <- c("0.25" = "#FD6467", "0.75" = "#018F00", "Multiple test" = "#0001CE", "Individual tests" = "#333C45")
# case1
power_data <- data.frame(Power=c(data_H1[data_H1$cases==1,]$Test_Power_pluginU,
# data_H1[data_H1$cases==1,]$Test_Power_pluginP,
data_H1[data_H1$cases==1,]$Test_Power_Boots,
data_H1[data_H1$cases==1,]$Test_Power_Bonf
,
data_H1[data_H1$cases==1,]$Test_Power_B,
data_H1[data_H1$cases==1,]$Test_Power_S
),
Test=c(rep("Plug-in",length(data_H1[data_H1$cases==1,]$Test_Power_pluginU)),
# rep("Pooled",length(data_H1[data_H1$cases==1,]$Test_Power_pluginP)),
rep("Bootstrap",length(data_H1[data_H1$cases==1,]$Test_Power_Boots)),
rep("Bonferroni",length(data_H1[data_H1$cases==1,]$Test_Power_Bonf))
,
rep("Binary test",length(data_H1[data_H1$cases==1,]$Test_Power_B)),
rep("Survival test",length(data_H1[data_H1$cases==1,]$Test_Power_S))
),
omegab=c(rep("Multiple test",length(data_H1[data_H1$cases==1,]$omegab)),
rep("Multiple test",length(data_H1[data_H1$cases==1,]$omegab)),
rep("Multiple test",length(data_H1[data_H1$cases==1,]$omegab)),
# data_H1[data_H1$cases==1,]$omegab,
# # data_H1[data_H1$cases==1,]$omegab,
# data_H1[data_H1$cases==1,]$omegab,
# rep(0.5,length(data_H1[data_H1$cases==1,]$omegab)),
rep("Individual tests",length(data_H1[data_H1$cases==1,]$omegab)),
rep("Individual tests",length(data_H1[data_H1$cases==1,]$omegab))
)
)
power_data$Test <- factor(power_data$Test,
levels = c('Bootstrap', 'Plug-in',
# 'Pooled',
'Bonferroni'
, 'Binary test', 'Survival test'
),
ordered = TRUE)
plot_case1 <- ggplot(power_data, aes(x=Test, y=Power, color=as.factor(omegab))) + geom_boxplot() + scale_color_manual(values = colors) +theme(legend.position = "bottom") + labs(color='Univariate or multiple tests')
windows()
plot_case1
########
################################################################
# Plot per case
################################################################
data_H1 = subset(data_H1,data_H1$omegab==0.5)
colors <- c("0.25" = "#FD6467", "0.75" = "#018F00", "Multiple test" = "#0001CE", "Individual tests" = "#333C45")
# case1
power_data <- data.frame(Power=c(data_H1[data_H1$cases==1,]$Test_Power_pluginU,
# data_H1[data_H1$cases==1,]$Test_Power_pluginP,
data_H1[data_H1$cases==1,]$Test_Power_Boots,
data_H1[data_H1$cases==1,]$Test_Power_Bonf
# ,
# data_H1[data_H1$cases==1,]$Test_Power_B,
# data_H1[data_H1$cases==1,]$Test_Power_S
),
Test=c(rep("Plug-in",length(data_H1[data_H1$cases==1,]$Test_Power_pluginU)),
# rep("Pooled",length(data_H1[data_H1$cases==1,]$Test_Power_pluginP)),
rep("Bootstrap",length(data_H1[data_H1$cases==1,]$Test_Power_Boots)),
rep("Bonferroni",length(data_H1[data_H1$cases==1,]$Test_Power_Bonf))
# ,
# rep("Binary test",length(data_H1[data_H1$cases==1,]$Test_Power_B)),
# rep("Survival test",length(data_H1[data_H1$cases==1,]$Test_Power_S))
),
omegab=c(rep("Multiple test",length(data_H1[data_H1$cases==1,]$omegab)),
rep("Multiple test",length(data_H1[data_H1$cases==1,]$omegab)),
rep("Multiple test",length(data_H1[data_H1$cases==1,]$omegab))
# ,
# data_H1[data_H1$cases==1,]$omegab,
# # data_H1[data_H1$cases==1,]$omegab,
# data_H1[data_H1$cases==1,]$omegab,
# rep(0.5,length(data_H1[data_H1$cases==1,]$omegab)),
# rep("Individual tests",length(data_H1[data_H1$cases==1,]$omegab)),
# rep("Individual tests",length(data_H1[data_H1$cases==1,]$omegab))
)
)
power_data$Test <- factor(power_data$Test,
levels = c('Bootstrap', 'Plug-in',
# 'Pooled',
'Bonferroni'
# , 'Binary test', 'Survival test'
),
ordered = TRUE)
plot_case1 <- ggplot(power_data, aes(x=Test, y=Power, color=as.factor(omegab))) + geom_boxplot() + scale_color_manual(values = colors) +theme(legend.position = "bottom") + labs(color='Univariate or multiple tests')
windows()
plot_case1
########
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################################################################
# SIMULATIONS Statistics Binary and Survival outcomes
# Results
# Marta Bofill and Guadalupe Gómez
################################################################
rm(list = ls())
# Install and load packages
# install.packages("ggplot2")
# install.packages("gridExtra")
library("ggplot2")
library("gridExtra")
# Working directory
setwd("C:/Users/mbofi/Dropbox/C5/Scripts/GitKraken/survivalbinary/Code_PAPER/Extension_Simulation")
# setwd("C:/Users/Marta/Nextcloud/Gitkraken/SurvBin/Code_PAPER/Extension_Simulation")
################################################################
# Unified version results
# Under H1
################################################################
load("C:/Users/mbofi/Dropbox/C5/Scripts/GitKraken/survivalbinary/Code_PAPER/Extension_Simulation/results/RESULTS_PAPER_H1_case1.RData")
data$cases = 1
data$tstar = 0
data_1 = data
dim(data_1)
summary(data_1)
# Comparison eta=1, eta=0
summary(data_1[data_1$eta==0,])
summary(data_1[data_1$eta==1,])
# Consider only small sample sizes for the simulation study
data_1 = data_1[data_1$eta==1,]
#
load("C:/Users/mbofi/Dropbox/C5/Scripts/GitKraken/survivalbinary/Code_PAPER/Extension_Simulation/results/RESULTS_PAPER_H1_case2.RData")
data$cases = 2
data$tstar = 0
data_2 = data
dim(data_2)
summary(data_2)
# Comparison eta=1, eta=0
summary(data_2[data_2$eta==0,])
summary(data_2[data_2$eta==1,])
# Comparison n=500 and n=1000
summary(data_2[data_2$n==500,])
summary(data_2[data_2$n==1000,])
summary(data_2[data_2$n==500,]$Test_Power_Bonf-data_2[data_2$n==500,]$Test_Power_Boots)
summary(data_2[data_2$n==1000,]$Test_Power_Bonf-data_2[data_2$n==1000,]$Test_Power_Boots)
# Consider only small sample sizes for the simulation study
data_2 = data_2[data_2$n==500,]
data_2 = data_2[data_2$eta==1,]
#
load("C:/Users/mbofi/Dropbox/C5/Scripts/GitKraken/survivalbinary/Code_PAPER/Extension_Simulation/results/RESULTS_PAPER_H1_case3.RData")
data$cases = 3
data$tstar = 0
data_3 = data
dim(data_3)
summary(data_3)
# Comparison eta=1, eta=0
summary(data_3[data_3$eta==0,])
summary(data_3[data_3$eta==1,])
# Comparison n=500 and n=1000
summary(data_3[data_3$n==500,])
summary(data_3[data_3$n==1000,])
summary(data_3[data_3$n==500,]$Test_Power_Bonf-data_3[data_3$n==500,]$Test_Power_Boots)
summary(data_3[data_3$n==1000,]$Test_Power_Bonf-data_3[data_3$n==1000,]$Test_Power_Boots)
# Consider only small sample sizes for the simulation study
data_3 = data_3[data_3$n==500,]
data_3 = data_3[data_3$eta==1,]
#
load("C:/Users/mbofi/Dropbox/C5/Scripts/GitKraken/survivalbinary/Code_PAPER/Extension_Simulation/results/RESULTS_PAPER_H1_nPH.RData")
data$cases = 4
data_4 = data
dim(data_4)
summary(data_4)
# Comparison eta=1, eta=0
summary(data_4[data_4$eta==0,])
summary(data_4[data_4$eta==1,])
# Comparison n=500 and n=1000
summary(data_4[data_4$n==500,])
summary(data_4[data_4$n==1000,])
summary(data_4[data_4$n==500,]$Test_Power_Bonf-data_4[data_4$n==500,]$Test_Power_Boots)
summary(data_4[data_4$n==1000,]$Test_Power_Bonf-data_4[data_4$n==1000,]$Test_Power_Boots)
# Consider only small sample sizes for the simulation study
data_4 = data_4[data_4$n==500,]
data_4 = data_4[data_4$eta==1,]
data_4 = data_4[data_4$a==1,]
################################################################
################################################################
#
# Unified dataset for the paper
#
################################################################
################################################################
data_H1 = rbind(data_1,data_2,data_3,data_4)
data_H1$cases = as.factor(data_H1$cases)
summary(data_H1)
# General summary powers
summary(data_H1[,17:22])
# Summary per cases
summary(data_H1[data_H1$cases==1,17:20])
summary(data_H1[data_H1$cases==2,17:20])
summary(data_H1[data_H1$cases==3,17:20])
summary(data_H1[data_H1$cases==4,17:20])
# taub
summary(data_H1[data_H1$cases==1 & data_H1$taub==0.5,17:20])
summary(data_H1[data_H1$cases==1 & data_H1$taub==1,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$taub==0.5,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$taub==1,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$taub==0.5,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$taub==1,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$taub==0.5,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$taub==1,17:20])
# Theta
summary(data_H1[data_H1$cases==1 & data_H1$theta==0.001,17:20])
summary(data_H1[data_H1$cases==1 & data_H1$theta==2,17:20])
summary(data_H1[data_H1$cases==1 & data_H1$theta==3,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$theta==0.001,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$theta==2,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$theta==3,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$theta==0.001,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$theta==2,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$theta==3,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$theta==0.001,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$theta==2,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$theta==3,17:20])
# Rho and Gamma
summary(data_H1[data_H1$cases==1 & data_H1$gamma==1 & data_H1$rho==1,17:20])
summary(data_H1[data_H1$cases==1 & data_H1$gamma==0 & data_H1$rho==1,17:20])
summary(data_H1[data_H1$cases==1 & data_H1$gamma==1 & data_H1$rho==0,17:20])
summary(data_H1[data_H1$cases==1 & data_H1$gamma==0 & data_H1$rho==0,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$gamma==1 & data_H1$rho==1,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$gamma==0 & data_H1$rho==1,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$gamma==1 & data_H1$rho==0,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$gamma==0 & data_H1$rho==0,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$gamma==1 & data_H1$rho==1,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$gamma==0 & data_H1$rho==1,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$gamma==1 & data_H1$rho==0,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$gamma==0 & data_H1$rho==0,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$gamma==1 & data_H1$rho==1,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$gamma==0 & data_H1$rho==1,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$gamma==1 & data_H1$rho==0,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$gamma==0 & data_H1$rho==0,17:20])
# Gamma (late-effects)
summary(data_H1[data_H1$cases==1 & data_H1$gamma==1,17:20])
summary(data_H1[data_H1$cases==1 & data_H1$gamma==0,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$gamma==1,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$gamma==0,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$gamma==1,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$gamma==0,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$gamma==1,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$gamma==0,17:20])
# Rho (censoring)
summary(data_H1[data_H1$cases==1 & data_H1$rho==1,17:20])
summary(data_H1[data_H1$cases==1 & data_H1$rho==0,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$rho==1,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$rho==0,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$rho==1,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$rho==0,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$rho==1,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$rho==0,17:20])
# p0 (more/less effect binary)
summary(data_H1[data_H1$cases==1 & data_H1$p0==0.1,17:20])
summary(data_H1[data_H1$cases==1 & data_H1$p0==0.3,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$p0==0.1,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$p0==0.3,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$p0==0.1,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$p0==0.3,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$p0==0.1,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$p0==0.3,17:20])
# a
summary(data_H1[data_H1$cases==1 & data_H1$a==0.5,17:20])
summary(data_H1[data_H1$cases==1 & data_H1$a==1,17:20])
summary(data_H1[data_H1$cases==1 & data_H1$a==2,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$a==0.5,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$a==1,17:20])
summary(data_H1[data_H1$cases==2 & data_H1$a==2,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$a==0.5,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$a==1,17:20])
summary(data_H1[data_H1$cases==3 & data_H1$a==2,17:20])
# summary(data_H1[data_H1$cases==4 & data_H1$a==0.5,17:20])
summary(data_H1[data_H1$cases==4 & data_H1$a==1,17:20])
# summary(data_H1[data_H1$cases==4 & data_H1$a==2,17:20])
################################################################
# Plot per case
################################################################
data_H1 = subset(data_H1,data_H1$omegab==0.5)
colors <- c("0.25" = "#FD6467", "0.75" = "#018F00", "Multiple test" = "#0001CE", "Individual tests" = "#333C45")
# case1
power_data <- data.frame(Power=c(data_H1[data_H1$cases==1,]$Test_Power_pluginU,
# data_H1[data_H1$cases==1,]$Test_Power_pluginP,
data_H1[data_H1$cases==1,]$Test_Power_Boots,
data_H1[data_H1$cases==1,]$Test_Power_Bonf
,
data_H1[data_H1$cases==1,]$Test_Power_B,
data_H1[data_H1$cases==1,]$Test_Power_S
),
Test=c(rep("Plug-in",length(data_H1[data_H1$cases==1,]$Test_Power_pluginU)),
# rep("Pooled",length(data_H1[data_H1$cases==1,]$Test_Power_pluginP)),
rep("Bootstrap",length(data_H1[data_H1$cases==1,]$Test_Power_Boots)),
rep("Bonferroni",length(data_H1[data_H1$cases==1,]$Test_Power_Bonf))
,
rep("Binary test",length(data_H1[data_H1$cases==1,]$Test_Power_B)),
rep("Survival test",length(data_H1[data_H1$cases==1,]$Test_Power_S))
),
omegab=c(rep("Multiple test",length(data_H1[data_H1$cases==1,]$omegab)),
rep("Multiple test",length(data_H1[data_H1$cases==1,]$omegab)),
rep("Multiple test",length(data_H1[data_H1$cases==1,]$omegab)),
# data_H1[data_H1$cases==1,]$omegab,
# # data_H1[data_H1$cases==1,]$omegab,
# data_H1[data_H1$cases==1,]$omegab,
# rep(0.5,length(data_H1[data_H1$cases==1,]$omegab)),
rep("Individual tests",length(data_H1[data_H1$cases==1,]$omegab)),
rep("Individual tests",length(data_H1[data_H1$cases==1,]$omegab))
)
)
power_data$Test <- factor(power_data$Test,
levels = c('Bootstrap', 'Plug-in',
# 'Pooled',
'Bonferroni'
, 'Binary test', 'Survival test'
),
ordered = TRUE)
plot_case1 <- ggplot(power_data, aes(x=Test, y=Power, color=as.factor(omegab))) + geom_boxplot() + scale_color_manual(values = colors) +theme(legend.position = "bottom") + labs(color='Univariate or multiple tests')
windows()
plot_case1
########
################################################################
# Plot per case
################################################################
data_H1 = subset(data_H1,data_H1$omegab==0.5)
colors <- c("0.25" = "#FD6467", "0.75" = "#018F00", "Multiple test" = "#0001CE", "Individual tests" = "#333C45")
# case1
power_data <- data.frame(Power=c(data_H1[data_H1$cases==1,]$Test_Power_pluginU,
# data_H1[data_H1$cases==1,]$Test_Power_pluginP,
data_H1[data_H1$cases==1,]$Test_Power_Boots,
data_H1[data_H1$cases==1,]$Test_Power_Bonf
# ,
# data_H1[data_H1$cases==1,]$Test_Power_B,
# data_H1[data_H1$cases==1,]$Test_Power_S
),
Test=c(rep("Plug-in",length(data_H1[data_H1$cases==1,]$Test_Power_pluginU)),
# rep("Pooled",length(data_H1[data_H1$cases==1,]$Test_Power_pluginP)),
rep("Bootstrap",length(data_H1[data_H1$cases==1,]$Test_Power_Boots)),
rep("Bonferroni",length(data_H1[data_H1$cases==1,]$Test_Power_Bonf))
# ,
# rep("Binary test",length(data_H1[data_H1$cases==1,]$Test_Power_B)),
# rep("Survival test",length(data_H1[data_H1$cases==1,]$Test_Power_S))
),
omegab=c(rep("Multiple test",length(data_H1[data_H1$cases==1,]$omegab)),
rep("Multiple test",length(data_H1[data_H1$cases==1,]$omegab)),
rep("Multiple test",length(data_H1[data_H1$cases==1,]$omegab))
# ,
# data_H1[data_H1$cases==1,]$omegab,
# # data_H1[data_H1$cases==1,]$omegab,
# data_H1[data_H1$cases==1,]$omegab,
# rep(0.5,length(data_H1[data_H1$cases==1,]$omegab)),
# rep("Individual tests",length(data_H1[data_H1$cases==1,]$omegab)),
# rep("Individual tests",length(data_H1[data_H1$cases==1,]$omegab))
)
)
power_data$Test <- factor(power_data$Test,
levels = c('Bootstrap', 'Plug-in',
# 'Pooled',
'Bonferroni'
# , 'Binary test', 'Survival test'
),
ordered = TRUE)
plot_case1 <- ggplot(power_data, aes(x=Test, y=Power, color=as.factor(omegab))) + geom_boxplot() + scale_color_manual(values = colors) +theme(legend.position = "bottom") + labs(color='Univariate or multiple tests')
windows()
plot_case1
########
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