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anova_parameters.R
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library(FactoMineR)
setwd("~/Documents/TraumaMatrix/CausalInference/Simulations/miss-vae/")
data.dir <- "./results/"
fig.dir <- "./figures/"
model <- "dlvm"
df <- read.csv(paste0(data.dir,"exp_24.1_10_choux.csv_temp"), row.names = 1)
df <- df[which(df$citcio == "False"),]
df_wy <- read.csv(paste0(data.dir,"exp_24.1_10_wy.csv_temp"), row.names = 1)
df_wy <- df_wy[which(df_wy$citcio == "False" & df_wy$prop_miss != 0),]
df <- rbind(df, df_wy)
summary(df)
df$sig_prior <- as.factor(df$sig_prior)
df$p <- as.factor(df$p)
df$n_epochs <- as.factor(df$n_epochs)
df$prop_miss <- as.factor(df$prop_miss)
df$add_wy <- as.factor(df$add_wy)
summary(df)
if (model == "dlvm"){
df_dlvm <-df[which(df$model=="dlvm"),]
df_dlvm$bias_ols = abs(1-df_dlvm$tau_ols)
df_dlvm$bias_dr = abs(1-df_dlvm$tau_dr)
res_dr_init <- AovSum(bias_dr ~ n + p + prop_miss + n_epochs + sig_prior + add_wy +
p:prop_miss + p:sig_prior + p:add_wy + p:n_epochs +
n:p + n:prop_miss + n:n_epochs + n:sig_prior + n:add_wy +
sig_prior:n_epochs + sig_prior:add_wy,
data = df_dlvm)
res_dr_init$Ftest
res_dr_init$Ttest
res_dr_reduced <- AovSum(tau_dr ~ n + p + n_epochs + sig_prior + add_wy +
p:n_epochs + p:sig_prior + p:add_wy +
n:p + n:n_epochs + n:sig_prior + n:add_wy +
sig_prior:n_epochs,
data = df_dlvm)
res_dr_reduced$Ftest
res_dr_reduced$Ttest
res_dr_reduced <- AovSum(tau_dr ~ n + p + n_epochs + sig_prior +
p:n_epochs + p:sig_prior +
n:p + n:n_epochs + n:sig_prior +
sig_prior:n_epochs,
data = df_dlvm)
res_dr_reduced$Ftest
res_dr_reduced$Ttest
res_dr_reduced <- AovSum(tau_dr ~ n + p + n_epochs +
p:n_epochs +
n:p + n:n_epochs,
data = df_dlvm)
res_dr_reduced$Ftest
res_dr_reduced$Ttest
res_dr_final <- AovSum(tau_dr ~ n + p + n_epochs +
n:p + n:n_epochs, data = df_dlvm)
res_dr_final$Ftest
res_dr_final$Ttest
###### OLS
res_ols_init <- AovSum(bias_ols ~ n + p + prop_miss + n_epochs + sig_prior + add_wy +
p:prop_miss + p:sig_prior + p:add_wy + p:n_epochs +
n:p + n:prop_miss + n:n_epochs + n:sig_prior + n:add_wy +
sig_prior:n_epochs,
data = df_dlvm)
res_ols_init$Ftest
res_ols_init$Ttest
res_ols_reduced <- AovSum(tau_ols ~ n + p + n_epochs + sig_prior + add_wy +
p:sig_prior + p:n_epochs + p:add_wy +
n:p + n:n_epochs + n:sig_prior + n:add_wy +
sig_prior:n_epochs,
data = df_dlvm)
res_ols_reduced$Ftest
res_ols_reduced$Ttest
res_ols_reduced <- AovSum(tau_ols ~ n + p + n_epochs + sig_prior +
p:sig_prior + p:n_epochs +
n:p + n:n_epochs + n:sig_prior +
sig_prior:n_epochs,
data = df_dlvm)
res_ols_reduced$Ftest
res_ols_reduced$Ttest
res_ols_final <- AovSum(tau_ols ~ n + p + n_epochs + sig_prior +
n:p + n:n_epochs +
sig_prior:n_epochs, data = df_dlvm)
res_ols_final$Ftest
res_ols_final$Ttest
} else {
df_lrmf <-df[which(df$model=="lrmf"),]
df_lrmf$bias_ols = abs(1-df_lrmf$tau_ols)
df_lrmf$bias_dr = abs(1-df_lrmf$tau_dr)
res_dr_init <- AovSum(bias_dr ~ n + p + prop_miss + n_epochs + sig_prior + add_wy +
p:prop_miss + p:sig_prior + p:add_wy + p:n_epochs +
n:p + n:prop_miss + n:n_epochs + n:sig_prior + n:add_wy +
sig_prior:n_epochs,
data = df_lrmf)
res_dr_init$Ftest
res_dr_init$Ttest
res_dr_reduced <- AovSum(tau_dr ~ n + p + n_epochs + sig_prior +
n:p + n:n_epochs + n:sig_prior + p:n_epochs +
sig_prior:n_epochs,
data = df_lrmf)
res_dr_reduced$Ftest
res_dr_reduced$Ttest
res_dr_final <- AovSum(tau_dr ~ p + n_epochs +
p:n_epochs, data = df_lrmf)
res_dr_final$Ftest
res_dr_final$Ttest
###### OLS
res_ols_init <- AovSum(bias_ols ~ n + p + prop_miss + n_epochs + sig_prior + add_wy +
p:prop_miss + p:sig_prior + p:add_wy +
n:p + n:prop_miss + n:n_epochs + n:sig_prior + n:add_wy +
sig_prior:n_epochs,
data = df_lrmf)
res_ols_init$Ftest
res_ols_init$Ttest
res_ols_reduced <- AovSum(tau_ols ~ n + p + n_epochs + sig_prior + add_wy +
p:sig_prior + p:add_wy +
n:p + n:n_epochs + n:sig_prior + n:add_wy +
sig_prior:n_epochs,
data = df_lrmf)
res_ols_reduced$Ftest
res_ols_reduced$Ttest
res_ols_reduced <- AovSum(tau_ols ~ n + p + n_epochs + sig_prior +
p:sig_prior +
n:p + n:n_epochs + n:sig_prior +
sig_prior:n_epochs,
data = df_lrmf)
res_ols_reduced$Ftest
res_ols_reduced$Ttest
res_ols_final <- AovSum(tau_ols ~ n + p + n_epochs + sig_prior +
n:p + n:n_epochs +
sig_prior:n_epochs, data = df_lrmf)
res_ols_final$Ftest
res_ols_final$Ttest
}
##### PLOT
results_ols <- data.frame(factor = rownames(res_ols_final$Ttest),
bias_ols = res_ols_final$Ttest[,1],
row.names = NULL)
results_dr <- data.frame(factor = rownames(res_dr_final$Ttest),
bias_dr = res_dr_final$Ttest[,1],
row.names = NULL)
ols_dr_diff <- setdiff(results_dr$factor, results_ols$factor) # factors in dr that are not in ols
dr_ols_diff <- setdiff(results_ols$factor, results_dr$factor) # factors in dr that are not in ols
results_ols <- rbind(results_ols[-1,], cbind(factor = ols_dr_diff, bias_ols = rep(0, length(ols_dr_diff))))
results_dr <- rbind(results_dr[-1,], cbind(factor = dr_ols_diff, bias_dr = rep(0, length(dr_ols_diff))))
results_ols <- results_ols[order(results_ols$factor),]
results_dr <- results_dr[order(results_dr$factor),]
results <- cbind(results_ols, bias_dr = results_dr[,2])
results <- data.frame(results[,2:3], row.names = results[,1])
results[,1] <- as.double(as.character(results[,1]))
results[,2] <- as.double(as.character(results[,2]))
# results <- cbind(results_ols, bias_dr = results_dr[,2])
# results <- data.frame(results, row.names = results[,1])
#
# results <- tidyr::gather(results, key = "estimate", value = "bias", bias_dr, bias_ols)
#
# results$bias <- as.double(results$bias)
# results$index_factor <- rep(1:length(unique(as.character(results$factor))),2)
# results$index_estimate <- rep(1:2, each=length(unique(as.character(results$factor))))
#
# image(x = results$index_estimate,
# y = results$index_factor,
# z = results$bias)
# library(RColorBrewer)
# #Get desired core colours from brewer
# cols0 <- rev(brewer.pal(n=10, name="PiYG"))
#
# #Derive desired break/legend colours from gradient of selected brewer palette
# cols1 <- colorRampPalette(cols0, space="rgb")(10)
#
# image(1:ncol(results),
# 1:nrow(results),
# t(results), col = cols1)
# axis(1, c("bias_ols","bias_dr"))
# #axis(2, at = seq(100, 600, by = 100))
tt <- results
colnames(tt) <- c("ols", "dr")
tt$row <- rownames(tt)
tt_melt <- reshape2::melt(tt)
ggplot(data=tt_melt,
aes(x=variable, y=row, fill=value)) +
geom_tile() +
scale_fill_gradient(low = "green", high = "red", name="effect on bias") +
theme(axis.text.x = element_text(size=18,angle=60,vjust = 0.5),
axis.text.y = element_text(size=18),
legend.text = element_text(size=18),
legend.title = element_text(size=18, face="bold"))+
xlab("Method") +
ylab("Factor")
ggsave(paste0(fig.dir, Sys.Date(), "_",model, "_anova.pdf"), plot = last_plot(),
width=11, height=8.5)