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revisions and appendix update

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Nathan TeBlunthuis
2023-02-24 15:10:00 -08:00
parent c5e0a01713
commit 3dc090ec6a
18 changed files with 254 additions and 438 deletions
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266
resources/#robustness_check_plots.R#
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@@ -1,266 +0,0 @@
library(data.table)
library(ggplot2)
source('resources/functions.R')
plot.robustness.1 <- function(iv='x'){
## robustness check 1 test g
r <- readRDS('robustness_1.RDS')
baseline_df <- readRDS('remembr.RDS')[['plot.df.example.2']]
robust_df <- data.table(r$robustness_1)
## just compare the mle methods in the two examples
robust_df <- robust_df[Bzy!=0]
robust_df <- robust_df[Bzx!=0]
baseline_df[method=='true', method:='True']
robust_df[method=='true', method:='True']
baseline_df <- baseline_df[(method=='mle') | (method=='True') | (method=='naive')]
robust_df <- robust_df[(method=='mle') | (method=='True')]
baseline_df[method=='mle',method:='MLE Reported']
robust_df[method=='mle',method:='No Z in Error Model']
df <- rbind(baseline_df, robust_df, fill=TRUE)
df[method=='naive', method:='Naive']
df <- df[(N %in% c(1000,5000)) & (m %in% c(200,100))]
p <- plot.simulation(df,iv=iv,levels=c('MLE Reported','No Z in Error Model', 'Naive', 'True'))
grid.draw(p)
}
plot.robustness.1.checkassumption <- function(iv='x'){
## robustness check 1 test g
r <- readRDS('robustness_1.RDS')
baseline_df <- readRDS('remembr.RDS')[['plot.df.example.2']]
robust_df <- data.table(r$robustness_1)
## just compare the mle methods in the two examples
robust_df <- robust_df[Bzy==0]
robust_df <- robust_df[Bzx!=0]
baseline_df[method=='true', method:='True']
robust_df[method=='true', method:='True']
baseline_df <- baseline_df[(method=='mle') | (method=='naive')]
robust_df <- robust_df[(method=='mle') | (method=='True')]
baseline_df[method=='mle',method:='MLE Reported']
robust_df[method=='mle',method:='No Z in Error Model']
df <- rbind(baseline_df, robust_df, fill=TRUE)
df[method=='naive', method:='Naive']
df <- df[(N %in% c(1000,5000)) & (m %in% c(200,100))]
p <- plot.simulation(df,iv=iv,levels=c('MLE Reported','No Z in Error Model', 'Naive', 'True'))
grid.draw(p)
}
plot.robustness.1.dv <- function(iv='z'){
## robustness check 1 test g
r <- readRDS('robustness_1_dv.RDS')
baseline_df <- readRDS('remembr.RDS')[['plot.df.example.4']]
robust_df <- data.table(r$robustness_1_dv)
## just compare the mle methods in the two examples
baseline_df[method=='true', method:='True']
robust_df[method=='true', method:='True']
robust_df <- robust_df[Bxy!=0]
robust_df <- robust_df[Bzy!=0]
# robust_df <- robust_df[Bzx==-0.1]
baseline_df <- baseline_df[(method=='mle') | (method=='True') | (method=='naive')]
robust_df <- robust_df[(method=='mle') | (method=='True')]
baseline_df[method=='mle',method:='MLE Reported']
robust_df[method=='mle',method:='No Z in Error Model']
df <- rbind(baseline_df, robust_df, fill=TRUE)
df <- df[(N %in% c(1000,5000)) & (m %in% c(200,100))]
df[method=='naive', method:='Naive']
p <- plot.simulation(df,iv=iv,levels=c('MLE Reported','No Z in Error Model','Naive', 'True'))
grid.draw(p)
}
plot.robustness.2.iv <- function(iv, n.annotations=100, n.classifications=5000){
r <- readRDS("robustness_2.RDS")
robust_df <- data.table(r[['robustness_2']])
robust_df <- robust_df[(m==n.annotations) & (N==n.classifications)]
new.levels <- c("true"="True","naive"="Naïve","amelia.full"="MI", "mecor"="mecor","gmm"="GMM", "mle"="MLE", "zhang"="PL","feasible"="Feasible")
robust_df <- robust_df[,method := new.levels[method]]
robust_df <- robust_df[method != "Feasible"]
p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLE", "PL", "Feasible"))
p <- p + facet_wrap(prediction_accuracy~., ncol=4,as.table=F)
p <- p + scale_x_discrete(labels=label_wrap_gen(14)) + ylab("Estimate") + xlab("Method") + coord_flip()
p <- arrangeGrob(p,
top=grid.text("AC Accuracy",x=0.32,just='right'))
grid.draw(p)
}
robust2 <- readRDS("robustness_2_dv.RDS")
robust_2_df <- data.table(robust2[['robustness_2_dv']])
robust_2_min_acc <- min(robust_2_df[,prediction_accuracy])
robust_2_max_acc <- max(robust_2_df[,prediction_accuracy])
plot.robustness.2.dv <- function(iv, n.annotations=100, n.classifications=5000){
r <- readRDS("robustness_2_dv.RDS")
robust_df <- data.table(r[['robustness_2_dv']])
#temporary work around a bug in the makefile
## if('Px' %in% names(robust_df))
## robust_df <- robust_df[is.na(Px)]
robust_df <- robust_df[(m==n.annotations) & (N==n.classifications)]
new.levels <- c("true"="True","naive"="Naïve","amelia.full"="MI", "mecor"="mecor","gmm"="GMM", "mle"="MLE", "zhang"="PL","feasible"="Feasible")
robust_df <- robust_df[,method := new.levels[method]]
robust_df <- robust_df[method != "Feasible"]
p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLE", "PL", "Feasible"))
p <- p + facet_wrap(prediction_accuracy~., ncol=4,as.table=F)
p <- p + scale_x_discrete(labels=label_wrap_gen(14)) + ylab("Estimate") + xlab("Method") + coord_flip()
p <- arrangeGrob(p,
top=grid.text("AC Accuracy",x=0.32,just='right'))
grid.draw(p)
}
plot.robustness.3.iv <- function(iv, n.annotations=100, n.classifications=5000){
r <- readRDS('robustness_3.RDS')
robust_df <- data.table(r[['robustness_3']])
r2 <- readRDS('robustness_3_proflik.RDS')
robust_df_proflik <- data.table(r2[['robustness_3_proflik']])
new.levels <- c("true"="True","naive"="Naïve","amelia.full"="MI", "mecor"="mecor","gmm"="GMM", "mle"="MLE", "zhang"="PL","feasible"="Feasible")
robust_df <- robust_df[,method := new.levels[method]]
robust_df <- robust_df[method != "Feasible"]
robust_df <- robust_df[method=='MLE',method:='Fischer approximation']
robust_df_proflik <- robust_df_proflik[(m==n.annotations) & (N==n.classifications)]
robust_df_proflik <- robust_df_proflik[,method := new.levels[method]]
robust_df_proflik <- robust_df_proflik[method=='MLE',method:='Profile likelihood']
robust_df_proflik <- robust_df_proflik[method != "Feasible"]
df <- df[(m==n.annotations) & (N==n.classifications)]
df <- rbind(robust_df, robust_df_proflik)
p <- .plot.simulation(df, iv=iv, levels=c("True","Naïve","MI", "GMM", "Profile likelihood","Fischer approximation", "PL", "Feasible"))
p <- p + facet_wrap(Px~., ncol=3,as.table=F)
p <- p + scale_x_discrete(labels=label_wrap_gen(14)) + ylab("Estimate") + xlab("Method") + coord_flip()
p <- arrangeGrob(p,
top=grid.text("P(X)",x=0.32,just='right'))
grid.draw(p)
}
plot.robustness.3.dv <- function(iv, n.annotations=100, n.classifications=1000){
r <- readRDS('robustness_3_dv.RDS')
robust_df <- data.table(r[['robustness_3_dv']])
new.levels <- c("true"="True","naive"="Naïve","amelia.full"="MI", "mecor"="mecor","mle"="MLE", "zhang"="PL","feasible"="Feasible")
robust_df <- robust_df[(m==n.annotations) & (N==n.classifications)]
robust_df <- robust_df[,method := new.levels[method]]
robust_df <- robust_df[method != "Feasible"]
p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLE", "PL", "Feasible"))
p <- p + facet_wrap(B0~., ncol=3,as.table=F)
p <- p + scale_x_discrete(labels=label_wrap_gen(14)) + ylab("Estimate") + xlab("Method") + coord_flip()
p <- arrangeGrob(p,
top=grid.text("P(Y)",x=0.32,just='right'))
grid.draw(p)
}
plot.robustness.4.iv <- function(iv, n.annotations=100, n.classifications=1000){
r <- readRDS('robustness_4.RDS')
robust_df <- data.table(r[['robustness_4']])
new.levels <- c("true"="True","naive"="Naïve","amelia.full"="MI", "mecor"="mecor","gmm"="GMM", "mle"="MLE", "zhang"="PL","feasible"="Feasible")
robust_df <- robust_df[(m==n.annotations) & (N==n.classifications)]
robust_df <- robust_df[,method := new.levels[method]]
robust_df <- robust_df[method != "Feasible"]
robust_df <- robust_df[,y_bias=factor(robust_df$y_bias,levels=sort(unique(robust_df$y_bias),decreasing=TRUE))]
p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLE", "PL", "Feasible"))
p <- p + facet_wrap(y_bias~., ncol=3,as.table=T)
p <- p + scale_x_discrete(labels=label_wrap_gen(14)) + ylab("Estimate") + xlab("Method") + coord_flip()
p <- arrangeGrob(p,
top=grid.text("Coefficient of Y for W",x=0.32,just='right'))
grid.draw(p)
}
plot.robustness.4.iv <- function(iv, n.annotations=100, n.classifications=1000){
r <- readRDS('robustness_4.RDS')
robust_df <- data.table(r[['robustness_4']])
new.levels <- c("true"="True","naive"="Naïve","amelia.full"="MI", "mecor"="mecor","gmm"="GMM", "mle"="MLE", "zhang"="PL","feasible"="Feasible")
robust_df <- robust_df[(m==n.annotations) & (N==n.classifications)]
robust_df <- robust_df[,method := new.levels[method]]
robust_df <- robust_df[method != "Feasible"]
robust_df <- robust_df[,y_bias=factor(robust_df$y_bias,levels=sort(unique(robust_df$y_bias),decreasing=TRUE))]
p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLE", "PL", "Feasible"))
p <- p + facet_wrap(y_bias~., ncol=3,as.table=T)
p <- p + scale_x_discrete(labels=label_wrap_gen(14)) + ylab("Estimate") + xlab("Method") + coord_flip()
p <- arrangeGrob(p,
top=grid.text("Coefficient of Y for W",x=0.32,just='right'))
grid.draw(p)
}
plot.robustness.4.dv <- function(iv, n.annotations=100, n.classifications=1000){
r <- readRDS('robustness_4_dv.RDS')
robust_df <- data.table(r[['robustness_4']])
new.levels <- c("true"="True","naive"="Naïve","amelia.full"="MI", "mecor"="mecor","mle"="MLE", "zhang"="PL","feasible"="Feasible")
robust_df <- robust_df[(m==n.annotations) & (N==n.classifications)]
robust_df <- robust_df[,method := new.levels[method]]
robust_df <- robust_df[method != "Feasible"]
robust_df <- robust_df[,z_bias=factor(z_bias, levels=sort(unique(z_bias),descending=TRUE))]
p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLE", "PL", "Feasible"))
p <- p + facet_wrap(z_bias~., ncol=3,as.table=F)
p <- p + scale_x_discrete(labels=label_wrap_gen(14)) + ylab("Estimate") + xlab("Method") + coord_flip()
p <- arrangeGrob(p,
top=grid.text("Coefficient of Z on W",x=0.32,just='right'))
grid.draw(p)
}

10
resources/functions.R
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@@ -39,7 +39,7 @@ plot.simulation <- function(plot.df, iv='x', levels=c("true","naive", "amelia.fu
p <- p + geom_hline(aes(yintercept=true.est),linetype=2)
p <- p + geom_pointrange(shape=1,size=0.5)
p <- p + geom_linerange(aes(ymax=mean.ci.upper, ymin=mean.ci.lower),position=position_nudge(x=0.4), color='grey40')
p <- p + geom_linerange(aes(ymax=median.ci.upper, ymin=median.ci.lower),position=position_nudge(x=0.4), color='grey40')
return(p)
}
@@ -48,10 +48,10 @@ plot.simulation <- function(plot.df, iv='x', levels=c("true","naive", "amelia.fu
plot.simulation.iv <- function(plot.df, iv='x'){
plot.df <- plot.df[(N!=8000) & (m!=800) & (m!=200)]
new.levels <- c("true"="True","naive"="Naïve","amelia.full"="MI", "mecor"="mecor","gmm"="GMM", "mle"="MLE", "zhang"="PL","feasible"="Feasible")
new.levels <- c("true"="True","naive"="Naïve","amelia.full"="MI", "mecor"="mecor","gmm"="GMM", "mle"="MLA", "zhang"="PL","feasible"="Feasible")
plot.df[,method := new.levels[method]]
return(plot.simulation(plot.df, iv, levels=c("True","Naïve","MI", "GMM", "MLE", "PL", "Feasible")))
return(plot.simulation(plot.df, iv, levels=c("True","Naïve","MI", "GMM", "MLA", "PL", "Feasible")))
}
@@ -59,10 +59,10 @@ plot.simulation.dv <- function(plot.df, iv='x'){
plot.df <- copy(plot.df)
plot.df <- plot.df[(N!=8000) & (m!=800) & (m!=200)]
new.levels <- c("true"="True","naive"="Naïve","amelia.full"="MI", "mecor"="mecor","gmm"="GMM", "mle"="MLE", "zhang"="PL","feasible"="Feasible")
new.levels <- c("true"="True","naive"="Naïve","amelia.full"="MI", "mecor"="mecor","gmm"="GMM", "mle"="MLA", "zhang"="PL","feasible"="Feasible")
plot.df[,method:=new.levels[method]]
return(plot.simulation(plot.df, iv, levels=c("True","Naïve", "MI","MLE","PL","Feasible")))
return(plot.simulation(plot.df, iv, levels=c("True","Naïve", "MI","MLA","PL","Feasible")))
}
plot.simulation.irr <- function(plot.df,iv='x'){

94
resources/robustness_check_plots.R
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@@ -18,13 +18,13 @@ plot.robustness.1 <- function(iv='x'){
baseline_df <- baseline_df[(method=='mle') | (method=='True') | (method=='naive')]
robust_df <- robust_df[(method=='mle') | (method=='True')]
baseline_df[method=='mle',method:='MLE Reported']
baseline_df[method=='mle',method:='MLA Reported']
robust_df[method=='mle',method:='No Z in Error Model']
df <- rbind(baseline_df, robust_df, fill=TRUE)
df[method=='naive', method:='Naive']
df <- df[(N %in% c(1000,5000)) & (m %in% c(200,100))]
p <- plot.simulation(df,iv=iv,levels=c('MLE Reported','No Z in Error Model', 'Naive', 'True'))
p <- plot.simulation(df,iv=iv,levels=c('MLA Reported','No Z in Error Model', 'Naive', 'True'))
grid.draw(p)
}
@@ -44,13 +44,13 @@ plot.robustness.1.checkassumption <- function(iv='x'){
baseline_df <- baseline_df[(method=='mle') | (method=='naive')]
robust_df <- robust_df[(method=='mle') | (method=='True')]
baseline_df[method=='mle',method:='MLE Reported']
baseline_df[method=='mle',method:='MLA Reported']
robust_df[method=='mle',method:='No Z in Error Model']
df <- rbind(baseline_df, robust_df, fill=TRUE)
df[method=='naive', method:='Naive']
df <- df[(N %in% c(1000,5000)) & (m %in% c(200,100))]
p <- plot.simulation(df,iv=iv,levels=c('MLE Reported','No Z in Error Model', 'Naive', 'True'))
p <- plot.simulation(df,iv=iv,levels=c('MLA Reported','No Z in Error Model', 'Naive', 'True'))
grid.draw(p)
}
@@ -73,14 +73,14 @@ plot.robustness.1.dv <- function(iv='z'){
baseline_df <- baseline_df[(method=='mle') | (method=='True') | (method=='naive')]
robust_df <- robust_df[(method=='mle') | (method=='True')]
baseline_df[method=='mle',method:='MLE Reported']
baseline_df[method=='mle',method:='MLA Reported']
robust_df[method=='mle',method:='No Z in Error Model']
df <- rbind(baseline_df, robust_df, fill=TRUE)
df <- df[(N %in% c(1000,5000)) & (m %in% c(200,100))]
df[method=='naive', method:='Naive']
p <- plot.simulation(df,iv=iv,levels=c('MLE Reported','No Z in Error Model','Naive', 'True'))
p <- plot.simulation(df,iv=iv,levels=c('MLA Reported','No Z in Error Model','Naive', 'True'))
grid.draw(p)
}
@@ -91,11 +91,11 @@ plot.robustness.2.iv <- function(iv, n.annotations=100, n.classifications=5000){
robust_df <- robust_df[(m==n.annotations) & (N==n.classifications)]
new.levels <- c("true"="True","naive"="Naïve","amelia.full"="MI", "mecor"="mecor","gmm"="GMM", "mle"="MLE", "zhang"="PL","feasible"="Feasible")
new.levels <- c("true"="True","naive"="Naïve","amelia.full"="MI", "mecor"="mecor","gmm"="GMM", "mle"="MLA", "zhang"="PL","feasible"="Feasible")
robust_df <- robust_df[,method := new.levels[method]]
robust_df <- robust_df[method != "Feasible"]
p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLE", "PL", "Feasible"))
p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLA", "PL", "Feasible"))
p <- p + facet_wrap(prediction_accuracy~., ncol=4,as.table=F)
p <- p + scale_x_discrete(labels=label_wrap_gen(14)) + ylab("Estimate") + xlab("Method") + coord_flip()
@@ -123,11 +123,11 @@ plot.robustness.2.dv <- function(iv, n.annotations=100, n.classifications=5000){
## robust_df <- robust_df[is.na(Px)]
robust_df <- robust_df[(m==n.annotations) & (N==n.classifications)]
new.levels <- c("true"="True","naive"="Naïve","amelia.full"="MI", "mecor"="mecor","gmm"="GMM", "mle"="MLE", "zhang"="PL","feasible"="Feasible")
new.levels <- c("true"="True","naive"="Naïve","amelia.full"="MI", "mecor"="mecor","gmm"="GMM", "mle"="MLA", "zhang"="PL","feasible"="Feasible")
robust_df <- robust_df[,method := new.levels[method]]
robust_df <- robust_df[method != "Feasible"]
p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLE", "PL", "Feasible"))
p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLA", "PL", "Feasible"))
p <- p + facet_wrap(prediction_accuracy~., ncol=4,as.table=F)
p <- p + scale_x_discrete(labels=label_wrap_gen(14)) + ylab("Estimate") + xlab("Method") + coord_flip()
@@ -139,30 +139,30 @@ plot.robustness.2.dv <- function(iv, n.annotations=100, n.classifications=5000){
}
plot.robustness.3.iv <- function(iv, n.annotations=100, n.classifications=5000){
plot.robustness.3.iv <- function(iv, n.annotations=200, n.classifications=5000){
r <- readRDS('robustness_3.RDS')
robust_df <- data.table(r[['robustness_3']])
r2 <- readRDS('robustness_3_proflik.RDS')
robust_df_proflik <- data.table(r2[['robustness_3_proflik']])
new.levels <- c("true"="True","naive"="Naïve","amelia.full"="MI", "mecor"="mecor","gmm"="GMM", "mle"="MLE", "zhang"="PL","feasible"="Feasible")
new.levels <- c("true"="True","naive"="Naïve","amelia.full"="MI", "mecor"="mecor","gmm"="GMM", "mle"="MLA", "zhang"="PL","feasible"="Feasible")
robust_df <- robust_df[(m==n.annotations) & (N==n.classifications)]
robust_df <- robust_df[,method := new.levels[method]]
robust_df <- robust_df[method != "Feasible"]
robust_df <- robust_df[method=='MLE',method:='Fischer likelihood']
robust_df <- robust_df[(method != "Feasible") & (Bzx==0.3)]
robust_df <- robust_df[(method != "PL")]
## robust_df <- robust_df[method=='MLA',method:='Fischer likelihood']
robust_df_proflik <- robust_df_proflik[(m==n.annotations) & (N==n.classifications)]
robust_df_proflik <- robust_df_proflik[method=='MLE',method:='Profile likelihood']
## robust_df_proflik <- robust_df_proflik[(m==n.annotations) & (N==n.classifications)]
## robust_df_proflik <- robust_df_proflik[,method := new.levels[method]]
## robust_df_proflik <- robust_df_proflik[method=='MLA']
## robust_df_proflik <- robust_df_proflik[method=='MLA',method:='Profile likelihood']
## robust_df_proflik <- robust_df_proflik[method != "Feasible"]
## df <- rbind(robust_df, robust_df_proflik)
robust_df_proflik <- robust_df_proflik[,method := new.levels[method]]
robust_df_proflik <- robust_df_proflik[method != "Feasible"]
df <- rbind(robust_df, robust_df_proflik)
p <- .plot.simulation(df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLE", "PL", "Feasible"))
p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM","MLA", "PL", "Feasible"))
p <- p + facet_wrap(Px~., ncol=3,as.table=F)
p <- p + scale_x_discrete(labels=label_wrap_gen(14)) + ylab("Estimate") + xlab("Method") + coord_flip()
@@ -177,16 +177,17 @@ plot.robustness.3.dv <- function(iv, n.annotations=100, n.classifications=1000){
r <- readRDS('robustness_3_dv.RDS')
robust_df <- data.table(r[['robustness_3_dv']])
new.levels <- c("true"="True","naive"="Naïve","amelia.full"="MI", "mecor"="mecor","mle"="MLE", "zhang"="PL","feasible"="Feasible")
new.levels <- c("true"="True","naive"="Naïve","amelia.full"="MI", "mecor"="mecor","mle"="MLA", "zhang"="PL","feasible"="Feasible")
robust_df <- robust_df[(m==n.annotations) & (N==n.classifications)]
robust_df <- robust_df[,method := new.levels[method]]
robust_df <- robust_df[method != "Feasible"]
p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLE", "PL", "Feasible"))
robust_df <- robust_df[,Py := round(plogis(B0),2)]
p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLA", "PL", "Feasible"))
robust_df <- robust_df[(method != "PL")]
p <- p + facet_wrap(B0~., ncol=3,as.table=F)
p <- p + facet_wrap(Py~., ncol=3,as.table=F,scales='free')
p <- p + scale_x_discrete(labels=label_wrap_gen(14)) + ylab("Estimate") + xlab("Method") + coord_flip()
p <- arrangeGrob(p,
@@ -194,20 +195,21 @@ plot.robustness.3.dv <- function(iv, n.annotations=100, n.classifications=1000){
grid.draw(p)
}
plot.robustness.4.iv <- function(iv, n.annotations=100, n.classifications=1000){
r <- readRDS('robustness_4.RDS')
robust_df <- data.table(r[['robustness_4']])
new.levels <- c("true"="True","naive"="Naïve","amelia.full"="MI", "mecor"="mecor","gmm"="GMM", "mle"="MLE", "zhang"="PL","feasible"="Feasible")
new.levels <- c("true"="True","naive"="Naïve","amelia.full"="MI", "mecor"="mecor","gmm"="GMM", "mle"="MLA", "zhang"="PL","feasible"="Feasible")
robust_df <- robust_df[(m==n.annotations) & (N==n.classifications)]
robust_df <- robust_df[,method := new.levels[method]]
robust_df <- robust_df[method != "Feasible"]
robust_df <- robust_df[,y_bias=factor(robust_df$y_bias,levels=sort(unique(robust_df$y_bias),decreasing=TRUE))]
p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLE", "PL", "Feasible"))
robust_df <- robust_df[,y_bias:=factor(robust_df$y_bias,levels=sort(unique(robust_df$y_bias),decreasing=TRUE))]
robust_df <- robust_df[Bzx==1]
p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLA", "PL", "Feasible"))
p <- p + facet_wrap(y_bias~., ncol=3,as.table=T)
p <- p + scale_x_discrete(labels=label_wrap_gen(14)) + ylab("Estimate") + xlab("Method") + coord_flip()
@@ -219,45 +221,21 @@ plot.robustness.4.iv <- function(iv, n.annotations=100, n.classifications=1000){
}
plot.robustness.4.iv <- function(iv, n.annotations=100, n.classifications=1000){
r <- readRDS('robustness_4.RDS')
robust_df <- data.table(r[['robustness_4']])
new.levels <- c("true"="True","naive"="Naïve","amelia.full"="MI", "mecor"="mecor","gmm"="GMM", "mle"="MLE", "zhang"="PL","feasible"="Feasible")
robust_df <- robust_df[(m==n.annotations) & (N==n.classifications)]
robust_df <- robust_df[,method := new.levels[method]]
robust_df <- robust_df[method != "Feasible"]
robust_df <- robust_df[,y_bias=factor(robust_df$y_bias,levels=sort(unique(robust_df$y_bias),decreasing=TRUE))]
p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLE", "PL", "Feasible"))
p <- p + facet_wrap(y_bias~., ncol=3,as.table=T)
p <- p + scale_x_discrete(labels=label_wrap_gen(14)) + ylab("Estimate") + xlab("Method") + coord_flip()
p <- arrangeGrob(p,
top=grid.text("Coefficient of Y for W",x=0.32,just='right'))
grid.draw(p)
}
plot.robustness.4.dv <- function(iv, n.annotations=100, n.classifications=1000){
r <- readRDS('robustness_4_dv.RDS')
robust_df <- data.table(r[['robustness_4']])
new.levels <- c("true"="True","naive"="Naïve","amelia.full"="MI", "mecor"="mecor","mle"="MLE", "zhang"="PL","feasible"="Feasible")
new.levels <- c("true"="True","naive"="Naïve","amelia.full"="MI", "mecor"="mecor","mle"="MLA", "zhang"="PL","feasible"="Feasible")
robust_df <- robust_df[(m==n.annotations) & (N==n.classifications)]
robust_df <- robust_df[,method := new.levels[method]]
robust_df <- robust_df[method != "Feasible"]
robust_df <- robust_df[,z_bias=factor(z_bias, levels=sort(unique(z_bias),descending=TRUE))]
robust_df <- robust_df[,z_bias:=factor(z_bias, levels=sort(unique(z_bias),decreasing=TRUE))]
p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLE", "PL", "Feasible"))
robust_df <- robust_df[Bzx==1]
p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLA", "PL", "Feasible"))
p <- p + facet_wrap(z_bias~., ncol=3,as.table=F)
p <- p + scale_x_discrete(labels=label_wrap_gen(14)) + ylab("Estimate") + xlab("Method") + coord_flip()