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