groceryheist/ml_measurement_error_overleaf
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Restore feasible estimator in robustness check 2.

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Nathan TeBlunthuis
2023-03-07 13:58:12 -08:00
parent 8a7deff29b
commit e0cb67a20d
1 changed files with 3 additions and 17 deletions
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18
resources/robustness_check_plots.R
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@@ -1,4 +1,4 @@
library(data.table) x1library(data.table)
library(ggplot2) library(ggplot2)
source('resources/functions.R') source('resources/functions.R')
@@ -122,15 +122,12 @@ plot.robustness.2.dv <- function(iv, n.annotations=100, n.classifications=5000){
robust_df <- data.table(r[['robustness_2_dv']]) 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)] 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"="MLA", "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 := new.levels[method]]
robust_df <- robust_df[method != "Feasible"]
p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLA", "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 + facet_wrap(prediction_accuracy~., ncol=4,as.table=F)
@@ -155,16 +152,6 @@ plot.robustness.3.iv <- function(iv, n.annotations=200, n.classifications=5000){
robust_df <- robust_df[,method := new.levels[method]] robust_df <- robust_df[,method := new.levels[method]]
robust_df <- robust_df[(method != "Feasible") & (Bzx==0.3)] robust_df <- robust_df[(method != "Feasible") & (Bzx==0.3)]
robust_df <- robust_df[(method != "PL")] 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 := 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)
p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM","MLA", "PL", "Feasible")) p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM","MLA", "PL", "Feasible"))
@@ -208,7 +195,6 @@ plot.robustness.4.iv <- function(iv, n.annotations=200, n.classifications=5000){
robust_df <- robust_df[(m==n.annotations) & (N==n.classifications)] robust_df <- robust_df[(m==n.annotations) & (N==n.classifications)]
robust_df <- robust_df[,method := new.levels[method]] 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))] 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] robust_df <- robust_df[Bzx==1]