Added, but didn't test the remaining robustness checks.

[ml_measurement_error_public.git] / simulations / plot_example.R

diff --git a/simulations/plot_example.R b/simulations/plot_example.R
index 7a853b74e6e3ee1f4a25cba281726edbb6854a7d..09d6bf3e7394c95439133896a2404c89e080f671 100644 (file)
--- a/simulations/plot_example.R
+++ b/simulations/plot_example.R
@@ -5,52 +5,58 @@ library(ggplot2)
 library(filelock)
 library(argparser)
 
+source("summarize_estimator.R")
+
+
 parser <- arg_parser("Simulate data and fit corrected models.")
-parser <- add_argument(parser, "--infile", default="", help="name of the file to read.")
+parser <- add_argument(parser, "--infile", default="example_2.feather", help="name of the file to read.")
+parser <- add_argument(parser, "--remember-file", default="remembr.RDS", help="name of the remember file.")
 parser <- add_argument(parser, "--name", default="", help="The name to safe the data to in the remember file.")
 args <- parse_args(parser)
 
-summarize.estimator <- function(df, suffix='naive', coefname='x'){
-
-    part <- df[,c('N',
-                  'm',
-                  'Bxy',
-                  paste0('B',coefname,'y.est.',suffix),
-                  paste0('B',coefname,'y.ci.lower.',suffix),
-                  paste0('B',coefname,'y.ci.upper.',suffix),
-                  'y_explained_variance',
-                  'Bzx',
-                  'Bzy',
-                  'accuracy_imbalance_difference'
-                  ),
-               with=FALSE]
+
+
+## summarize.estimator <- function(df, suffix='naive', coefname='x'){
+
+##     part <- df[,c('N',
+##                   'm',
+##                   'Bxy',
+##                   paste0('B',coefname,'y.est.',suffix),
+##                   paste0('B',coefname,'y.ci.lower.',suffix),
+##                   paste0('B',coefname,'y.ci.upper.',suffix),
+##                   'y_explained_variance',
+##                   'Bzx',
+##                   'Bzy',
+##                   'accuracy_imbalance_difference'
+##                   ),
+##                with=FALSE]
     
-    true.in.ci <- as.integer((part$Bxy >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (part$Bxy <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]]))
-    zero.in.ci <- as.integer(0 >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (0 <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]])
-    bias <- part$Bxy - part[[paste0('B',coefname,'y.est.',suffix)]]
-    sign.correct <- as.integer(sign(part$Bxy) == sign(part[[paste0('B',coefname,'y.est.',suffix)]]))
-
-    part <- part[,':='(true.in.ci = true.in.ci,
-                       zero.in.ci = zero.in.ci,
-                       bias=bias,
-                       sign.correct =sign.correct)]
-
-    part.plot <- part[, .(p.true.in.ci = mean(true.in.ci),
-                          mean.bias = mean(bias),
-                          mean.est = mean(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
-                          var.est = var(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
-                          est.upper.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.95,na.rm=T),
-                          est.lower.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.05,na.rm=T),
-                          N.sims = .N,
-                          p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
-                          variable=coefname,
-                          method=suffix
-                          ),
-                      by=c("N","m",'y_explained_variance','Bzx', 'Bzy', 'accuracy_imbalance_difference')
-                      ]
+##     true.in.ci <- as.integer((part$Bxy >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (part$Bxy <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]]))
+##     zero.in.ci <- as.integer(0 >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (0 <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]])
+##     bias <- part$Bxy - part[[paste0('B',coefname,'y.est.',suffix)]]
+##     sign.correct <- as.integer(sign(part$Bxy) == sign(part[[paste0('B',coefname,'y.est.',suffix)]]))
+
+##     part <- part[,':='(true.in.ci = true.in.ci,
+##                        zero.in.ci = zero.in.ci,
+##                        bias=bias,
+##                        sign.correct =sign.correct)]
+
+##     part.plot <- part[, .(p.true.in.ci = mean(true.in.ci),
+##                           mean.bias = mean(bias),
+##                           mean.est = mean(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
+##                           var.est = var(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
+##                           est.upper.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.95,na.rm=T),
+##                           est.lower.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.05,na.rm=T),
+##                           N.sims = .N,
+##                           p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
+##                           variable=coefname,
+##                           method=suffix
+##                           ),
+##                       by=c("N","m",'y_explained_variance','Bzx', 'Bzy', 'accuracy_imbalance_difference')
+##                       ]
     
-    return(part.plot)
-}
+##     return(part.plot)
+## }
 
 build_plot_dataset <- function(df){
     
@@ -70,13 +76,13 @@ build_plot_dataset <- function(df){
 
     z.amelia.full <- summarize.estimator(df, 'amelia.full', 'z')
     
-    x.mecor <- summarize.estimator(df, 'mecor', 'x')
+    ## x.mecor <- summarize.estimator(df, 'mecor', 'x')
 
-    z.mecor <- summarize.estimator(df, 'mecor', 'z')
+    ## z.mecor <- summarize.estimator(df, 'mecor', 'z')
 
-    x.mecor <- summarize.estimator(df, 'mecor', 'x')
+    ## x.mecor <- summarize.estimator(df, 'mecor', 'x')
 
-    z.mecor <- summarize.estimator(df, 'mecor', 'z')
+    ## z.mecor <- summarize.estimator(df, 'mecor', 'z')
 
     x.mle <- summarize.estimator(df, 'mle', 'x')
 
@@ -91,31 +97,48 @@ build_plot_dataset <- function(df){
     z.gmm <- summarize.estimator(df, 'gmm', 'z')
 
     accuracy <- df[,mean(accuracy)]
-    plot.df <- rbindlist(list(x.true,z.true,x.naive,z.naive,x.amelia.full,z.amelia.full,x.mecor, z.mecor, x.gmm, z.gmm, x.feasible, z.feasible,z.mle, x.mle, x.zhang, z.zhang, x.gmm, z.gmm),use.names=T)
+    plot.df <- rbindlist(list(x.true,z.true,x.naive,z.naive,x.amelia.full,z.amelia.full,x.gmm, z.gmm, x.feasible, z.feasible,z.mle, x.mle, x.zhang, z.zhang, x.gmm, z.gmm),use.names=T)
     plot.df[,accuracy := accuracy]
     plot.df <- plot.df[,":="(sd.est=sqrt(var.est)/N.sims)]
     return(plot.df)
 }
 
 
-plot.df <- read_feather(args$infile)
-print(unique(plot.df$N))
+sims.df <- read_feather(args$infile)
+unique(sims.df[,.N,by=.(N,m)])
+print(unique(sims.df$N))
 
 # df <- df[apply(df,1,function(x) !any(is.na(x)))]
 
-if(!('Bzx' %in% names(plot.df)))
-    plot.df[,Bzx:=NA]
+if(!('Bzx' %in% names(sims.df)))
+    sims.df[,Bzx:=NA]
 
-if(!('accuracy_imbalance_difference' %in% names(plot.df)))
-    plot.df[,accuracy_imbalance_difference:=NA]
+if(!('accuracy_imbalance_difference' %in% names(sims.df)))
+    sims.df[,accuracy_imbalance_difference:=NA]
 
-unique(plot.df[,'accuracy_imbalance_difference'])
+unique(sims.df[,'accuracy_imbalance_difference'])
 
+change.remember.file(args$remember_file, clear=TRUE)
 #plot.df <- build_plot_dataset(df[accuracy_imbalance_difference==0.1][N==700])
-plot.df <- build_plot_dataset(plot.df)
+plot.df <- build_plot_dataset(sims.df)
 
 remember(plot.df,args$name)
 
+set.remember.prefix(gsub("plot.df.","",args$name))
+
+remember(median(sims.df$cor.xz),'med.cor.xz')
+remember(median(sims.df$accuracy),'med.accuracy')
+remember(median(sims.df$accuracy.y0),'med.accuracy.y0')
+remember(median(sims.df$accuracy.y1),'med.accuracy.y1')
+remember(median(sims.df$fpr),'med.fpr')
+remember(median(sims.df$fpr.y0),'med.fpr.y0')
+remember(median(sims.df$fpr.y1),'med.fpr.y1')
+remember(median(sims.df$fnr),'med.fnr')
+remember(median(sims.df$fnr.y0),'med.fnr.y0')
+remember(median(sims.df$fnr.y1),'med.fnr.y1')
+
+remember(median(sims.df$cor.resid.w_pred),'cor.resid.w_pred')
+
 #ggplot(df,aes(x=Bxy.est.mle)) + geom_histogram() + facet_grid(accuracy_imbalance_difference ~ Bzy)
 
 ## ## ## df[gmm.ER_pval<0.05]