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="robustness_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 = .
+## 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){
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')
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]
projects / ml_measurement_error_public.git / blobdiff