153 lines
6.7 KiB
R
153 lines
6.7 KiB
R
source("RemembR/R/RemembeR.R")
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library(arrow)
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library(data.table)
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library(ggplot2)
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library(filelock)
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library(argparser)
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parser <- arg_parser("Simulate data and fit corrected models.")
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parser <- add_argument(parser, "--infile", default="", help="name of the file to read.")
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parser <- add_argument(parser, "--name", default="", help="The name to safe the data to in the remember file.")
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args <- parse_args(parser)
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source("summarize_estimator.R")
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build_plot_dataset <- function(df){
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x.true <- summarize.estimator(df, 'true','x')
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z.true <- summarize.estimator(df, 'true','z')
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x.naive <- summarize.estimator(df, 'naive','x')
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z.naive <- summarize.estimator(df, 'naive','z')
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x.loa0.feasible <- summarize.estimator(df, 'loa0.feasible','x')
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z.loa0.feasible <- summarize.estimator(df,'loa0.feasible','z')
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x.loa0.mle <- summarize.estimator(df, 'loa0.mle', 'x')
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z.loa0.mle <- summarize.estimator(df, 'loa0.mle', 'z')
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x.loco.feasible <- summarize.estimator(df, 'loco.feasible', 'x')
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z.loco.feasible <- summarize.estimator(df, 'loco.feasible', 'z')
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x.loco.mle <- summarize.estimator(df, 'loco.mle', 'x')
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z.loco.mle <- summarize.estimator(df, 'loco.mle', 'z')
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x.loco.mle <- summarize.estimator(df, 'loco.mle', 'x')
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z.loco.amelia <- summarize.estimator(df, 'amelia.full', 'z')
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x.loco.amelia <- summarize.estimator(df, 'amelia.full', 'x')
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z.loco.zhang <- summarize.estimator(df, 'zhang', 'z')
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x.loco.zhang <- summarize.estimator(df, 'zhang', 'x')
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z.loco.gmm <- summarize.estimator(df, 'gmm', 'z')
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x.loco.gmm <- summarize.estimator(df, 'gmm', 'x')
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## x.mle <- summarize.estimator(df, 'mle', 'x')
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## z.mle <- summarize.estimator(df, 'mle', 'z')
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accuracy <- df[,mean(accuracy)]
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plot.df <- rbindlist(list(x.true,z.true,x.loa0.feasible,z.loa0.feasible,x.loa0.mle,z.loa0.mle,x.loco.feasible, z.loco.feasible, x.loco.mle, z.loco.mle, x.loco.amelia, z.loco.amelia,x.loco.zhang, z.loco.zhang,x.loco.gmm, z.loco.gmm,x.naive,z.naive),use.names=T)
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plot.df[,accuracy := accuracy]
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plot.df <- plot.df[,":="(sd.est=sqrt(var.est)/N.sims)]
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return(plot.df)
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}
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sims.df <- read_feather(args$infile)
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print(unique(sims.df$N))
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# df <- df[apply(df,1,function(x) !any(is.na(x)))]
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if(!('Bzx' %in% names(sims.df)))
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sims.df[,Bzx:=NA]
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if(!('accuracy_imbalance_difference' %in% names(sims.df)))
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sims.df[,accuracy_imbalance_difference:=NA]
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unique(sims.df[,'accuracy_imbalance_difference'])
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#plot.df <- build_plot_dataset(df[accuracy_imbalance_difference==0.1][N==700])
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plot.df <- build_plot_dataset(sims.df)
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change.remember.file("remember_irr.RDS",clear=TRUE)
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remember(plot.df,args$name)
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set.remember.prefix(gsub("plot.df.","",args$name))
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remember(median(sims.df$loco.accuracy),'med.loco.acc')
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#ggplot(df,aes(x=Bxy.est.mle)) + geom_histogram() + facet_grid(accuracy_imbalance_difference ~ Bzy)
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## ## ## df[gmm.ER_pval<0.05]
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## plot.df.test <- plot.df[,':='(method=factor(method,levels=c("Naive","Multiple imputation", "Multiple imputation (Classifier features unobserved)","Regression Calibration","2SLS+gmm","Bespoke MLE", "Feasible"),ordered=T),
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## N=factor(N),
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## m=factor(m))]
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## plot.df.test <- plot.df.test[(variable=='x') & (method!="Multiple imputation (Classifier features unobserved)")]
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## p <- ggplot(plot.df.test, aes(y=mean.est, ymax=mean.est + var.est/2, ymin=mean.est-var.est/2, x=method))
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## p <- p + geom_hline(data=plot.df.test, mapping=aes(yintercept=0.1),linetype=2)
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## p <- p + geom_pointrange() + facet_grid(N~m,as.table=F,scales='free') + scale_x_discrete(labels=label_wrap_gen(4))
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## print(p)
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## plot.df.test <- plot.df[,':='(method=factor(method,levels=c("Naive","Multiple imputation", "Multiple imputation (Classifier features unobserved)","Regression Calibration","2SLS+gmm","Bespoke MLE", "Feasible"),ordered=T),
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## N=factor(N),
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## m=factor(m))]
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## plot.df.test <- plot.df.test[(variable=='z') & (method!="Multiple imputation (Classifier features unobserved)")]
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## p <- ggplot(plot.df.test, aes(y=mean.est, ymax=mean.est + var.est/2, ymin=mean.est-var.est/2, x=method))
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## p <- p + geom_hline(data=plot.df.test, mapping=aes(yintercept=-0.1),linetype=2)
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## p <- p + geom_pointrange() + facet_grid(m~N,as.table=F,scales='free') + scale_x_discrete(labels=label_wrap_gen(4))
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## print(p)
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## x.mle <- df[,.(N,m,Bxy.est.mle,Bxy.ci.lower.mle, Bxy.ci.upper.mle, y_explained_variance, Bzx, Bzy, accuracy_imbalance_difference)]
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## x.mle.plot <- x.mle[,.(mean.est = mean(Bxy.est.mle),
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## var.est = var(Bxy.est.mle),
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## N.sims = .N,
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## variable='z',
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## method='Bespoke MLE'
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## ),
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## by=c("N","m",'y_explained_variance', 'Bzx', 'Bzy','accuracy_imbalance_difference')]
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## z.mle <- df[,.(N,m,Bzy.est.mle,Bzy.ci.lower.mle, Bzy.ci.upper.mle, y_explained_variance, Bzx, Bzy, accuracy_imbalance_difference)]
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## z.mle.plot <- z.mle[,.(mean.est = mean(Bzy.est.mle),
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## var.est = var(Bzy.est.mle),
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## N.sims = .N,
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## variable='z',
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## method='Bespoke MLE'
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## ),
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## by=c("N","m",'y_explained_variance','Bzx')]
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## plot.df <- z.mle.plot
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## plot.df.test <- plot.df[,':='(method=factor(method,levels=c("Naive","Multiple imputation", "Multiple imputation (Classifier features unobserved)","Regression Calibration","2SLS+gmm","Bespoke MLE", "Feasible"),ordered=T),
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## N=factor(N),
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## m=factor(m))]
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## plot.df.test <- plot.df.test[(variable=='z') & (m != 1000) & (m!=500) & (method!="Multiple imputation (Classifier features unobserved)")]
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## p <- ggplot(plot.df.test, aes(y=mean.est, ymax=mean.est + var.est/2, ymin=mean.est-var.est/2, x=method))
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## p <- p + geom_hline(aes(yintercept=0.2),linetype=2)
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## p <- p + geom_pointrange() + facet_grid(m~Bzx, Bzy,as.table=F) + scale_x_discrete(labels=label_wrap_gen(4))
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## print(p)
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## ## ggplot(plot.df[variable=='x'], aes(y=mean.est, ymax=mean.est + var.est/2, ymin=mean.est-var.est/2, x=method)) + geom_pointrange() + facet_grid(-m~N) + scale_x_discrete(labels=label_wrap_gen(10))
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## ## ggplot(plot.df,aes(y=N,x=m,color=p.sign.correct)) + geom_point() + facet_grid(variable ~ method) + scale_color_viridis_c(option='D') + theme_minimal() + xlab("Number of gold standard labels") + ylab("Total sample size")
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## ## ggplot(plot.df,aes(y=N,x=m,color=abs(mean.bias))) + geom_point() + facet_grid(variable ~ method) + scale_color_viridis_c(option='D') + theme_minimal() + xlab("Number of gold standard labels") + ylab("Total sample size")
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