source("RemembR/R/RemembeR.R") library(arrow) library(data.table) library(ggplot2) library(filelock) library(argparse) l <- filelock::lock("example_2_B.feather_lock",exclusive=FALSE) df <- data.table(read_feather("example_2_B.feather")) filelock::unlock(l) 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, "--name", default="", help="The name to safe the data to in the remember file.") args <- parse_args(parser) build_plot_dataset <- function(df){ x.naive <- df[,.(N, m, Bxy, Bxy.est.naive, Bxy.ci.lower.naive, Bxy.ci.upper.naive)] x.naive <- x.naive[,':='(true.in.ci = as.integer((Bxy >= Bxy.ci.lower.naive) & (Bxy <= Bxy.ci.upper.naive)), zero.in.ci = (0 >= Bxy.ci.lower.naive) & (0 <= Bxy.ci.upper.naive), bias = Bxy - Bxy.est.naive, Bxy.est.naive = Bxy.est.naive, sign.correct = as.integer(sign(Bxy) == sign(Bxy.est.naive)))] x.naive.plot <- x.naive[,.(p.true.in.ci = mean(true.in.ci), mean.bias = mean(bias), mean.est = mean(Bxy.est.naive), var.est = var(Bxy.est.naive), N.sims = .N, p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))), variable='x', method='Naive' ), by=c('N','m')] g.naive <- df[,.(N, m, Bgy, Bgy.est.naive, Bgy.ci.lower.naive, Bgy.ci.upper.naive)] g.naive <- g.naive[,':='(true.in.ci = as.integer((Bgy >= Bgy.ci.lower.naive) & (Bgy <= Bgy.ci.upper.naive)), zero.in.ci = (0 >= Bgy.ci.lower.naive) & (0 <= Bgy.ci.upper.naive), bias = Bgy - Bgy.est.naive, Bgy.est.naive = Bgy.est.naive, sign.correct = as.integer(sign(Bgy) == sign(Bgy.est.naive)))] g.naive.plot <- g.naive[,.(p.true.in.ci = mean(true.in.ci), mean.bias = mean(bias), mean.est = mean(Bgy.est.naive), var.est = var(Bgy.est.naive), N.sims = .N, p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))), variable='g', method='Naive' ), by=c('N','m')] x.feasible <- df[,.(N, m, Bxy, Bxy.est.feasible, Bxy.ci.lower.feasible, Bxy.ci.upper.feasible)] x.feasible <- x.feasible[,':='(true.in.ci = as.integer((Bxy >= Bxy.ci.lower.feasible) & (Bxy <= Bxy.ci.upper.feasible)), zero.in.ci = (0 >= Bxy.ci.lower.feasible) & (0 <= Bxy.ci.upper.feasible), bias = Bxy - Bxy.est.feasible, Bxy.est.feasible = Bxy.est.feasible, sign.correct = as.integer(sign(Bxy) == sign(Bxy.est.feasible)))] x.feasible.plot <- x.feasible[,.(p.true.in.ci = mean(true.in.ci), mean.bias = mean(bias), mean.est = mean(Bxy.est.feasible), var.est = var(Bxy.est.feasible), N.sims = .N, p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))), variable='x', method='Feasible' ), by=c('N','m')] g.feasible <- df[,.(N, m, Bgy, Bgy.est.feasible, Bgy.ci.lower.feasible, Bgy.ci.upper.feasible)] g.feasible <- g.feasible[,':='(true.in.ci = as.integer((Bgy >= Bgy.ci.lower.feasible) & (Bgy <= Bgy.ci.upper.feasible)), zero.in.ci = (0 >= Bgy.ci.lower.feasible) & (0 <= Bgy.ci.upper.feasible), bias = Bgy - Bgy.est.feasible, Bgy.est.feasible = Bgy.est.feasible, sign.correct = as.integer(sign(Bgy) == sign(Bgy.est.feasible)))] g.feasible.plot <- g.feasible[,.(p.true.in.ci = mean(true.in.ci), mean.bias = mean(bias), mean.est = mean(Bgy.est.feasible), var.est = var(Bgy.est.feasible), N.sims = .N, p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))), variable='g', method='Feasible' ), by=c('N','m')] x.amelia.full <- df[,.(N, m, Bxy, Bxy.est.true, Bxy.ci.lower.amelia.full, Bxy.ci.upper.amelia.full, Bxy.est.amelia.full)] x.amelia.full <- x.amelia.full[,':='(true.in.ci = (Bxy.est.true >= Bxy.ci.lower.amelia.full) & (Bxy.est.true <= Bxy.ci.upper.amelia.full), zero.in.ci = (0 >= Bxy.ci.lower.amelia.full) & (0 <= Bxy.ci.upper.amelia.full), bias = Bxy.est.true - Bxy.est.amelia.full, sign.correct = sign(Bxy.est.true) == sign(Bxy.est.amelia.full))] x.amelia.full.plot <- x.amelia.full[,.(p.true.in.ci = mean(as.integer(true.in.ci)), mean.bias = mean(bias), p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))), mean.est = mean(Bxy.est.amelia.full), var.est = var(Bxy.est.amelia.full), N.sims = .N, variable='x', method='Multiple imputation' ), by=c('N','m')] g.amelia.full <- df[,.(N, m, Bgy.est.true, Bgy.est.amelia.full, Bgy.ci.lower.amelia.full, Bgy.ci.upper.amelia.full)] g.amelia.full <- g.amelia.full[,':='(true.in.ci = (Bgy.est.true >= Bgy.ci.lower.amelia.full) & (Bgy.est.true <= Bgy.ci.upper.amelia.full), zero.in.ci = (0 >= Bgy.ci.lower.amelia.full) & (0 <= Bgy.ci.upper.amelia.full), bias = Bgy.est.amelia.full - Bgy.est.true, sign.correct = sign(Bgy.est.true) == sign(Bgy.est.amelia.full))] g.amelia.full.plot <- g.amelia.full[,.(p.true.in.ci = mean(as.integer(true.in.ci)), mean.bias = mean(bias), p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))), mean.est = mean(Bgy.est.amelia.full), var.est = var(Bgy.est.amelia.full), N.sims = .N, variable='g', method='Multiple imputation' ), by=c('N','m')] x.amelia.nok <- df[,.(N, m, Bxy.est.true, Bxy.est.amelia.nok, Bxy.ci.lower.amelia.nok, Bxy.ci.upper.amelia.nok)] x.amelia.nok <- x.amelia.nok[,':='(true.in.ci = (Bxy.est.true >= Bxy.ci.lower.amelia.nok) & (Bxy.est.true <= Bxy.ci.upper.amelia.nok), zero.in.ci = (0 >= Bxy.ci.lower.amelia.nok) & (0 <= Bxy.ci.upper.amelia.nok), bias = Bxy.est.amelia.nok - Bxy.est.true, sign.correct = sign(Bxy.est.true) == sign(Bxy.est.amelia.nok))] x.amelia.nok.plot <- x.amelia.nok[,.(p.true.in.ci = mean(as.integer(true.in.ci)), mean.bias = mean(bias), p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))), mean.est = mean(Bxy.est.amelia.nok), var.est = var(Bxy.est.amelia.nok), N.sims = .N, variable='x', method='Multiple imputation (Classifier features unobserved)' ), by=c('N','m')] g.amelia.nok <- df[,.(N, m, Bgy.est.true, Bgy.est.amelia.nok, Bgy.ci.lower.amelia.nok, Bgy.ci.upper.amelia.nok)] g.amelia.nok <- g.amelia.nok[,':='(true.in.ci = (Bgy.est.true >= Bgy.ci.lower.amelia.nok) & (Bgy.est.true <= Bgy.ci.upper.amelia.nok), zero.in.ci = (0 >= Bgy.ci.lower.amelia.nok) & (0 <= Bgy.ci.upper.amelia.nok), bias = Bgy.est.amelia.nok - Bgy.est.true, sign.correct = sign(Bgy.est.true) == sign(Bgy.est.amelia.nok))] g.amelia.nok.plot <- g.amelia.nok[,.(p.true.in.ci = mean(as.integer(true.in.ci)), mean.bias = mean(bias), p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))), mean.est = mean(Bgy.est.amelia.nok), var.est = var(Bgy.est.amelia.nok), N.sims = .N, variable='g', method="Multiple imputation (Classifier features unobserved)" ), by=c('N','m')] x.mecor <- df[,.(N,m,Bxy.est.true, Bxy.est.mecor,Bxy.lower.mecor, Bxy.upper.mecor)] x.mecor <- x.mecor[,':='(true.in.ci = (Bxy.est.true >= Bxy.lower.mecor) & (Bxy.est.true <= Bxy.upper.mecor), zero.in.ci = (0 >= Bxy.lower.mecor) & (0 <= Bxy.upper.mecor), bias = Bxy.est.mecor - Bxy.est.true, sign.correct = sign(Bxy.est.true) == sign(Bxy.est.mecor))] x.mecor.plot <- x.mecor[,.(p.true.in.ci = mean(as.integer(true.in.ci)), mean.bias = mean(bias), p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))), mean.est = mean(Bxy.est.mecor), var.est = var(Bxy.est.mecor), N.sims = .N, variable='x', method='Regression Calibration' ), by=c('N','m')] g.mecor <- df[,.(N,m,Bgy.est.true, Bgy.est.mecor,Bgy.lower.mecor, Bgy.upper.mecor)] g.mecor <- g.mecor[,':='(true.in.ci = (Bgy.est.true >= Bgy.lower.mecor) & (Bgy.est.true <= Bgy.upper.mecor), zero.in.ci = (0 >= Bgy.lower.mecor) & (0 <= Bgy.upper.mecor), bias = Bgy.est.mecor - Bgy.est.true, sign.correct = sign(Bgy.est.true) == sign(Bgy.est.mecor))] g.mecor.plot <- g.mecor[,.(p.true.in.ci = mean(as.integer(true.in.ci)), mean.bias = mean(bias), p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))), mean.est = mean(Bgy.est.mecor), var.est = var(Bgy.est.mecor), N.sims = .N, variable='g', method='Regression Calibration' ), by=c('N','m')] ## x.mecor <- df[,.(N,m,Bgy.est.true, Bgy.est.mecor,Bgy.ci.lower.mecor, Bgy.ci.upper.mecor)] ## x.mecor <- x.mecor[,':='(true.in.ci = (Bgy.est.true >= Bgy.ci.lower.mecor) & (Bgy.est.true <= Bgy.ci.upper.mecor), ## zero.in.ci = (0 >= Bgy.ci.lower.mecor) & (0 <= Bgy.ci.upper.mecor), ## bias = Bgy.est.mecor - Bgy.est.true, ## sign.correct = sign(Bgy.est.true) == sign(Bgy.est.mecor))] ## x.mecor.plot <- x.mecor[,.(p.true.in.ci = mean(as.integer(true.in.ci)), ## mean.bias = mean(bias), ## p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))), ## variable='g', ## method='Regression Calibration' ## ), ## by=c('N','m')] x.gmm <- df[,.(N,m,Bxy.est.true, Bxy.est.gmm,Bxy.ci.lower.gmm, Bxy.ci.upper.gmm)] x.gmm <- x.gmm[,':='(true.in.ci = (Bxy.est.true >= Bxy.ci.lower.gmm) & (Bxy.est.true <= Bxy.ci.upper.gmm), zero.in.ci = (0 >= Bxy.ci.lower.gmm) & (0 <= Bxy.ci.upper.gmm), bias = Bxy.est.gmm - Bxy.est.true, sign.correct = sign(Bxy.est.true) == sign(Bxy.est.gmm))] x.gmm.plot <- x.gmm[,.(p.true.in.ci = mean(as.integer(true.in.ci)), mean.bias = mean(bias), p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))), mean.est = mean(Bxy.est.gmm), var.est = var(Bxy.est.gmm), N.sims = .N, variable='x', method='2SLS+gmm' ), by=c('N','m')] g.gmm <- df[,.(N,m,Bgy.est.true, Bgy.est.gmm,Bgy.ci.lower.gmm, Bgy.ci.upper.gmm)] g.gmm <- g.gmm[,':='(true.in.ci = (Bgy.est.true >= Bgy.ci.lower.gmm) & (Bgy.est.true <= Bgy.ci.upper.gmm), zero.in.ci = (0 >= Bgy.ci.lower.gmm) & (0 <= Bgy.ci.upper.gmm), bias = Bgy.est.gmm - Bgy.est.true, sign.correct = sign(Bgy.est.true) == sign(Bgy.est.gmm))] g.gmm.plot <- g.gmm[,.(p.true.in.ci = mean(as.integer(true.in.ci)), mean.bias = mean(bias), p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))), mean.est = mean(Bgy.est.gmm), var.est = var(Bgy.est.gmm), N.sims = .N, variable='g', method='2SLS+gmm' ), by=c('N','m')] accuracy <- df[,mean(accuracy)] return(plot.df) } df <- read_feather(args$infile) plot.df <- build_plot_dataset(df) remember(plot.df,args$name)) ## df[gmm.ER_pval<0.05] ## plot.df <- rbindlist(list(x.naive.plot,g.naive.plot,x.amelia.full.plot,g.amelia.full.plot,x.amelia.nok.plot,g.amelia.nok.plot, x.mecor.plot, g.mecor.plot, x.gmm.plot, g.gmm.plot, x.feasible.plot, g.feasible.plot),use.names=T) ## plot.df[,accuracy := accuracy] ## # plot.df <- plot.df[,":="(sd.est=sqrt(var.est)/N.sims)] ## 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)) ## 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") ## 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")