X-Git-Url: https://code.communitydata.science/ml_measurement_error_public.git/blobdiff_plain/6057688060b5bf2a94f2b96b65b275a91991c0f3..e41d11afb9a80180feff844666e3ee463d20a7cd:/simulations/plot_example_2_B.R diff --git a/simulations/plot_example_2_B.R b/simulations/plot_example_2_B.R new file mode 100644 index 0000000..62c6848 --- /dev/null +++ b/simulations/plot_example_2_B.R @@ -0,0 +1,277 @@ +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")