X-Git-Url: https://code.communitydata.science/ml_measurement_error_public.git/blobdiff_plain/cb1e895ff1e3359db17d918caa67b758c0d7e901..82fe7b0f482a71c95e8ae99f7e6d37b79357506a:/simulations/plot_dv_example.R diff --git a/simulations/plot_dv_example.R b/simulations/plot_dv_example.R index 961bc87..45a5d51 100644 --- a/simulations/plot_dv_example.R +++ b/simulations/plot_dv_example.R @@ -6,208 +6,76 @@ library(filelock) library(argparser) 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_4.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) -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')] +## 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', +## 'Bzy' +## ), +## with=FALSE] - - 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')] +## 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), +## est.lower.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.05), +## N.sims = .N, +## p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))), +## variable=coefname, +## method=suffix +## ), +## by=c("N","m",'Bzy','y_explained_variance') +## ] +## return(part.plot) +## } - 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')] - +source("summarize_estimator.R") - 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')] - +build_plot_dataset <- function(df){ + x.true <- summarize.estimator(df, 'true','x') + z.true <- summarize.estimator(df, 'true','z') - 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.mle <- df[,.(N,m, Bxy.est.true, Bxy.est.mle, Bxy.ci.lower.mle, Bxy.ci.upper.mle)] - - x.mle <- x.mle[,':='(true.in.ci = (Bxy.est.true >= Bxy.ci.lower.mle) & (Bxy.est.true <= Bxy.ci.upper.mle), - zero.in.ci = (0 >= Bxy.ci.lower.mle) & (0 <= Bxy.ci.upper.mle), - bias = Bxy.est.mle - Bxy.est.true, - sign.correct = sign(Bxy.est.true) == sign(Bxy.est.mle))] - - x.mle.plot <- x.mle[,.(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.mle), - var.est = var(Bxy.est.mle), - N.sims = .N, - variable='x', - method='Maximum Likelihood' - ), - by=c('N','m')] - - - - g.mle <- df[,.(N,m, Bgy.est.true, Bgy.est.mle, Bgy.ci.lower.mle, Bgy.ci.upper.mle)] - - g.mle <- g.mle[,':='(true.in.ci = (Bgy.est.true >= Bgy.ci.lower.mle) & (Bgy.est.true <= Bgy.ci.upper.mle), - zero.in.ci = (0 >= Bgy.ci.lower.mle) & (0 <= Bgy.ci.upper.mle), - bias = Bgy.est.mle - Bgy.est.true, - sign.correct = sign(Bgy.est.true) == sign(Bgy.est.mle))] - - g.mle.plot <- g.mle[,.(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.mle), - var.est = var(Bgy.est.mle), - N.sims = .N, - variable='g', - method='Maximum Likelihood' - ), - by=c('N','m')] - - - - - x.pseudo <- df[,.(N,m, Bxy.est.true, Bxy.est.pseudo, Bxy.ci.lower.pseudo, Bxy.ci.upper.pseudo)] - - x.pseudo <- x.pseudo[,':='(true.in.ci = (Bxy.est.true >= Bxy.ci.lower.pseudo) & (Bxy.est.true <= Bxy.ci.upper.pseudo), - zero.in.ci = (0 >= Bxy.ci.lower.pseudo) & (0 <= Bxy.ci.upper.pseudo), - bias = Bxy.est.pseudo - Bxy.est.true, - sign.correct = sign(Bxy.est.true) == sign(Bxy.est.pseudo))] - - x.pseudo.plot <- x.pseudo[,.(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.pseudo), - var.est = var(Bxy.est.pseudo), - N.sims = .N, - variable='x', - method='Pseudo Likelihood' - ), - by=c('N','m')] - - - - g.pseudo <- df[,.(N,m, Bgy.est.true, Bgy.est.pseudo, Bgy.ci.lower.pseudo, Bgy.ci.upper.pseudo)] - - g.pseudo <- g.pseudo[,':='(true.in.ci = (Bgy.est.true >= Bgy.ci.lower.pseudo) & (Bgy.est.true <= Bgy.ci.upper.pseudo), - zero.in.ci = (0 >= Bgy.ci.lower.pseudo) & (0 <= Bgy.ci.upper.pseudo), - bias = Bgy.est.pseudo - Bgy.est.true, - sign.correct = sign(Bgy.est.true) == sign(Bgy.est.pseudo))] - - g.pseudo.plot <- g.pseudo[,.(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.pseudo), - var.est = var(Bgy.est.pseudo), - N.sims = .N, - variable='g', - method='Pseudo Likelihood' - ), - by=c('N','m')] + x.naive <- summarize.estimator(df, 'naive','x') + z.naive <- summarize.estimator(df, 'naive','z') + + x.feasible <- summarize.estimator(df, 'feasible','x') + z.feasible <- summarize.estimator(df, 'feasible','z') + + x.amelia.full <- summarize.estimator(df, 'amelia.full','x') + z.amelia.full <- summarize.estimator(df, 'amelia.full','z') + x.mle <- summarize.estimator(df, 'mle','x') + z.mle <- summarize.estimator(df, 'mle','z') + x.zhang <- summarize.estimator(df, 'zhang','x') + z.zhang <- summarize.estimator(df, 'zhang','z') accuracy <- df[,mean(accuracy)] - plot.df <- rbindlist(list(x.naive.plot,g.naive.plot,x.amelia.full.plot,g.amelia.full.plot,x.mle.plot, g.mle.plot, x.pseudo.plot, g.pseudo.plot, x.feasible.plot, g.feasible.plot),use.names=T) + plot.df <- rbindlist(list(x.true, z.true, x.naive,z.naive,x.amelia.full,z.amelia.full,x.mle, z.mle, x.zhang, z.zhang, x.feasible, z.feasible),use.names=T) plot.df[,accuracy := accuracy] @@ -216,18 +84,38 @@ build_plot_dataset <- function(df){ return(plot.df) } +change.remember.file(args$remember_file, clear=TRUE) +sims.df <- read_feather(args$infile) +sims.df[,Bzx:=NA] +sims.df[,y_explained_variance:=NA] +sims.df[,accuracy_imbalance_difference:=NA] +plot.df <- build_plot_dataset(sims.df) -df <- read_feather(args$infile) -plot.df <- build_plot_dataset(df) remember(plot.df,args$name) +set.remember.prefix(gsub("plot.df.","",args$name)) + +remember(median(sims.df$cor.xz),'med.cor.xz') +remember(median(sims.df$accuracy),'med.accuracy') +remember(median(sims.df$error.cor.x),'med.error.cor.x') +remember(median(sims.df$error.cor.z),'med.error.cor.z') +remember(median(sims.df$lik.ratio),'med.lik.ratio') -## df[gmm.ER_pval<0.05] +## df[gmm.ER_pval<0.05] +## 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), +## N=factor(N), +## m=factor(m))] + +## plot.df.test <- plot.df.test[(variable=='z') & (m != 1000) & (m!=500) & !is.na(p.true.in.ci) & (method!="Multiple imputation (Classifier features unobserved)")] +## p <- ggplot(plot.df.test, aes(y=mean.est, ymax=mean.est + var.est/2, ymin=mean.est-var.est/2, x=method)) +## p <- p + geom_hline(aes(yintercept=-0.05),linetype=2) +## p <- p + geom_pointrange() + facet_grid(m~N,as.table=F) + scale_x_discrete(labels=label_wrap_gen(4)) +## print(p) ## 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")