library(arrow) library(data.table) library(ggplot2) df <- data.table(read_feather("example_2_simulation.feather")) 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, 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), 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, 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), p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))), variable='g', method='Naive' ), by=c('N','m')] 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))), 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))), 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))), 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))), variable='g', method='Multiple imputation (Classifier features unobserved)' ), by=c('N','m')] 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)) ggplot(plot.df,aes(y=N,x=m,color=p.sign.correct)) + geom_point() + facet_grid(variable ~ method) + scale_color_viridis_c() + theme_minimal() + xlab("Number of gold standard labels") + ylab("Total sample size") ggplot(plot.df,aes(y=N,x=m,color=mean.bias)) + geom_point() + facet_grid(variable ~ method) + scale_color_viridis_c() + theme_minimal() + xlab("Number of gold standard labels") + ylab("Total sample size")