X-Git-Url: https://code.communitydata.science/ml_measurement_error_public.git/blobdiff_plain/6057688060b5bf2a94f2b96b65b275a91991c0f3..e41d11afb9a80180feff844666e3ee463d20a7cd:/simulations/plot_example_1.R diff --git a/simulations/plot_example_1.R b/simulations/plot_example_1.R new file mode 100644 index 0000000..d883d24 --- /dev/null +++ b/simulations/plot_example_1.R @@ -0,0 +1,175 @@ +source("RemembR/R/RemembeR.R") +library(arrow) +library(data.table) +library(ggplot2) + +df <- data.table(read_feather("example_1.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))), + mean.estimate=mean(Bxy.est.naive), + var.estimate=var(Bxy.est.naive), + Bxy=mean(Bxy), + variable='x', + method='Naive' + ), + 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))), + 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')] + + +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))), + 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))), + 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))), + 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))), + variable='g', + method='2SLS+gmm' + ), + 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, x.mecor.plot, g.mecor.plot, x.gmm.plot, g.gmm.plot)) + +remember(plot.df,'example.1.plot.df') + + +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") + +ests <- df[,.(Bxy.est.true = mean(Bxy.est.true), + Bxy.est.naive = mean(Bxy.est.naive), + Bxy.est.feasible = mean(Bxy.est.feasible), + Bxy.est.amelia.full = mean(Bxy.est.amelia.full), + Bxy.est.amelia.nok = mean(Bxy.est.amelia.nok), + Bxy.est.mecor = mean(Bxy.est.mecor), + Bxy.est.gmm = mean(Bxy.est.gmm)), + by=c("N","m")]