+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")]
projects / ml_measurement_error_public.git / blobdiff