176 lines
11 KiB
R
176 lines
11 KiB
R
source("RemembR/R/RemembeR.R")
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library(arrow)
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library(data.table)
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library(ggplot2)
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df <- data.table(read_feather("example_1.feather"))
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x.naive <- df[,.(N, m, Bxy, Bxy.est.naive, Bxy.ci.lower.naive, Bxy.ci.upper.naive)]
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x.naive <- x.naive[,':='(true.in.ci = as.integer((Bxy >= Bxy.ci.lower.naive) & (Bxy <= Bxy.ci.upper.naive)),
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zero.in.ci = (0 >= Bxy.ci.lower.naive) & (0 <= Bxy.ci.upper.naive),
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bias = Bxy - Bxy.est.naive,
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sign.correct = as.integer(sign(Bxy) == sign(Bxy.est.naive)))]
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x.naive.plot <- x.naive[,.(p.true.in.ci = mean(true.in.ci),
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mean.bias = mean(bias),
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p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
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mean.estimate=mean(Bxy.est.naive),
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var.estimate=var(Bxy.est.naive),
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Bxy=mean(Bxy),
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variable='x',
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method='Naive'
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),
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by=c('N','m')]
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x.amelia.full <- df[,.(N, m, Bxy, Bxy.est.true, Bxy.ci.lower.amelia.full, Bxy.ci.upper.amelia.full, Bxy.est.amelia.full)]
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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),
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zero.in.ci = (0 >= Bxy.ci.lower.amelia.full) & (0 <= Bxy.ci.upper.amelia.full),
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bias = Bxy.est.true - Bxy.est.amelia.full,
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sign.correct = sign(Bxy.est.true) == sign(Bxy.est.amelia.full))]
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x.amelia.full.plot <- x.amelia.full[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
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mean.bias = mean(bias),
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p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
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variable='x',
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method='Multiple imputation'
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),
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by=c('N','m')]
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g.amelia.full <- df[,.(N, m, Bgy.est.true, Bgy.est.amelia.full, Bgy.ci.lower.amelia.full, Bgy.ci.upper.amelia.full)]
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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),
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zero.in.ci = (0 >= Bgy.ci.lower.amelia.full) & (0 <= Bgy.ci.upper.amelia.full),
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bias = Bgy.est.amelia.full - Bgy.est.true,
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sign.correct = sign(Bgy.est.true) == sign(Bgy.est.amelia.full))]
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g.amelia.full.plot <- g.amelia.full[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
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mean.bias = mean(bias),
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p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
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variable='g',
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method='Multiple imputation'
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),
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by=c('N','m')]
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x.amelia.nok <- df[,.(N, m, Bxy.est.true, Bxy.est.amelia.nok, Bxy.ci.lower.amelia.nok, Bxy.ci.upper.amelia.nok)]
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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),
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zero.in.ci = (0 >= Bxy.ci.lower.amelia.nok) & (0 <= Bxy.ci.upper.amelia.nok),
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bias = Bxy.est.amelia.nok - Bxy.est.true,
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sign.correct = sign(Bxy.est.true) == sign(Bxy.est.amelia.nok))]
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x.amelia.nok.plot <- x.amelia.nok[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
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mean.bias = mean(bias),
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p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
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variable='x',
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method='Multiple imputation (Classifier features unobserved)'
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),
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by=c('N','m')]
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g.amelia.nok <- df[,.(N, m, Bgy.est.true, Bgy.est.amelia.nok, Bgy.ci.lower.amelia.nok, Bgy.ci.upper.amelia.nok)]
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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),
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zero.in.ci = (0 >= Bgy.ci.lower.amelia.nok) & (0 <= Bgy.ci.upper.amelia.nok),
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bias = Bgy.est.amelia.nok - Bgy.est.true,
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sign.correct = sign(Bgy.est.true) == sign(Bgy.est.amelia.nok))]
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g.amelia.nok.plot <- g.amelia.nok[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
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mean.bias = mean(bias),
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p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
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variable='g',
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method='Multiple imputation (Classifier features unobserved)'
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),
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by=c('N','m')]
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x.mecor <- df[,.(N,m,Bxy.est.true, Bxy.est.mecor,Bxy.lower.mecor, Bxy.upper.mecor)]
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x.mecor <- x.mecor[,':='(true.in.ci = (Bxy.est.true >= Bxy.lower.mecor) & (Bxy.est.true <= Bxy.upper.mecor),
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zero.in.ci = (0 >= Bxy.lower.mecor) & (0 <= Bxy.upper.mecor),
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bias = Bxy.est.mecor - Bxy.est.true,
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sign.correct = sign(Bxy.est.true) == sign(Bxy.est.mecor))]
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x.mecor.plot <- x.mecor[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
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mean.bias = mean(bias),
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p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
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variable='x',
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method='Regression Calibration'
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),
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by=c('N','m')]
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g.mecor <- df[,.(N,m,Bgy.est.true, Bgy.est.mecor,Bgy.lower.mecor, Bgy.upper.mecor)]
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g.mecor <- g.mecor[,':='(true.in.ci = (Bgy.est.true >= Bgy.lower.mecor) & (Bgy.est.true <= Bgy.upper.mecor),
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zero.in.ci = (0 >= Bgy.lower.mecor) & (0 <= Bgy.upper.mecor),
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bias = Bgy.est.mecor - Bgy.est.true,
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sign.correct = sign(Bgy.est.true) == sign(Bgy.est.mecor))]
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g.mecor.plot <- g.mecor[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
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mean.bias = mean(bias),
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p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
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variable='g',
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method='Regression Calibration'
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),
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by=c('N','m')]
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## x.mecor <- df[,.(N,m,Bgy.est.true, Bgy.est.mecor,Bgy.ci.lower.mecor, Bgy.ci.upper.mecor)]
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## x.mecor <- x.mecor[,':='(true.in.ci = (Bgy.est.true >= Bgy.ci.lower.mecor) & (Bgy.est.true <= Bgy.ci.upper.mecor),
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## zero.in.ci = (0 >= Bgy.ci.lower.mecor) & (0 <= Bgy.ci.upper.mecor),
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## bias = Bgy.est.mecor - Bgy.est.true,
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## sign.correct = sign(Bgy.est.true) == sign(Bgy.est.mecor))]
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## x.mecor.plot <- x.mecor[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
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## mean.bias = mean(bias),
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## p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
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## variable='g',
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## method='Regression Calibration'
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## ),
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## by=c('N','m')]
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x.gmm <- df[,.(N,m,Bxy.est.true, Bxy.est.gmm,Bxy.ci.lower.gmm, Bxy.ci.upper.gmm)]
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x.gmm <- x.gmm[,':='(true.in.ci = (Bxy.est.true >= Bxy.ci.lower.gmm) & (Bxy.est.true <= Bxy.ci.upper.gmm),
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zero.in.ci = (0 >= Bxy.ci.lower.gmm) & (0 <= Bxy.ci.upper.gmm),
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bias = Bxy.est.gmm - Bxy.est.true,
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sign.correct = sign(Bxy.est.true) == sign(Bxy.est.gmm))]
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x.gmm.plot <- x.gmm[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
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mean.bias = mean(bias),
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p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
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variable='x',
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method='2SLS+gmm'
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),
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by=c('N','m')]
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g.gmm <- df[,.(N,m,Bgy.est.true, Bgy.est.gmm,Bgy.ci.lower.gmm, Bgy.ci.upper.gmm)]
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g.gmm <- g.gmm[,':='(true.in.ci = (Bgy.est.true >= Bgy.ci.lower.gmm) & (Bgy.est.true <= Bgy.ci.upper.gmm),
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zero.in.ci = (0 >= Bgy.ci.lower.gmm) & (0 <= Bgy.ci.upper.gmm),
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bias = Bgy.est.gmm - Bgy.est.true,
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sign.correct = sign(Bgy.est.true) == sign(Bgy.est.gmm))]
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g.gmm.plot <- g.gmm[,.(p.true.in.ci = mean(as.integer(true.in.ci)),
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mean.bias = mean(bias),
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p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
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variable='g',
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method='2SLS+gmm'
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),
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by=c('N','m')]
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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))
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remember(plot.df,'example.1.plot.df')
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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")
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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")
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ests <- df[,.(Bxy.est.true = mean(Bxy.est.true),
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Bxy.est.naive = mean(Bxy.est.naive),
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Bxy.est.feasible = mean(Bxy.est.feasible),
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Bxy.est.amelia.full = mean(Bxy.est.amelia.full),
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Bxy.est.amelia.nok = mean(Bxy.est.amelia.nok),
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Bxy.est.mecor = mean(Bxy.est.mecor),
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Bxy.est.gmm = mean(Bxy.est.gmm)),
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by=c("N","m")]
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