103 lines
6.1 KiB
R
103 lines
6.1 KiB
R
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_2_simulation.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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variable='x',
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method='Naive'
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),
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by=c('N','m')]
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g.naive <- df[,.(N, m, Bgy, Bgy.est.naive, Bgy.ci.lower.naive, Bgy.ci.upper.naive)]
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g.naive <- g.naive[,':='(true.in.ci = as.integer((Bgy >= Bgy.ci.lower.naive) & (Bgy <= Bgy.ci.upper.naive)),
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zero.in.ci = (0 >= Bgy.ci.lower.naive) & (0 <= Bgy.ci.upper.naive),
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bias = Bgy - Bgy.est.naive,
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sign.correct = as.integer(sign(Bgy) == sign(Bgy.est.naive)))]
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g.naive.plot <- g.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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variable='g',
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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 <- 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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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))
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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='C') + theme_minimal() + xlab("Number of gold standard labels") + ylab("Total sample size")
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kggplot(plot.df,aes(y=N,x=m,color=abs(mean.bias))) + geom_point() + facet_grid(variable ~ method) + scale_color_viridis_c(option='C') + theme_minimal() + xlab("Number of gold standard labels") + ylab("Total sample size")
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