add missing simulation code

[ml_measurement_error_public.git] / simulations / plot_example_2.R

diff --git a/simulations/plot_example_2.R b/simulations/plot_example_2.R
new file mode 100644 (file)
index 0000000..d5ca2b6
--- /dev/null
+++ b/simulations/plot_example_2.R
@@ -0,0 +1,102 @@
+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(option='C') + theme_minimal() + xlab("Number of gold standard labels") + ylab("Total sample size") 
+
+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")