parser <- add_argument(parser, "--name", default="", help="The name to safe the data to in the remember file.")
args <- parse_args(parser)
-build_plot_dataset <- function(df){
- 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,
- Bxy.est.naive = 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),
- mean.est = mean(Bxy.est.naive),
- var.est = var(Bxy.est.naive),
- N.sims = .N,
- p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
- variable='x',
- method='Naive'
- ),
- by=c('N','m')]
+summarize.estimator <- function(df, suffix='naive', coefname='x'){
+
+ part <- df[,c('N',
+ 'm',
+ 'Bxy',
+ paste0('B',coefname,'y.est.',suffix),
+ paste0('B',coefname,'y.ci.lower.',suffix),
+ paste0('B',coefname,'y.ci.upper.',suffix),
+ 'y_explained_variance',
+ 'Bzx',
+ 'Bzy',
+ 'accuracy_imbalance_difference'
+ ),
+ with=FALSE]
+
+ true.in.ci <- as.integer((part$Bxy >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (part$Bxy <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]]))
+ zero.in.ci <- as.integer(0 >= part[[paste0('B',coefname,'y.ci.lower.',suffix)]]) & (0 <= part[[paste0('B',coefname,'y.ci.upper.',suffix)]])
+ bias <- part$Bxy - part[[paste0('B',coefname,'y.est.',suffix)]]
+ sign.correct <- as.integer(sign(part$Bxy) == sign(part[[paste0('B',coefname,'y.est.',suffix)]]))
+
+ part <- part[,':='(true.in.ci = true.in.ci,
+ zero.in.ci = zero.in.ci,
+ bias=bias,
+ sign.correct =sign.correct)]
+
+ part.plot <- part[, .(p.true.in.ci = mean(true.in.ci),
+ mean.bias = mean(bias),
+ mean.est = mean(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
+ var.est = var(.SD[[paste0('B',coefname,'y.est.',suffix)]]),
+ est.upper.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.95,na.rm=T),
+ est.lower.95 = quantile(.SD[[paste0('B',coefname,'y.est.',suffix)]],0.05,na.rm=T),
+ N.sims = .N,
+ p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
+ variable=coefname,
+ method=suffix
+ ),
+ by=c("N","m",'y_explained_variance','Bzx', 'Bzy', 'accuracy_imbalance_difference')
+ ]
+ return(part.plot)
+}
- 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,
- Bgy.est.naive = 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),
- mean.est = mean(Bgy.est.naive),
- var.est = var(Bgy.est.naive),
- N.sims = .N,
- p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
- variable='g',
- method='Naive'
- ),
- by=c('N','m')]
+build_plot_dataset <- function(df){
+ x.true <- summarize.estimator(df, 'true','x')
+
+ z.true <- summarize.estimator(df, 'true','z')
- x.feasible <- df[,.(N, m, Bxy, Bxy.est.feasible, Bxy.ci.lower.feasible, Bxy.ci.upper.feasible)]
- x.feasible <- x.feasible[,':='(true.in.ci = as.integer((Bxy >= Bxy.ci.lower.feasible) & (Bxy <= Bxy.ci.upper.feasible)),
- zero.in.ci = (0 >= Bxy.ci.lower.feasible) & (0 <= Bxy.ci.upper.feasible),
- bias = Bxy - Bxy.est.feasible,
- Bxy.est.feasible = Bxy.est.feasible,
- sign.correct = as.integer(sign(Bxy) == sign(Bxy.est.feasible)))]
-
- x.feasible.plot <- x.feasible[,.(p.true.in.ci = mean(true.in.ci),
- mean.bias = mean(bias),
- mean.est = mean(Bxy.est.feasible),
- var.est = var(Bxy.est.feasible),
- N.sims = .N,
- p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
- variable='x',
- method='Feasible'
- ),
- by=c('N','m')]
+ x.naive <- summarize.estimator(df, 'naive','x')
+ z.naive <- summarize.estimator(df,'naive','z')
- g.feasible <- df[,.(N, m, Bgy, Bgy.est.feasible, Bgy.ci.lower.feasible, Bgy.ci.upper.feasible)]
- g.feasible <- g.feasible[,':='(true.in.ci = as.integer((Bgy >= Bgy.ci.lower.feasible) & (Bgy <= Bgy.ci.upper.feasible)),
- zero.in.ci = (0 >= Bgy.ci.lower.feasible) & (0 <= Bgy.ci.upper.feasible),
- bias = Bgy - Bgy.est.feasible,
- Bgy.est.feasible = Bgy.est.feasible,
- sign.correct = as.integer(sign(Bgy) == sign(Bgy.est.feasible)))]
-
- g.feasible.plot <- g.feasible[,.(p.true.in.ci = mean(true.in.ci),
- mean.bias = mean(bias),
- mean.est = mean(Bgy.est.feasible),
- var.est = var(Bgy.est.feasible),
- N.sims = .N,
- p.sign.correct = mean(as.integer(sign.correct & (! zero.in.ci))),
- variable='g',
- method='Feasible'
- ),
- by=c('N','m')]
+ x.feasible <- summarize.estimator(df, 'feasible', 'x')
+
+ z.feasible <- summarize.estimator(df, 'feasible', 'z')
+
+ x.amelia.full <- summarize.estimator(df, 'amelia.full', 'x')
+
+ z.amelia.full <- summarize.estimator(df, 'amelia.full', 'z')
+ x.mecor <- summarize.estimator(df, 'mecor', 'x')
+ z.mecor <- summarize.estimator(df, 'mecor', 'z')
- 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))),
- mean.est = mean(Bxy.est.amelia.full),
- var.est = var(Bxy.est.amelia.full),
- N.sims = .N,
- 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))),
- mean.est = mean(Bgy.est.amelia.full),
- var.est = var(Bgy.est.amelia.full),
- N.sims = .N,
- 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))),
- ## mean.est = mean(Bxy.est.amelia.nok),
- ## var.est = var(Bxy.est.amelia.nok),
- ## N.sims = .N,
- ## 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))),
- ## mean.est = mean(Bgy.est.amelia.nok),
- ## var.est = var(Bgy.est.amelia.nok),
- ## N.sims = .N,
- ## 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))),
- mean.est = mean(Bxy.est.mecor),
- var.est = var(Bxy.est.mecor),
- N.sims = .N,
- 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))),
- mean.est = mean(Bgy.est.mecor),
- var.est = var(Bgy.est.mecor),
- N.sims = .N,
- 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))),
- mean.est = mean(Bxy.est.gmm),
-
- var.est = var(Bxy.est.gmm),
- N.sims = .N,
- 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))),
- mean.est = mean(Bgy.est.gmm),
- var.est = var(Bgy.est.gmm),
- N.sims = .N,
- variable='g',
- method='2SLS+gmm'
- ),
- by=c('N','m')]
+ x.mecor <- summarize.estimator(df, 'mecor', 'x')
- accuracy <- df[,mean(accuracy)]
+ z.mecor <- summarize.estimator(df, 'mecor', 'z')
- plot.df <- rbindlist(list(x.naive.plot,g.naive.plot,x.amelia.full.plot,g.amelia.full.plot,x.mecor.plot, g.mecor.plot, x.gmm.plot, g.gmm.plot, x.feasible.plot, g.feasible.plot),use.names=T)
+ x.mle <- summarize.estimator(df, 'mle', 'x')
- plot.df[,accuracy := accuracy]
+ z.mle <- summarize.estimator(df, 'mle', 'z')
+
+ x.zhang <- summarize.estimator(df, 'zhang', 'x')
- plot.df <- plot.df[,":="(sd.est=sqrt(var.est)/N.sims)]
+ z.zhang <- summarize.estimator(df, 'zhang', 'z')
+
+ x.gmm <- summarize.estimator(df, 'gmm', 'x')
+ z.gmm <- summarize.estimator(df, 'gmm', 'z')
+
+ accuracy <- df[,mean(accuracy)]
+ plot.df <- rbindlist(list(x.true,z.true,x.naive,z.naive,x.amelia.full,z.amelia.full,x.mecor, z.mecor, x.gmm, z.gmm, x.feasible, z.feasible,z.mle, x.mle, x.zhang, z.zhang, x.gmm, z.gmm),use.names=T)
+ plot.df[,accuracy := accuracy]
+ plot.df <- plot.df[,":="(sd.est=sqrt(var.est)/N.sims)]
return(plot.df)
}
-df <- read_feather(args$infile)
-plot.df <- build_plot_dataset(df)
+plot.df <- read_feather(args$infile)
+
+# df <- df[apply(df,1,function(x) !any(is.na(x)))]
+
+if(!('Bzx' %in% names(plot.df)))
+ plot.df[,Bzx:=NA]
+
+if(!('accuracy_imbalance_difference' %in% names(plot.df)))
+ plot.df[,accuracy_imbalance_difference:=NA]
+
+unique(plot.df[,'accuracy_imbalance_difference'])
+
+#plot.df <- build_plot_dataset(df[accuracy_imbalance_difference==0.1][N==700])
+plot.df <- build_plot_dataset(plot.df)
+
remember(plot.df,args$name)
+#ggplot(df,aes(x=Bxy.est.mle)) + geom_histogram() + facet_grid(accuracy_imbalance_difference ~ Bzy)
+
+## ## ## df[gmm.ER_pval<0.05]
+
+## plot.df.test <- plot.df[,':='(method=factor(method,levels=c("Naive","Multiple imputation", "Multiple imputation (Classifier features unobserved)","Regression Calibration","2SLS+gmm","Bespoke MLE", "Feasible"),ordered=T),
+## N=factor(N),
+## m=factor(m))]
+
+## plot.df.test <- plot.df.test[(variable=='x') & (method!="Multiple imputation (Classifier features unobserved)")]
+## p <- ggplot(plot.df.test, aes(y=mean.est, ymax=mean.est + var.est/2, ymin=mean.est-var.est/2, x=method))
+## p <- p + geom_hline(data=plot.df.test, mapping=aes(yintercept=0.1),linetype=2)
+
+## p <- p + geom_pointrange() + facet_grid(N~m,as.table=F,scales='free') + scale_x_discrete(labels=label_wrap_gen(4))
+## print(p)
+
+## plot.df.test <- plot.df[,':='(method=factor(method,levels=c("Naive","Multiple imputation", "Multiple imputation (Classifier features unobserved)","Regression Calibration","2SLS+gmm","Bespoke MLE", "Feasible"),ordered=T),
+## N=factor(N),
+## m=factor(m))]
+
+## plot.df.test <- plot.df.test[(variable=='z') & (method!="Multiple imputation (Classifier features unobserved)")]
+## p <- ggplot(plot.df.test, aes(y=mean.est, ymax=mean.est + var.est/2, ymin=mean.est-var.est/2, x=method))
+## p <- p + geom_hline(data=plot.df.test, mapping=aes(yintercept=-0.1),linetype=2)
+
+## p <- p + geom_pointrange() + facet_grid(m~N,as.table=F,scales='free') + scale_x_discrete(labels=label_wrap_gen(4))
+## print(p)
+
+
+## x.mle <- df[,.(N,m,Bxy.est.mle,Bxy.ci.lower.mle, Bxy.ci.upper.mle, y_explained_variance, Bzx, Bzy, accuracy_imbalance_difference)]
+## x.mle.plot <- x.mle[,.(mean.est = mean(Bxy.est.mle),
+## var.est = var(Bxy.est.mle),
+## N.sims = .N,
+## variable='z',
+## method='Bespoke MLE'
+## ),
+## by=c("N","m",'y_explained_variance', 'Bzx', 'Bzy','accuracy_imbalance_difference')]
+
+## z.mle <- df[,.(N,m,Bzy.est.mle,Bzy.ci.lower.mle, Bzy.ci.upper.mle, y_explained_variance, Bzx, Bzy, accuracy_imbalance_difference)]
-## df[gmm.ER_pval<0.05]
+## z.mle.plot <- z.mle[,.(mean.est = mean(Bzy.est.mle),
+## var.est = var(Bzy.est.mle),
+## N.sims = .N,
+## variable='z',
+## method='Bespoke MLE'
+## ),
+## by=c("N","m",'y_explained_variance','Bzx')]
+## plot.df <- z.mle.plot
+## plot.df.test <- plot.df[,':='(method=factor(method,levels=c("Naive","Multiple imputation", "Multiple imputation (Classifier features unobserved)","Regression Calibration","2SLS+gmm","Bespoke MLE", "Feasible"),ordered=T),
+## N=factor(N),
+## m=factor(m))]
+## plot.df.test <- plot.df.test[(variable=='z') & (m != 1000) & (m!=500) & (method!="Multiple imputation (Classifier features unobserved)")]
+## p <- ggplot(plot.df.test, aes(y=mean.est, ymax=mean.est + var.est/2, ymin=mean.est-var.est/2, x=method))
+## p <- p + geom_hline(aes(yintercept=0.2),linetype=2)
+## p <- p + geom_pointrange() + facet_grid(m~Bzx, Bzy,as.table=F) + scale_x_discrete(labels=label_wrap_gen(4))
+## print(p)
-## ggplot(plot.df[variable=='x'], aes(y=mean.est, ymax=mean.est + var.est/2, ymin=mean.est-var.est/2, x=method)) + geom_pointrange() + facet_grid(-m~N) + scale_x_discrete(labels=label_wrap_gen(10))
+## ## ggplot(plot.df[variable=='x'], aes(y=mean.est, ymax=mean.est + var.est/2, ymin=mean.est-var.est/2, x=method)) + geom_pointrange() + facet_grid(-m~N) + scale_x_discrete(labels=label_wrap_gen(10))
-## 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=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")
+## ## 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")
projects / ml_measurement_error_public.git / blobdiff