groceryheist/ml_measurement_error_overleaf
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improve axis labels in robustness checks.

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Nathan TeBlunthuis
2023-03-07 15:09:38 -08:00
parent dd34a63ef6
commit 465e4f7e85
1 changed files with 8 additions and 8 deletions
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16
resources/robustness_check_plots.R
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@@ -88,7 +88,7 @@ plot.robustness.1.dv <- function(iv='z'){
grid.draw(p) grid.draw(p)
} }
plot.robustness.2.iv <- function(iv, n.annotations=100, n.classifications=5000){ plot.robustness.2.iv <- function(iv, n.annotations=200, n.classifications=5000){
r <- readRDS("robustness_2.RDS") r <- readRDS("robustness_2.RDS")
robust_df <- data.table(r[['robustness_2']]) robust_df <- data.table(r[['robustness_2']])
@@ -101,7 +101,7 @@ plot.robustness.2.iv <- function(iv, n.annotations=100, n.classifications=5000){
p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLA", "PL", "Feasible")) p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLA", "PL", "Feasible"))
p <- p + facet_wrap(prediction_accuracy~., ncol=4,as.table=F) p <- p + facet_wrap(prediction_accuracy~., ncol=4,as.table=F)
p <- p + scale_x_discrete(labels=label_wrap_gen(14)) + ylab("Estimate") + xlab("Method") + coord_flip() p <- p + scale_x_discrete(labels=label_wrap_gen(14)) + ylab(bquote('Estimate of B'[.(toupper(iv))])) + xlab("Method") + coord_flip()
p <- arrangeGrob(p, p <- arrangeGrob(p,
@@ -115,7 +115,7 @@ robust_2_df <- data.table(robust2[['robustness_2_dv']])
robust_2_min_acc <- min(robust_2_df[,prediction_accuracy]) robust_2_min_acc <- min(robust_2_df[,prediction_accuracy])
robust_2_max_acc <- max(robust_2_df[,prediction_accuracy]) robust_2_max_acc <- max(robust_2_df[,prediction_accuracy])
plot.robustness.2.dv <- function(iv, n.annotations=100, n.classifications=5000){ plot.robustness.2.dv <- function(iv, n.annotations=200, n.classifications=5000){
r <- readRDS("robustness_2_dv.RDS") r <- readRDS("robustness_2_dv.RDS")
robust_df <- data.table(r[['robustness_2_dv']]) robust_df <- data.table(r[['robustness_2_dv']])
@@ -128,7 +128,7 @@ plot.robustness.2.dv <- function(iv, n.annotations=100, n.classifications=5000){
p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLA", "PL", "Feasible")) p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLA", "PL", "Feasible"))
p <- p + facet_wrap(prediction_accuracy~., ncol=4,as.table=F) p <- p + facet_wrap(prediction_accuracy~., ncol=4,as.table=F)
p <- p + scale_x_discrete(labels=label_wrap_gen(14)) + ylab("Estimate") + xlab("Method") + coord_flip() p <- p + scale_x_discrete(labels=label_wrap_gen(14)) + ylab(bquote('Estimate of B'[.(toupper(iv))])) + xlab("Method") + coord_flip()
p <- arrangeGrob(p, p <- arrangeGrob(p,
top=grid.text("Varying Accuracy of the AC",x=0.42,just='right')) top=grid.text("Varying Accuracy of the AC",x=0.42,just='right'))
@@ -153,7 +153,7 @@ plot.robustness.3.iv <- function(iv, n.annotations=200, n.classifications=5000){
p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM","MLA", "PL", "Feasible")) p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM","MLA", "PL", "Feasible"))
p <- p + facet_wrap(Px~., ncol=3,as.table=F) p <- p + facet_wrap(Px~., ncol=3,as.table=F)
p <- p + scale_x_discrete(labels=label_wrap_gen(14)) + ylab("Estimate") + xlab("Method") + coord_flip() p <- p + scale_x_discrete(labels=label_wrap_gen(14)) + ylab(bquote('Estimate of B'[.(toupper(iv))])) + xlab("Method") + coord_flip()
p <- arrangeGrob(p, p <- arrangeGrob(p,
top=grid.text("Imbalance in X",x=0.32,just='right')) top=grid.text("Imbalance in X",x=0.32,just='right'))
@@ -175,7 +175,7 @@ plot.robustness.3.dv <- function(iv, n.annotations=100, n.classifications=1000){
robust_df <- robust_df[(method != "PL")] robust_df <- robust_df[(method != "PL")]
p <- p + facet_wrap(Py~., ncol=3,as.table=F,scales='free') p <- p + facet_wrap(Py~., ncol=3,as.table=F,scales='free')
p <- p + scale_x_discrete(labels=label_wrap_gen(14)) + ylab("Estimate") + xlab("Method") + coord_flip() p <- p + scale_x_discrete(labels=label_wrap_gen(14)) + ylab(bquote('Estimate of B'[.(toupper(iv))])) + xlab("Method") + coord_flip()
p <- arrangeGrob(p, p <- arrangeGrob(p,
top=grid.text("Imbalance in Y",x=0.32,just='right')) top=grid.text("Imbalance in Y",x=0.32,just='right'))
@@ -199,7 +199,7 @@ plot.robustness.4.iv <- function(iv, n.annotations=200, n.classifications=5000){
p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLA", "PL", "Feasible")) p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLA", "PL", "Feasible"))
p <- p + facet_wrap(y_bias~., ncol=3,as.table=T) p <- p + facet_wrap(y_bias~., ncol=3,as.table=T)
p <- p + scale_x_discrete(labels=label_wrap_gen(14)) + ylab("Estimate") + xlab("Method") + coord_flip() p <- p + scale_x_discrete(labels=label_wrap_gen(14)) + ylab(bquote('Estimate of B'[.(toupper(iv))])) + xlab("Method") + coord_flip()
p <- arrangeGrob(p, p <- arrangeGrob(p,
top=grid.text("Varying Degree of Misclassification in X",x=0.52,just='right')) top=grid.text("Varying Degree of Misclassification in X",x=0.52,just='right'))
@@ -224,7 +224,7 @@ plot.robustness.4.dv <- function(iv, n.annotations=100, n.classifications=1000){
robust_df <- robust_df[Bzx==1] robust_df <- robust_df[Bzx==1]
p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLA", "PL", "Feasible")) p <- .plot.simulation(robust_df, iv=iv, levels=c("True","Naïve","MI", "GMM", "MLA", "PL", "Feasible"))
p <- p + facet_wrap(z_bias~., ncol=3,as.table=F) p <- p + facet_wrap(z_bias~., ncol=3,as.table=F)
p <- p + scale_x_discrete(labels=label_wrap_gen(14)) + ylab("Estimate") + xlab("Method") + coord_flip() p <- p + scale_x_discrete(labels=label_wrap_gen(14)) + ylab(bquote('Estimate of B'[.(toupper(iv))])) + xlab("Method") + coord_flip()
p <- arrangeGrob(p, p <- arrangeGrob(p,
top=grid.text("Varying Degree of Misclassification in Y",x=0.52,just='right')) top=grid.text("Varying Degree of Misclassification in Y",x=0.52,just='right'))