require(ggplot2)
require(lol)
require(reshape2)
require(Rmisc)
require(randomForest)
require(gridExtra)
require(latex2exp)
g_legend<-function(a.gplot){
tmp <- ggplot_gtable(ggplot_build(a.gplot))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend <- tmp$grobs[[leg]]
return(legend)}
performance <- readRDS('simulations.rds')
plot_example <- function(data, sim) {
p1 <- ggplot(data[data$classification=='lda',], aes(x=dimensions, y=lhat, color=algorithm, group=algorithm)) +
stat_summary(geom="line", fun.y="mean", size=2) +
xlab("Dimensions") +
ylab(TeX("$\\hat{L}$")) +
ggtitle(paste(sim, "Simulation, LDA Classifier")) +
scale_color_discrete(name="Algorithm") +
theme_bw()
p2 <- ggplot(data[data$classification=='rf',], aes(x=dimensions, y=lhat, color=algorithm, group=algorithm)) +
stat_summary(geom="line", fun.y="mean", size=2) +
xlab("Dimensions") +
ylab(TeX("$\\hat{L}$")) +
ggtitle(paste(sim, "Simulation, RF Classifier")) +
theme_bw() +
scale_color_discrete(name="Algorithm")
my_legend <- g_legend(p1)
p3 <- grid.arrange(arrangeGrob(p1 + theme(legend.position=NaN), p2 + theme(legend.position=NaN), nrow=2), my_legend, nrow=1, widths=c(.88, .12))
}
for (sim in unique(performance$simulation)) {
subset <- performance[performance$simulation == sim,]
plotlist <- list()
undims <- unique(subset$dimensions)
plot_example(subset, sim)
}
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