Nothing
# --------------------------------------------------------------------------------
# Plot the H and F smoothing functions for a range of smoothing parameters.
# --------------------------------------------------------------------------------
options( width=150, max.print=500 )
# gather the smoothing results.
fn1 <- function( i ) {
ifile <- sprintf("RDS/HFResults_1.%05d.rds", i )
trtR <- readRDS(ifile)
return( trtR )
}
fn2 <- function( i ) {
ifile <- sprintf("RDS/HFResults_2.%05d.rds", i )
trtR <- readRDS(ifile)
return( trtR )
}
trt1R <- lapply(1:5,fn1)
trt2R <- lapply(1:5,fn2)
trt1R <- do.call(rbind,trt1R)
trt2R <- do.call(rbind,trt2R)
print( trt1R )
print( trt2R )
colnames(trt1R) <- c("impute", "dtype", "sigmaH", "lossH", "sigmaF", "lossF")
colnames(trt2R) <- c("impute", "dtype", "sigmaH", "lossH", "sigmaF", "lossF")
ag.o <- aggregate( lossH ~ impute + dtype, trt1R, min )
print(ag.o)
Hmin1 <- merge(trt1R,ag.o,sort=FALSE)
Hmin1 <- Hmin1[,c("impute","dtype","sigmaH","lossH")]
print(Hmin1)
ag.o <- aggregate( lossF ~ impute + dtype, trt1R, min )
print(ag.o)
Fmin1 <- merge(trt1R,ag.o,sort=FALSE)
Fmin1 <- Fmin1[,c("impute","dtype","sigmaF","lossF")]
print(Fmin1)
ag.o <- aggregate( lossH ~ impute + dtype, trt2R, min )
print(ag.o)
Hmin2 <- merge(trt2R,ag.o,sort=FALSE)
Hmin2 <- Hmin2[,c("impute","dtype","sigmaH","lossH")]
print(Hmin2)
ag.o <- aggregate( lossF ~ impute + dtype, trt2R, min )
print(ag.o)
Fmin2 <- merge(trt2R,ag.o,sort=FALSE)
Fmin2 <- Fmin2[,c("impute","dtype","sigmaF","lossF")]
print(Fmin2)
cols <- c("#ffb3baFF","#ffdfbaFF","#ffffbaFF","#baffc9FF","#bae1ffFF","#edc951FF","#eb6841FF","#cc2a36FF","#4f372dFF","#00a0b0FF",
"#757676FF","#800909FF","#e0cda7FF","#2a334fFF","#ac8f57FF","#00b159FF","#005b96FF","#6497b1FF","#a32020FF","#602320FF",
"#666547FF","#ffe28aFF","#fffeb3FF","#fb2e01FF","#6fcb9fFF","#e8d174FF","#e39e54FF","#d64d4dFF","#4d7358FF","#9ed670FF",
"#a67c00FF","#ffbf00FF")
cols <- c("#415e3cFF", "#8d0808FF", "#0f50a0FF", "#007fccFF", "#3c415aFF", "#fff68fFF", "#8a2be2FF" )
cols <- c("#585858FF", "#118C4EFF", "#C1E1A6FF", "#FF9009FF", "#6DBDD6FF", "#B71427FF", "#FFE658FF" )
HFPlot <- function( Res, file, height, width, ylab, xlim, ylim, legpos, nimputes, HFmin, rc) {
pdf(file=file, height=height, width=width)
par(mar=c(4,5,0.6,0.6))
plot.new()
plot.window( xlim = xlim, ylim = ylim )
axis(1,cex.axis=1.2)
axis(2,cex.axis=1.2)
for ( i in 1:nimputes ) {
XY <- Res[ Res[,1] == i, ]
lines( x = XY[,2], y = XY[,3], lwd=1, col = cols[(i%%rc)+1], lty=c("solid"))
rug( HFmin[i,3],col=cols[(i%%rc)+1])
}
title( xlab = expression(sigma), ylab = ylab, cex.lab=1.2)
box()
dev.off()
invisible(return())
}
HFPlot(trt1R[,c(1,3,4)], "Plots/H1.pdf", 4.7, 5.0, "Loss Function (H)", c(0,33), c( 3.5, 6), c(25, 8), 5, Hmin1, rc = 17 )
HFPlot(trt1R[,c(1,5,6)], "Plots/F1.pdf", 4.7, 5.0, "Loss Function (F)", c(0,15), c( 7, 10), c(25, 11), 5, Fmin1, rc = 17 )
HFPlot(trt2R[,c(1,3,4)], "Plots/H2.pdf", 4.7, 5.0, "Loss Function (H)", c(0,33), c( 4.5, 7), c(25, 8), 5, Hmin2, rc = 17 )
HFPlot(trt2R[,c(1,5,6)], "Plots/F2.pdf", 4.7, 5.0, "Loss Function (F)", c(0,15), c( 7, 10), c(25, 11), 5, Fmin2, rc = 17 )
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