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Version 2.3.0 (2006-04-24)
ISBN 3-900051-07-0

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> # functions related to this project
> source("cfneval_lib.R")
> 
> # read the data
> full_data <- read.rdb('example.rdb')
> 
> # the names of the cost functions that we want to see
> cfn_names <- c("bystroff", "pred_bys_mean", "pred_pb_mean")
> 
> # (or just get all the cost functions in the file; note that there
> #  are only 8 different color-blind safe plotting colors)
> #
> #   cfn_names <- colnames(full_data)[-1]
> 
> # select the cost functions we are interested in
> data <- full_data[cfn_names]
> 
> # evaluate some summary statistics
> mean(data)
     bystroff pred_bys_mean  pred_pb_mean 
    0.1458256     0.3738023     0.4091512 
> sd(data)
     bystroff pred_bys_mean  pred_pb_mean 
    0.1331700     0.1068046     0.1147102 
> 
> # the five number summary is:
> #    minimum, lower-hinge, median, upper-hinge, maximum
> lapply(data, fivenum)
$bystroff
[1] -0.105  0.049  0.135  0.217  0.467

$pred_bys_mean
[1] 0.1210 0.2870 0.3725 0.4430 0.6320

$pred_pb_mean
[1] 0.0930 0.3230 0.3945 0.4970 0.6340

> 
> # create a notched boxplot
> eps_output("plots/boxplot.eps")
> boxplot(data, main="Cost Function Comparison", ylab=expression(tau), notch=TRUE)
> graphics.off()
> 
> # display a kernel density regression
> eps_output("plots/densityreg.eps")
> denplot(data, main="Cost Function Comparison", xlab=expression(tau),
+ 	ylim=c(0,3.6), ylab="Density", col=cb_colors, lwd=2)
> legend("topleft", legend=cfn_names, fill=cb_colors)
> graphics.off()
> 
> # display a histogram
> eps_output("plots/histogram.eps")
> pmulthist(data, main="Cost Function Comparison", xlab=expression(tau),
+ 	  ylab="Density", col=cb_colors[1:length(cfn_names)],
+ 	  breaks=5, yscale=1.1)
> legend("topright", legend=cfn_names, fill=cb_colors)
> graphics.off()
>