An intuitive approach to ROC curves

roc demo.r

# R Code to support: http://econometricsense.blogspot.com/2011/05/intuitive-approach-to-roc-curves-with.html

# *------------------------------------------------------------------
# |                
# | import scored logit data from SAS - code generated by SAS MACRO %EXPORT_TO_R
# |  
# |  
# *-----------------------------------------------------------------
 
 
#  set R working directory
setwd("C:\\Documents and Settings\\wkuuser\\Desktop\\PROJECTS\\Stats Training")
 
#  get data
dat.from.SAS <- read.csv("fromSAS_delete.CSV", header=T)
 
#  check data dimensions
dim(dat.from.SAS)
names(dat.from.SAS)
 
 
# *------------------------------------------------------------------
# |                
# |  scatter plot with marginal histograms
# |  
# |  
# *-----------------------------------------------------------------
 
#
# model predicts P(G) so we want these probabilities for each group
#
 
 
# get p(G) data set for the group that is actually green
 
green <- dat.from.SAS[ dat.from.SAS$class=="G",]
dim(green)
 
# get p(G) data set for group that is actually red
 
red <- dat.from.SAS[ dat.from.SAS$class=="R",]
dim(red)
 
# just look at regular histograms for each group
 
 
hist(green$P_G, main = 'histogram for green')
hist(red$P_G, main = 'histogram for red')
 
# in order to do scatter plots n must be the same for each 
# group, randomly sample n = n(green) from red
 
 
# Total number of red observations to match green
N <- 24 
print(N)
 
 
# Randomly arrange the data and select out N size sample for red
# and test set.
 
dat <- red[sample(1:N),]
red.rs <- dat[1:N,]
dim(red.rs)
 
# does the distribution retain original properties? Yes
hist(red.rs$P_G, main = 'histogram for red sample')
 
 
plot(green$P_G, red.rs$P_G) 
 
 
# *------------------------------------------------------------------
# |                
# |  create the marginal plots
# |  
# |  
# *-----------------------------------------------------------------
 
 
 
def.par <- par(no.readonly = TRUE) # save default, for resetting...
 
 
# define histograms
Ghist <- hist(green$P_G,plot=FALSE)
Rhist <- hist(red.rs$P_G, plot=FALSE)
 
top <- max(c(Ghist$counts, Rhist$counts))
Grange <- c(0,1)
Rrange <- c(0,1)
nf <- layout(matrix(c(2,0,1,3),2,2,byrow=TRUE), c(3,1), c(1,3), TRUE)
 
#layout.show(nf)
 
 
par(mar=c(3,3,1,1))
plot(green$P_G, red.rs$P_G, xlim=Grange, ylim=Rrange, xlab="green", ylab="red")
 
par(mar=c(0,3,1,1))
barplot(Ghist$counts, axes=FALSE, ylim=c(0, top), space=0, main = 'green')
 
par(mar=c(3,0,1,1))
barplot(Rhist$counts, axes=FALSE, xlim=c(0, top), space=0, horiz=TRUE, main = 'red')
 
par(def.par)