Nothing
calcSegmentationPower <-
function(outList, classProbs, class, printXtable=TRUE, calcSharp=TRUE, printSharpXtable=TRUE, grLabels=paste("Group", 1:outList$Prior$H) ) {
maxProbs <- apply(classProbs, 1, max)
segPowTab <- matrix(0,outList$Prior$H+1, 6, dimnames=list(c(grLabels,"overall"), c( names( summary(maxProbs))) ))
for (h in 1:outList$Prior$H) segPowTab[h,] <- c( summary(maxProbs[class==h]))
segPowTab[outList$Prior$H+1,] <- c( summary(maxProbs))
if ( printXtable ) {
print( xtable( segPowTab, digits=4, caption=paste("Segmentation power: reported are some summary statistics for the maximum individual posterior classification probabilities",
"for all individuals within a certain cluster as well as for all individuals."), label="tab:segPower" ) )
cat("\n")
}
if ( calcSharp ) {
sharp <- apply(classProbs, 1, function(x) max(x) - sort(x, decreasing = TRUE)[2])
sharpTab <- matrix(0,outList$Prior$H+1, 6, dimnames=list(c(grLabels,"overall"), c( names( summary(sharp))) ))
for (h in 1:outList$Prior$H) sharpTab[h,] <- c( summary(sharp[class==h]))
sharpTab[outList$Prior$H+1,] <- c( summary(sharp))
if ( printXtable ) {
print( xtable( sharpTab, digits=4, caption="'Sharpness': reported are some summary statistics for the difference between highest and second highest
individual posterior classification probabilities within groups and overall",
label="tab:sharpness" ) )
cat("\n")
}
}
return( invisible( list(segPowTab=segPowTab, sharpTab=sharpTab, maxProbs=maxProbs, sharp=sharp) ) )
}
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