Nothing
improvedResiduals <-
function (oldResiduals,newResiduals,testType=c("Binomial","Wilcox","tStudent","Ftest"))
{
testType <- match.arg(testType)
# initialize the variables
pwil <- 1;
pbin <- 1;
ptstu <- 1;
f.test <- 1;
p1=0;
p2=0;
improved = 0;
# compute the difference in residuals
size <- length(oldResiduals);
size1 <- size - 1;
oldres <- abs(oldResiduals);
newres <- abs(newResiduals);
delta <- newres - oldres;
# Count the number of samples that improved the residuals
reduction <- sum(delta < 0);
# Count the number of samples that worsen the residuals
increase <- sum(delta > 0);
improved <- (reduction-increase)/size; # the net improvement in residuals
p1 <- reduction/size; #proportion of subjects with improved residuals
p2 <- increase/size; #proportion of subjects with worst residuals
rss1 <- sum(oldResiduals^2)
rss2 <- sum(newResiduals^2)/size1;
f.test <- 0.5*(1-pf(rss1/rss2-size1,1,size1)); # Just compare an improvement of the residual Variance
tdat <- try(t.test(oldres, newres, paired = TRUE,alternative = "greater"))
if (!inherits(tdat, "try-error"))
{
ptstu <- tdat$p.value # let compute that the probability that the new residuals are better than the old residuals via t-test
}
else
{
ptstu <- 1.0;
}
if (improved>0)
{
pbin <- binom.test(reduction,size,alternative = "greater")$p.value # Lets do a sign test to test a significant improvement in residual variance
pwil <- wilcox.test(oldres, newres, paired = TRUE,alternative = "greater")$p.value # let compute that the probability that the new residuals are better than the old residuals via wilcoxon
}
switch(testType,
Binomial =
{
pvalue = pbin;
},
Wilcox =
{
pvalue = pwil;
},
tStudent =
{
pvalue = ptstu;
},
Ftest =
{
pvalue = f.test;
}
)
result <- list(p1=p1,
p2=p2,
NeRI=improved,
p.value = pvalue,
BinP.value = pbin,
WilcoxP.value = pwil,
tP.value = ptstu,
FP.value = f.test);
return (result)
}
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