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# modified on July 15, 2016
# (1) revise the definition of zi
# zi1=|xi1-trimedmean(xi1)|
# zi0=|xi0-trimedmean(xi0)|
#
# modified on Dec. 13, 2015
# (1) rename 'y' to 'group'
# (2) rename 'x' to 'value'
# (3) rename scoreTestVarTrimMean.default' to 'iAWvar.TrimMean'
#
# created on July 19, 2015
# scor test of logistic regresssion based on Trimmed-mean based
# Levene's test to test equality of variance
#
# test for equal variance
# group - vector of binary values
# value - continuous variable
# trim.alpha=0.25 is default value for R function 'mean', it is
# the fraction (0 to 0.5) of observations to be trimmed from each
# end of x before the mean is computed. Values of trim.alpha outside
# that range are taken as the nearest endpoint.
iAWvar.TrimMean=function(value,group, trim.alpha = 0.25)
{
u.group=sort(unique(group))
if(length(u.group)!=2)
{
stop("group must take 2 and only 2 values\n")
}
if(!identical(u.group, c(0, 1)))
{
stop("group must only take values 0 or 1\n")
}
if(length(value) != length(group))
{
stop("value must have the same length as group\n")
}
pos1=which(group==1)
pos0=which(group==0)
value1=value[pos1]
value0=value[pos0]
# get trimmed mean
m.value1=mean(value1, na.rm=TRUE, trim = trim.alpha)
m.value0=mean(value0, na.rm=TRUE, trim = trim.alpha)
# trimmed mean centering
value1.2=abs(value1-m.value1)
value0.2=abs(value0-m.value0)
z=rep(NA, length(value))
z[pos1]=value1.2
z[pos0]=value0.2
ybar=mean(group, na.rm=TRUE)
U2=sum((group-ybar)*z, na.rm=TRUE)
zbar=mean(z, na.rm=TRUE)
varU2 = ybar*(1-ybar)*sum((z-zbar)^2, na.rm=TRUE)
T2 = U2^2/varU2
pval= 1-pchisq(T2, df=1)
res=list(U2=U2, varU2=varU2, stat=T2, pval=pval, z=z, zbar=zbar)
return(res)
}
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