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dep.effect <-function(x, y, tr = 0.2, nboot = 1000, ...){
#
#
# For two dependent groups,
# compute confidence intervals for four measures of effect size based on difference scores:
#
# AKP: robust standardized difference similar to Cohen's d
# QS: Quantile shift based on the median of the distribution of difference scores,
# QStr: Quantile shift based on the trimmed mean of the distribution of X-Y
# SIGN: P(X<Y), probability that for a random pair, the first is less than the second.
#
# OPT=TRUE: No effect, difference scores are symmetric about zero.
# Under normality, suppose a shift of .2, .5 and .8 standard deviation of the difference score
# is considered small, medium and large. The corresponding values for QS and SIGN are printed.
#
#
# REL.MAG: suppose it is decided that AKP values .1, .3 and .5 are viewed as small, medium and large under normality Then
#
# if OPT=T and
# REL.MAG=c(.1,.3,.5) the default,
# means that the function will compute the corresponding values for the measures of effect size used here.
REL.MAG = NULL
SEED <- FALSE
ecom=c(0.10, 0.54, 0.54, 0.46, 0.30, 0.62, 0.62, 0.38, 0.50, 0.69, 0.69, 0.31)
REL.EF=matrix(NA,4,3)
if(!is.null(y))x=x-y
x=elimna(x)
n=length(x)
output=matrix(NA,ncol=7,nrow=4)
dimnames(output)=list(c('AKP','QS (median)','QStr','SIGN'),c('NULL','Est','S','M','L','ci.low','ci.up'))
output[1,1:2]=c(0,D.akp.effect(x, tr=tr))
output[2,1:2]=c(0.5,depQS(x)$Q.effect)
output[3,1:2]=c(0.5,depQS(x,locfun=mean,tr=tr)$Q.effect)
output[4,1:2]=c(0.5,mean(x[x!=0]<0))
if(is.null(REL.MAG)){
REL.MAG=c(.1,.3,.5)
REL.EF=matrix(ecom,4,3)
}
if(output[1,2]<0)REL.EF[1,]=0-REL.EF[1,]
if(output[2,2]<0.5)REL.EF[2,]=.5-(REL.EF[2,]-.5)
if(output[3,2]<0.5)REL.EF[3,]=.5-(REL.EF[3,]-.5)
if(output[4,2]>0.5)REL.EF[4,]=.5-(REL.EF[4,]-.5)
output[,3:5]=REL.EF
output[1,3:5]=REL.MAG
#}
output[1,6:7]=D.akp.effect.ci(x,SEED=SEED,tr=tr,nboot=nboot)$ci
output[2,6:7]=depQSci(x,SEED=SEED,nboot=nboot)$ci
output[3,6:7]=depQSci(x,locfun=tmean,SEED=SEED,tr=tr,nboot=nboot)$ci
Z=sum(x<0)
output[4,6:7]=binom.conf(Z,n)$ci
class(output)=c("matrix", "array", "dep.effect")
output
}
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