Hi,
I'm new to R, so sorry if this is a simple answer. I'm currently trying to
collapse some ordinal variables into a composite; the program ideally should
take a data frame as input, perform a factor analysis, compute factor
scores, sds, etc., and return the rescaled scores and loadings. The
difficulty I'm having is that my data set contains a number of NA, which I
am excluding from the analysis using complete.cases(), and thus the
incomplete cases are "skipped". These functions are for a longitudinal data
set with repeated waves of data, so the final rescaled scores from each wave
need to be saved as variables grouped by a unique ID (DMID). The functions
I'm trying to implement are as follows:
weighted.sd<-function(x,w){
sum.w<-sum(w)
sum.w2<-sum(w^2)
mean.w<-sum(x*w)/sum(w)
x.sd.w<-sqrt((sum.w/(sum.w^2-sum.w2))*sum(w*(x-mean.w)^2))
return(x.sd.w)
}
re.scale<-function(f.scores, raw.data, loadings){
fz.scores<-(f.scores+mean(f.scores))/(sd(f.scores))
means<-apply(raw.data,1,weighted.mean,w=loadings)
sds<-apply(raw.data,1,weighted.sd,w=loadings)
grand.mean<-mean(means)
grand.sd<-mean(sds)
final.scores<-((fz.scores*grand.sd)+grand.mean)
return(final.scores)
}
get.scores<-function(data){
fact<-factanal(data[complete.cases(data),],factors=1,scores="regression")
f.scores<-fact$scores[,1]
f.loads<-fact$loadings[,1]
rescaled.scores<-re.scale(f.scores,
data[complete.cases(data),], f.loads)
output.list<-list(rescaled.scores, f.loads)
names(output.list)<-c("rescaled.scores",
"factor.loadings")
return(output.list)
}
init.dfs<-function(){
ab.1.df<-subset(ab.df,,select=c(dmid,g5oab2:g5ovb1))
ab.2.df<-subset(ab.df,,select=c(dmid,w2oab3:w2ovb1))
ab.3.df<-subset(ab.df,,select=c(dmid,
w3oab3, w3oab4, w3oab7, w3oab8, w3ovb1))
ab.1.fa<-get.scores(ab.1.df[-1])
ab.2.fa<-get.scores(ab.2.df[-1])
ab.3.fa<-get.scores(ab.3.df[-1])
}
Thanks for your help,
Justin
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