Maintaining data order in factanal with missing data

-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
project.org] On Behalf Of s00123776 at myacu.edu.au
Sent: Friday, July 26, 2013 9:35 AM
To: r-help at r-project.org
Subject: [R] Maintaining data order in factanal with missing data

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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