[R] Maintaining data order in factanal with missing data

[PIKAL Petr]

Hi

You provided functions, so far so good. But without data it would be quite difficult to understand what the functions do and where could be the issue.

I suspect combination of complete cases selection together with subset and factor behaviour. But I can be completely out of target too.

Petr

> -----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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> 
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