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-boun...@r-project.org [mailto:r-help-bounces@r- > project.org] On Behalf Of s00123...@myacu.edu.au > Sent: Friday, July 26, 2013 9:35 AM > To: r-help@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 > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting- > guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
