Hi Petr, Thanks for the quick response. Unfortunately I cannot share the data I am working with, however please find attached a suitable R workspace with generated data. It has the appropriate variable names, only the data has been changed.
The last function in the list (init.dfs()) I call to subset the overall data set into the three waves, then conduct the factor analysis on each (1 factor CFA); it's just in a function to ease re-typing in a new workspace. Thanks, Justin -----Original Message----- From: PIKAL Petr [mailto:petr.pi...@precheza.cz] Sent: Friday, 26 July 2013 7:35 PM To: Justin Delahunty; r-help@r-project.org Subject: RE: [R] Maintaining data order in factanal with missing data 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.
