Why do you want to do this? If there is just a small part of the logistic regression that you are interested in, then there may be a way to compute or approximate that more quickly than doing a full glm fit on every pair. It seems unlikely that you would get much meaning out of that many full regressions, but there may be some piece that you are looking for that getting just that could lend itself to further graphing/analysis.
-- Gregory (Greg) L. Snow Ph.D. Statistical Data Center Intermountain Healthcare greg.s...@imail.org 801.408.8111 > -----Original Message----- > From: r-help-boun...@r-project.org [mailto:r-help-boun...@r- > project.org] On Behalf Of Alison Macalady > Sent: Wednesday, September 22, 2010 5:05 AM > To: r-help@r-project.org > Subject: [R] speeding up regressions using ddply > > > > Hi, > > I have a data set that I'd like to run logistic regressions on, using > ddply to speed up the computation of many models with different > combinations of variables. I would like to run regressions on every > unique two-variable combination in a portion of my data set, but I > can't quite figure out how to do using ddply. The data set looks like > this, with "status" as the binary dependent variable and V1:V8 as > potential independent variables in the logistic regression: > > m <- matrix(rnorm(288), nrow = 36) > colnames(m) <- paste('V', 1:8, sep = '') > x <- data.frame( status = factor(rep(rep(c('D','L'), each = 6), 3)), > as.data.frame(m)) > > I used melt to put my data frame into a more workable format > require(reshape) > xm <- melt(x, id = 'status') > > Here is the basic shape of the function I'd like to apply to every > combination of variables in the dataset: > > h<- function(df) > { > > attach(df) > log.glm <- (glm(status ~ value1+ value2 , family=binomial(link=logit), > na.action=na.omit)) #What I can't figure out is how to specify 2 > different variables (I've put value1 and value2 as placeholders) from > the xm to include in the model > > glm.summary<-summary(log.glm) > aic <- extractAIC(log.glm) > coef <- coef(glm.summary) > list(Est1=coef[1,2], Est2=coef[3,2], AIC=aic[2]) #or whatever other > output here > } > > And then I'd like to use ddply to speed up the computations. > > require(pplyr) > output<-dddply(xm, .(variable), as.data.frame.function(h)) > output > > > I can easily do this using ddply when I only want to use 1 variable in > the model, but can't figure out how to do it with two variables. > > Many thanks for any hints! > > Ali > > > > -------------------- > Alison Macalady > Ph.D. Candidate > University of Arizona > School of Geography and Development > & Laboratory of Tree Ring Research > > ______________________________________________ > 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.
