Hi Alison, On Wed, Sep 22, 2010 at 11:05 AM, Alison Macalady <a...@kmhome.org> wrote: > > > 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.
In my experience ddply is not particularly fast. I use it a lot because it is flexible and has easy to understand syntax, not for it's speed. 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. I'm not sure ddply is the tool for this job. 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)) > You can use combn to determine the combinations you want: Varcombos <- combn(names(x)[-1], 2) >From there you can do a loop, something like results <- list() for(i in 1:dim(Varcombos)[2]) { log.glm <- glm(as.formula(paste("status ~ ", Varcombos[1,i], " + ", Varcombos[2,i], sep="")), family=binomial(link=logit), na.action=na.omit, data=x) glm.summary<-summary(log.glm) aic <- extractAIC(log.glm) coef <- coef(glm.summary) results[[i]] <- list(Est1=coef[1,2], Est2=coef[3,2], AIC=aic[2]) #or whatever other output here names(results)[i] <- paste(Varcombos[1,i], Varcombos[2,i], sep="_") } I'm sure you could replace the loop with something more elegant, but I'm not really sure how to go about it. > 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. I don't think this approach can work. You are saying "split up xm by variable" and then expecting to be able to reference different levels of variable within each split, an impossible request. Hope this helps, Ista > > 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. > -- Ista Zahn Graduate student University of Rochester Department of Clinical and Social Psychology http://yourpsyche.org ______________________________________________ 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.