Hi (R 2.13.1, OSX 10.6.8)
I am trying to use predict.rma with continuous and categorical variables. The argument newmods in predict.rma seems to handle coviariates, but appears to falter on factors. While I realise that the coefficients for factors provide the answers, the goal is to eventually use predict.rma with ANCOVA type model with an interaction. Here is a self contained example (poached in part from the MAd package): id<-c(1:20) n.1<-c(10,20,13,22,28,12,12,36,19,12,36,75,33,121,37,14,40,16,14,20) n.2 <- c(11,22,10,20,25,12,12,36,19,11,34,75,33,120,37,14,40,16,10,21) g <- c(.68,.56,.23,.64,.49,-.04,1.49,1.33,.58,1.18,-.11,1.27,.26,.40,.49, .51,.40,.34,.42,1.16) var.g <- c(.08,.06,.03,.04,.09,.04,.009,.033,.0058,.018,.011,.027,.026,.0040, .049,.0051,.040,.034,.0042,.016) mod<-factor(c(rep(c(1,1,2,3),5))) # factor mid<-c(rep(1:5,4)) # covariate df<-data.frame(id, n.1,n.2, g, var.g,mod, mid) # Examples # Random Effects model1<-rma(g,var.g,mods=~mid,method="REML") # covariate model model2<-rma(g,var.g,mods=~mod,method="REML") # factor model model3<-rma(g,var.g,mods=~mid+mod,method="REML") # multiple # example matrix for predicting against model3 newdat<-expand.grid(c(1,2,3,4,5),c(1,2,3)) predict(model1,newmods=c(1,2,3,4,5)) predict(model2,newmods=c(1,2,3)) predict(model3,newmods=newdat) -- Andrew Beckerman Sent with Sparrow (http://bit.ly/sigsprw) [[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.
