Hello Andrew, Take a look at the following:
predict(model1, addx=T) predict(model2, addx=T) predict(model3, addx=T) As you can see, the factor was turned into dummy variables. However, the predict.rma() function does not expand a factor passed via newmods into the corresponding dummy variables. At the moment, you will have to do this yourself. Note: the intercept is added automatically, so it should not be added to the newmods vector(s): For model2: predict(model2, newmods=c(0,0), addx=T) predict(model2, newmods=c(1,0), addx=T) predict(model2, newmods=c(0,1), addx=T) or use the model.matrix() function: newdat <- model.matrix(~factor(c(1,2,3))) predict(model2, newmods=newdat[,-1], addx=T) For model3: predict(model3, newmods=cbind(1:5,0,0), addx=T) predict(model3, newmods=cbind(1:5,1,0), addx=T) predict(model3, newmods=cbind(1:5,0,1), addx=T) or using model.matrix(): newdat <- expand.grid(mid=c(1,2,3,4,5), mod=factor(c(1,2,3))) newdat <- model.matrix(~ mid + mod, data=newdat) predict(model3, newmods=newdat[,-1], addx=T) Yes, I realize this takes some extra work and is not as convenient as having that factor expanded automatically. Something to put on the to-do list ... Best, Wolfgang > -----Original Message----- > From: [email protected] [mailto:[email protected]] > On Behalf Of Andrew Beckerman > Sent: Thursday, September 08, 2011 14:22 > To: [email protected] > Subject: [R] predict.rma (metafor package) > > 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) ______________________________________________ [email protected] 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.
