 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)

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