I'm not sure why, but lme() doesn't seem to like the variables to be
referenced as part of a list using [ or $.
Here's an easy workaround ...
ids <- a$id
for(i in 2:4){
for(j in 5:7){
y <- a[, j]
x <- a[, i]
lme(y ~ x , random= ~1|ids, na.action="na.exclude")
}}
Jean
Berta Ibáñez <bertu...@hotmail.com> wrote on 07/18/2012 08:53:51 AM:
> Dear R-list,
>
> I have a data set (in the following example called "a") which have:
>
> one "subject indicator" variable (called "id")
> three dependent variables (varD, varE, var F)
> three independent variables (varA, varB, varC)
>
> I want to fit 9 lme models, one per posible combination (DA, DB, DC,
> EA, EB, EC, FA, FB, FC).
> In stead of writting the 9 lme models, I want to do it
> sistematically (the example is a simplification of what I really
> have). Here you have the comands for the first model:
>
> library(nlme)
> set.seed(50)
> a<-data.frame(array(c(rep(1:10,10), rnorm(600)), c(100,7)))
> names(a)<-c("id", "varA", "varB", "varC", "varD", "varE", "varF")
> lme(varD ~ varA , random= ~1|id, data=a, na.action="na.exclude")
>
> I supossed that a simple sintaxis going through the variables of
> dataset "a" could cope with it:
>
> for(i in 2:4){
> for(j in 5:7){
> lme(a[,j] ~ a[,i] , random= ~1|id, data=a, na.action="na.exclude")
> }}
>
> but it does not, and the use of eval, as.symbol and so on does not help.
>
> for(i in 2:4){
> for(j in 5:7){
> lme(eval(as.symbol(names(a)[j])) ~ eval(as.symbol(names(a)[i])) ,
> random= ~1|id, data=a, na.action="na.exclude")
> }}
>
> Any help??? Thanks a lot in advance!
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