Looping over dependent variable in a mixed model - r

Question

I am trying to loop over multiple variables in a mixed model (using the rptGaussian function from the rptR package) but I am unable to do it despite several efforts. I am trying the following code. I use the following code without a loop and it works fine:
(rptGaussian(Arg ~ (1|class)+(1|kit)+(1|sex),
grname=c("class","kit","sex","Fixed"),
data=ggm2, nboot=10, npermut=10, adjusted=FALSE)
However, when I try to loop more variables I get the error
Error in terms.default(formula) : no terms component nor attribute
I am trying the following code for the loop.
varlist<-c("var1", "var2")
blups.models <- lapply(varlist, function(x) {
rptGaussian(substitute(i ~ (1|class)+(1|kit)+(1|sex),
list(i = as.name(x))),
grname=c("class","kit","lab","Fixed"),
data=ggm2, nboot=10, npermut=10, adjusted=FALSE)
})
Here is a dummy data table:
sex class kit var1 var2 var3 var4
Female A Cont 10.79730768 10 20 18
Female A Exp 11.2474347 17 1 17
Female A Cont 11.64820939 10 5 17
Female A Exp 15.62800413 20 8 4
Female B Cont 12.41705885 5 16 8
Female B Exp 12.80249244 9 10 1
Female B Cont 10.76949177 6 13 2
Female B Exp 14.71370141 7 12 11
Male A Cont 8.931529823 8 3 6
Male A Exp 10.46899683 3 12 13
Male A Cont 8.363257621 3 13 17
Male A Exp 8.753117911 10 16 10
Male B Cont 9.110946315 9 13 4
Male B Exp 9.595131886 18 10 17
Male B Cont 9.454670188 1 10 11
Male B Exp 10.59379123 11 1 3

Answer 1

In general this kind of looping is easier (IMO) with string-based solutions, especially the reformulate() wrapper function, than with substitute().
I used read.table(header=TRUE,text="...") to read the data above and this slightly modified code for the single model:
library(rptR)
r1 <- rptGaussian(var1 ~ (1|class)+(1|kit)+(1|sex),
grname=c("class","kit","sex","Fixed"),
data=ggm2, nboot=10, npermut=10, adjusted=FALSE)
For multiple models:
varlist <- c("var1", "var2")
Make list of formulas:
formulas <- lapply(varlist,
reformulate,
termlabels="(1|class)+(1|kit)+(1|sex)")
Apply rptGaussian to formulas:
blups.models <- lapply(formulas,
rptGaussian,
grname=c("class","kit","sex","Fixed"),
data=ggm2, nboot=10, npermut=10, adjusted=FALSE)
If you want to collapse the results to a nice form, you have to figure out how to extract the results from a single fit into a data frame or similar structure. In this case the result is a rpt object and methods(class="rpt") tells you that there are only print, plot, and summary methods, but the summary() method returns an object that has lots of potentially useful bits. Here's an example:
## extract estimates and standard errors of estimates as a 1-row data frame
sumfun <- function(x) {
ss <- summary(x)
se.names <- paste(rownames(ss$se),"se",sep=".")
cbind(ss$R,setNames(as.data.frame(t(ss$se)),se.names))
}
A possibly-better alternative would be to return data.frame(term=names(ss$R),rpt=unlist(ss$R),se=ss$se) (a 3-column by n-row data frame) instead.
I'm going to use dplyr::bind_rows() because it's handy, but you could use base-R tools (do.call(rbind(...))) instead if you prefer.
names(blups.models) <- varlist
dplyr::bind_rows(lapply(blups.models,sumfun),
.id="var")
var class kit sex Fixed class.se kit.se sex.se Fixed.se
1 var1 0 0.1444659 0.65887365 0 0.04992624 0.2136589 0.2954982 0
2 var2 0 0.3322780 0.01734343 0 0.01981748 0.2243989 0.1158878 0
Are you sure it makes sense to do repeatability scores across sexes and other categories with small numbers of levels?