Hello,
So, I have this (simplified for better understanding) binomial mixed
effects model [library (lme4)]
Mymodel <- glmer(cross.01 ~ stream.01 + width.m + grass.per + (1|
structure.id),
data = Mydata, family = binomial)
stream is a factor with 2 levels; width.m is continuous; grass.per is a
percentage
Now, a reviewer is asking me to apply "a cross-validation procedure (i.e. a
leave-one-out design coupled with predictive metrics as e.g. AUC) on this
model"
Does anyone have R-code to do this cross validation in my logistic mixed
effects model? In the reviewer words: "the model should be evaluated also
as for their predictive performance, not only for assumptions violation and
for goodness-of-fit" (which I presented already in the reviewed paper draft)
Many thanks in advance,
pedro
[[alternative HTML version deleted]]
______________________________________________
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
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.
