I wanted to model some data with heteroscedastic errors using a gls
model of the form
library(nlme)
gls(y ~ x, data = data, weights=varPower(1, form= ~y))
That is, with the variance being a power function of the dependent variable y
(or the independent variable x
?). See link here for an example.
For my application I would also need 95% prediction intervals on the model predictions for a specific value of x
. Would anybody know how to calculate these for a gls
model with this kind of error structure? (gls
does not appear to provide for a predict
method with interval="prediction"
) Or alternative approaches that would allow for such an error structure and allow me to calculate prediction intervals? (A solution using weighted regression is posted here, but I would prefer to use gls
instead, as that fits the appropriate power coefficient describing the mean-variance relationship)