A couple of methods for location-scale regression models from the lmls()
function are provided.
# S3 method for lmls
coef(object, predictor = c("location", "scale"), ...)# S3 method for lmls
fitted(object, predictor = c("location", "scale"), ...)
# S3 method for lmls
predict(
object,
newdata = NULL,
predictor = c("location", "scale"),
type = c("link", "response"),
...
)
# S3 method for lmls
residuals(object, type = c("deviance", "pearson", "response"), ...)
# S3 method for lmls
vcov(object, predictor = c("location", "scale"), ...)
A location-scale regression model from the lmls() function.
The predictor to work on. Either "location" or "scale"
or both. If both, a list with the names "location" and
"scale" is returned.
Currently ignored.
A data frame (or list or environment) with the covariate
values at which the predictions are computed. If NULL, the
predictions at the original data are returned.
Used by predict() and residuals():
For predict(), "link" or "response". If "link"
(default), \(\mu\) and log(\(\sigma\)) are returned.
If "response", \(\mu\) and \(\sigma\)
are returned.
For residuals(), "deviance", "pearson" or
"response". If "deviance" (default) or "pearson",
(\(y - \mu\)) / \(\sigma\) is returned.
If "response", \(y - \mu\) is returned.
A numeric vector for residuals(). For the other methods, a numeric vector
if the argument predictor is either "location" or "scale", or a list
with the names location and scale if it is both.