Extract coefficients or predict response in new data using fitted model from an xrnet
object.
Note that we currently only support returning coefficient estimates that are in the original path(s).
# S3 method for xrnet predict( object, newdata = NULL, newdata_fixed = NULL, p = NULL, pext = NULL, type = c("response", "link", "coefficients"), ... )
object | A |
---|---|
newdata | matrix with new values for penalized variables |
newdata_fixed | matrix with new values for unpenalized variables |
p | vector of penalty values to apply to predictor variables |
pext | vector of penalty values to apply to external data variables |
type | type of prediction to make using the xrnet model, options include
|
... | pass other arguments to xrnet function (if needed) |
The object returned is based on the value of type as follows:
response: An array with the response predictions based on the data for each penalty combination
link: An array with linear predictions based on the data for each penalty combination
coefficients: A list with the coefficient estimates for each penalty combination. See coef.xrnet
.
data(GaussianExample) fit_xrnet <- xrnet( x = x_linear, y = y_linear, external = ext_linear, family = "gaussian" ) lambda1 <- fit_xrnet$penalty[10] lambda2 <- fit_xrnet$penalty_ext[10] coef_xrnet <- predict( fit_xrnet, p = lambda1, pext = lambda2, type = "coefficients" ) pred_xrnet <- predict( fit_xrnet, p = lambda1, pext = lambda2, newdata = x_linear, type = "response" )