Extract coefficients or predict response in new data using fitted model from a tune_xrnet
object.
Note that we currently only support returning results that are in the original path(s).
# S3 method for tune_xrnet predict( object, newdata = NULL, newdata_fixed = NULL, p = "opt", pext = "opt", 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. Default is optimal value in tune_xrnet object. |
pext | vector of penalty values to apply to external data variables. Default is optimal value in tune_xrnet object. |
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) ## 5-fold cross validation cv_xrnet <- tune_xrnet( x = x_linear, y = y_linear, external = ext_linear, family = "gaussian", control = xrnet.control(tolerance = 1e-6) ) ## Get coefficients and predictions at optimal penalty combination coef_xrnet <- predict(cv_xrnet, type = "coefficients") pred_xrnet <- predict(cv_xrnet, newdata = x_linear, type = "response")