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"),
  ...
)

Arguments

object

A tune_xrnet object

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:

  • response

  • link (linear predictor)

  • coefficients

...

pass other arguments to xrnet function (if needed)

Value

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.

Examples

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")