predict.tune_xrnet: Predict function for "tune_xrnet" object

In USCbiostats/hierr: Hierarchical Regularized Regression

Predict function for "tune_xrnet" object

Description

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).

Usage

## S3 method for class '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")

USCbiostats/hierr documentation built on Dec. 12, 2023, 12:55 p.m.