predict.xrnet: Predict function for "xrnet" object

In USCbiostats/hierr: Hierarchical Regularized Regression

Predict function for "xrnet" object

Description

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).

Usage

## S3 method for class 'xrnet'
predict(
  object,
  newdata = NULL,
  newdata_fixed = NULL,
  p = NULL,
  pext = NULL,
  type = c("response", "link", "coefficients"),
  ...
)

Arguments

object	A 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
pext	vector of penalty values to apply to external data variables
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)

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"
)

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