Make predictions from a `cv.sparsegl` object.

This function makes predictions from a cross-validated [cv.sparsegl()] object, using the stored `sparsegl.fit` object, and the value chosen for `lambda`.

Usage

# S3 method for class 'cv.sparsegl'
predict(
  object,
  newx,
  s = c("lambda.1se", "lambda.min"),
  type = c("link", "response", "coefficients", "nonzero", "class"),
  ...
)

Arguments

object

Fitted [cv.sparsegl()] object.

newx

Matrix of new values for `x` at which predictions are to be made. Must be a matrix. This argument is mandatory.

s

Value(s) of the penalty parameter `lambda` at which coefficients are desired. Default is the single value `s = "lambda.1se"` stored in the CV object (corresponding to the largest value of `lambda` such that CV error estimate is within 1 standard error of the minimum). Alternatively `s = "lambda.min"` can be used (corresponding to the minimum of cross validation error estimate). If `s` is numeric, it is taken as the value(s) of `lambda` to be used.

type

Type of prediction required. Type `"link"` gives the linear predictors for `"binomial"`; for `"gaussian"` models it gives the fitted values. Type `"response"` gives predictions on the scale of the response (for example, fitted probabilities for `"binomial"`); for `"gaussian"` type `"response"` is equivalent to type `"link"`. Type `"coefficients"` computes the coefficients at the requested values for `s`. Type `"class"` applies only to `"binomial"` models, and produces the class label corresponding to the maximum probability. Type `"nonzero"` returns a list of the indices of the nonzero coefficients for each value of s.

...

Not used.

Value

A matrix or vector of predicted values.

See also

[cv.sparsegl()] and [coef.cv.sparsegl()].