Make predictions from a `sparsegl` object.

Similar to other predict methods, this function produces fitted values and class labels from a fitted [`sparsegl`] object.

Usage

# S3 method for class 'sparsegl'
predict(
  object,
  newx,
  s = NULL,
  type = c("link", "response", "coefficients", "nonzero", "class"),
  ...
)

Arguments

object

Fitted [sparsegl()] model 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 predictions are required. Default is the entire sequence used to create the model.

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

The object returned depends on type.

Details

`s` is the new vector of `lambda` values at which predictions are requested. If `s` is not in the lambda sequence used for fitting the model, the `coef` function will use linear interpolation to make predictions. The new values are interpolated using a fraction of coefficients from both left and right `lambda` indices.

See also

[sparsegl()], [coef.sparsegl()].