predict.glintnet: make predictions from a "glintnet" object.

In adelie: Group Lasso and Elastic Net Solver for Generalized Linear Models

make predictions from a "glintnet" object.

Description

Similar to other predict methods, this functions predicts linear predictors, coefficients and more from a fitted "glintnet" object.

Usage

## S3 method for class 'glintnet'
predict(
  object,
  newx,
  lambda = NULL,
  type = c("link", "response", "coefficients", "nonzero"),
  newoffsets = NULL,
  n_threads = 1,
  ...
)

Arguments

object	Fitted "glintnet" model.
newx	Matrix of new values for x at which predictions are to be made. This matrix is of the same form as in the call to glintnet.
lambda	Value(s) of the penalty parameter lambda at which predictions are required. Default is the entire sequence used to create the model. If values of lambda are supplied, the function uses linear interpolation to make predictions for values of lambda that do not coincide with those used in the fitting algorithm. Note: if newx is a vector (a single row which has lost its matrix dimensions), convert it to a 1-row matrix first, e.g. by supplying t(newx) instead.
type	Type of prediction required. Type "link" is the default, and gives the linear predictors. Type "response" applies the inverse link to these predictions. Type "coefficients" extracts the coefficients, intercepts and the active-set sizes. Type "nonzero" returns a list of active groups along the path, indexed from 1 to number of groups.
newoffsets	If an offset is used in the fit, then one must be supplied for making predictions (except for type="coefficients".
n_threads	Number of threads, default 1.
...	Other arguments that can be passed to predict.grpnet

Value

The object returned depends on type.

Author(s)

James Yang, Trevor Hastie, and Balasubramanian Narasimhan
Maintainer: Trevor Hastie [e-mail]

References

Yang, James and Hastie, Trevor. (2024) A Fast and Scalable Pathwise-Solver for Group Lasso and Elastic Net Penalized Regression via Block-Coordinate Descent. arXiv \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.2405.08631")}.
Adelie Python user guide https://jamesyang007.github.io/adelie/

See Also

grpnet, and print, and coef methods, and cv.grpnet.

Examples

set.seed(0)
n=500
d_cont = 5     # number of continuous features
d_disc = 5     # number of categorical features
Z_cont = matrix(rnorm(n*d_cont), n, d_cont)
levels = sample(2:5,d_disc, replace = TRUE)
Z_disc = matrix(0,n,d_disc)
for(i in seq(d_disc))Z_disc[,i] = sample(0:(levels[i]-1),n,replace=TRUE)
Z = cbind(Z_cont,Z_disc)
levels = c(rep(1,d_cont),levels)

xmat = model.matrix(~Z_cont[,1]*factor(Z_disc[,2]))
nc=ncol(xmat)
beta = rnorm(nc)
y = xmat%*%beta+rnorm(n)*1.5

fit <- glintnet(Z, glm.gaussian(y), levels=levels, intr_keys = 1)
predict(fit, lambda = c(.1,.01), newx = Z[1:4,])
predict(fit, lambda = c(0.1,0.01), type="nonzero")

adelie documentation built on April 3, 2025, 8:58 p.m.