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

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

make predictions from a "cv.glintnet" object.

Description

This function makes predictions from a cross-validated glintnet model, using the stored "glintnet.fit" object, and the optimal value chosen for lambda.

Usage

## S3 method for class 'cv.glintnet'
predict(object, newx, lambda = c("lambda.1se", "lambda.min"), ...)

Arguments

object	Fitted "cv.glintnet".
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.
...	Other arguments that can be passed to predict.grpnet

Details

This function makes it easier to use the results of cross-validation to make a prediction.

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

cvfit <- cv.glintnet(Z, glm.gaussian(y), levels=levels, intr_keys = 1)
plot(cvfit)
predict(cvfit, newx=Z[1:5,])

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