Prediction for Cross Validation Hierarchical Lasso Object

# S3 method for cv.vennLasso
predict(object, newx, group.mat, s = c("lambda.min"), use.refit = FALSE, ...)

Arguments

object	fitted cv.vennLasso object
newx	new matrix for predictions
group.mat	A matrix of the group memberships for now. Ignore the rest: A list of length equal to the number of groups containing vectors of integers indicating the variable IDs for each group. For example, groups=list(c(1,2), c(2,3), c(3,4,5)) specifies that Group 1 contains variables 1 and 2, Group 2 contains variables 2 and 3, and Group 3 contains variables 3, 4, and 5. Can also be a matrix of 0s and 1s with the number of columns equal to the number of groups and the number of rows equal to the number of variables. A value of 1 in row i and column j indicates that variable i is in group j and 0 indicates that variable i is not in group j.
s	lambda value for the predictions. defaults to all values computed in the vennLasso object
use.refit	Should the refitted beta estimates be used for prediction? Defaults to FALSE. If TRUE then the beta estimates from the model refit on just the selected covariates are used
...	parameters to be passed to predict.vennLasso

predictions or coefficients