predictFSGL: Outputs Predicted Responses from an FSGL Model for New...

In xhnnnnn/FSGL: Fit a Linear Model with a Combination of Lasso and Group Lasso Regularization. The calculation will more fast than 'SGL' package.

Description

Outputs predicted response values for new user input observations at a specified lambda value

Usage

predictFSGL(x, newX, lam)

Arguments

x	fitted "FSGL" object
newX	covariate matrix for new observations whose responses we wish to predict
lam	the index of the lambda value for the model with which we desire to predict

Details

Predicted outcomes are given

Examples

n = 50; p = 100; size.groups = 10
index <- ceiling(1:p / size.groups)
X = matrix(rnorm(n * p), ncol = p, nrow = n)
beta = (-2:2)
y = X[,1:5] %*% beta + 0.1*rnorm(n)
data = list(x = X, y = y)
Fit = FSGL(data, index, type = "linear")
X.new = matrix(rnorm(n * p), ncol = p, nrow = n)
predictFSGL(Fit, X.new, 5)

xhnnnnn/FSGL documentation built on Jan. 7, 2022, 6:51 p.m.