ProgPredLasso: Identification of prognostic and predictive biomarkers

In PPLasso: Prognostic Predictive Lasso for Biomarker Selection

Identification of prognostic and predictive biomarkers

Description

The computes the regularization path of the Prognostic Predictive Lasso described in the paper Zhu et al. (2022) given in the references.

Usage

ProgPredLasso(X1, X2, Y=Y, cor_matrix=NULL, gamma=0.99, maxsteps=500, lambda='single')

Arguments

X1	Design matrix of patients characteristics with treatment 1
X2	Design matrix of patients characteristics with treatment 2
Y	Response variable
cor_matrix	Correlation matrix of biomarkers. If not specified, the function cvCovEst from package cvCovEst will be used to estimate this matrix.
gamma	Parameter γ defined in the paper Zhu et al. (2020) given in the references. Its default value is 0.99.
maxsteps	Integer specifying the maximum number of steps for the generalized Lasso algorithm. Its default value is 500.
lambda	Using single tuning parameter or both.

Value

Returns a list with the following components

lambda	different values of the parameter λ considered.
beta	matrix of the estimations of β for all the λ considered.
beta.min	estimation of β which minimize the MSE.
bic	BIC for all the λ considered.
mse	MSE for all the λ considered.

Author(s)

Wencan Zhu, Celine Levy-Leduc, Nils Ternes

PPLasso documentation built on March 7, 2023, 6:33 p.m.