The computes the regularization path of the Prognostic Predictive Lasso described in the paper Zhu et al. (2022) given in the references.
ProgPredLasso(X1, X2, Y=Y, cor_matrix=NULL, gamma=0.99, maxsteps=500, lambda='single')Returns a list with the following components
different values of the parameter \(\lambda\) considered.
matrix of the estimations of \(\beta\) for all the \(\lambda\) considered.
estimation of \(\beta\) which minimize the MSE.
BIC for all the \(\lambda\) considered.
MSE for all the \(\lambda\) considered.
Design matrix of patients characteristics with treatment 1
Design matrix of patients characteristics with treatment 2
Response variable
Correlation matrix of biomarkers. If not specified, the function cvCovEst from package cvCovEst will be used to estimate this matrix.
Parameter \(\gamma\) defined in the paper Zhu et al. (2020) given in the references. Its default value is 0.99.
Integer specifying the maximum number of steps for the generalized Lasso algorithm. Its default value is 500.
Using single tuning parameter or both.
Wencan Zhu, Celine Levy-Leduc, Nils Ternes