This class contains all the input parameters to run CLERE.
Details
Y
[numeric]: The vector of observed responses - size n.
X
[matrix]: The matrix of predictors - size n rows and p columns.
lambda
[numeric]: A non-negative penalty term that controls simultaneouly clusetering and sparsity.
betaInput
[numeric]: A vector of initial guess of the model parameters. The authors suggest to use coefficients obtained after fitting a ridge regression with the shrinkage parameter selected using AIC criterion.
epsPACS
[numeric]: A tolerance threshold that control the convergence of the algroithm. The default value fixed in Bondell's initial script is 1e-5.
nItMax
[integer]: Maximum number of iterations in the algorithm.