Applies proximal gradient algorithm to the optimal scoring formulation of sparse discriminant analysis proposed by Clemmensen et al. 2011.
SDAP(x, ...)# S3 method for default
SDAP(Xt, Yt, Om, gam, lam, q, PGsteps, PGtol, maxits, tol)
n by p data matrix, (not a data frame, but a matrix)
n by K matrix of indicator variables (Yij = 1 if i in class j). This will later be changed to handle factor variables as well. Each observation belongs in a single class, so for a given row/observation, only one element is 1 and the rest is 0.
p by p parameter matrix Omega in generalized elastic net penalty.
Regularization parameter for elastic net penalty.
Regularization parameter for l1 penalty, must be greater than zero.
Desired number of discriminant vectors.
Maximum number if inner proximal gradient algorithm for finding beta.
Stopping tolerance for inner APG method.
Number of iterations to run
Stopping tolerance for proximal gradient algorithm.
SDAP returns an object of class "SDAP" including a list
with the following named components: (More will be added later to handle the predict function)
callThe matched call.
Bp by q matrix of discriminant vectors.
QK by q matrix of scoring vectors.
NULL