SDAP | R Documentation |
Applies proximal gradient algorithm to the optimal scoring formulation of sparse discriminant analysis proposed by Clemmensen et al. 2011.
SDAP(Xt, ...)
## Default S3 method:
SDAP(
Xt,
Yt,
Om,
gam,
lam,
q,
PGsteps,
PGtol,
maxits,
tol,
initTheta,
bt = FALSE,
L,
eta,
...
)
Xt |
n by p data matrix, (not a data frame, but a matrix) |
Yt |
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. |
Om |
p by p parameter matrix Omega in generalized elastic net penalty. |
gam |
Regularization parameter for elastic net penalty. |
lam |
Regularization parameter for l1 penalty, must be greater than zero. |
q |
Desired number of discriminant vectors. |
PGsteps |
Maximum number if inner proximal gradient algorithm for finding beta. |
PGtol |
Stopping tolerance for inner APG method. |
maxits |
Number of iterations to run |
tol |
Stopping tolerance for proximal gradient algorithm. |
initTheta |
Initial first theta, default value is a vector of ones. |
bt |
Boolean to indicate whether backtracking should be used, default false. |
L |
Initial estimate for Lipshitz constant used for backtracking. |
eta |
Scalar for Lipshitz constant. |
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)
call
The matched call.
B
p by q matrix of discriminant vectors.
Q
K by q matrix of scoring vectors.
subits
Total number of iterations in proximal gradient subroutine.
totalits
Number coordinate descent iterations for all discriminant vectors
NULL
SDAPcv
, SDAAP
and SDAD
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