cv.sofar {rrpack} | R Documentation |
Sparse orthognal factor regression tuned by cross validation
Description
Sparse orthognal factor regression tuned by cross validation
Usage
cv.sofar(
Y,
X,
nrank = 1,
su = NULL,
sv = NULL,
nfold = 5,
norder = NULL,
modstr = list(),
control = list(),
screening = FALSE
)
Arguments
Y |
response matrix |
X |
covariate matrix |
nrank |
an integer specifying the desired rank/number of factors |
su |
a scaling vector for U such that |
sv |
a scaling vector for V such that |
nfold |
number of fold; used for cv.sofar |
norder |
observation orders to constrct data folds; used for cv.sofar |
modstr |
a list of internal model parameters controlling the model fitting |
control |
a list of internal computation parameters controlling optimization |
screening |
If TRUE, marginal screening via lasso is performed before sofar fitting. |
Details
The model parameters can be specified through argument modstr
.
The available elements include
mu: parameter in the augmented Lagrangian function.
mugamma: increament of mu along iterations to speed up computation.
WA: weight matrix for A.
WB: weight matrix for B.
Wd: weight matrix for d.
wgamma: power parameter in constructing adaptive weights.
The model fitting can be controled through argument control
.
The avilable elements include
nlam: number of lambda triplets to be used.
lam.min.factor: set the smallest lambda triplets as a fraction of the estimation lambda.max triplets.
lam.max.factor: set the largest lambda triplets as a multiple of the estimation lambda.max triplets.
lam.AB.factor: set the relative penalty level between A/B and D.
penA,penB,penD: if TRUE, penalty is applied.
lamA: sequence of tuning parameters for A.
lamB: sequence of tuning parameters for B.
lamD: sequence of tuning parameters for d.
methodA: penalty for penalizing A.
methodB: penalty for penalizing B.
epsilon: convergence tolerance.
maxit: maximum number of iterations.
innerEpsilon: convergence tolerance for inner subroutines.
innerMaxit: maximum number of iterations for inner subroutines.
sv.tol: tolerance for singular values.