rrpack (version 0.1-13)

cv.sofar: 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 \(U^{T}U = diag(s_{u})\)

sv

a scaling vector for V such that \(V^{T}V = diag(s_{v})\)

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.