cv.srrr: Row-sparse reduced-rank regression tuned by cross validation

In rrpack: Reduced-Rank Regression

Row-sparse reduced-rank regression tuned by cross validation

Description

Row-sparse reduced-rank regression tuned by cross validation

Usage

cv.srrr(
  Y,
  X,
  nrank = 1,
  method = c("glasso", "adglasso"),
  nfold = 5,
  norder = NULL,
  A0 = NULL,
  V0 = NULL,
  modstr = list(),
  control = list()
)

Arguments

Y	response matrix
X	covariate matrix
nrank	prespecified rank
method	group lasso or adaptive group lasso
nfold	fold number
norder	for constructing the folds
A0	initial value
V0	initial value
modstr	a list of model parameters controlling the model fitting
control	a list of parameters for controlling the fitting process

Details

Model parameters controlling the model fitting can be specified through argument modstr. The available elements include

• lamA: tuning parameter sequence.

• nlam: number of tuning parameters; no effect if lamA is specified.

• minLambda: minimum lambda value, no effect if lamA is specified.

• maxLambda: maxmum lambda value, no effect if lamA is specified.

• WA: adaptive weights. If NULL, the weights are constructed from RRR.

• wgamma: power parameter for constructing adaptive weights.

Similarly, the computational parameters controlling optimization can be specified through argument control. The available elements include

• epsilon: epsilonergence tolerance.

• maxit: maximum number of iterations.

• inner.eps: used in inner loop.

• inner.maxit: used in inner loop.

Value

A list of fitting results

References

Chen, L. and Huang, J.Z. (2012) Sparse reduced-rank regression for simultaneous dimension reduction and variable selection. Journal of the American Statistical Association. 107:500, 1533–1545.

rrpack documentation built on June 16, 2022, 9:05 a.m.