srrr: Row-sparse reduced-eank regresssion

In rrpack: Reduced-Rank Regression

Row-sparse reduced-eank regresssion

Description

Row-sparse reduced-rank regresssion for a prespecified rank; produce a solution path for selecting predictors

Usage

srrr(
  Y,
  X,
  nrank = 2,
  method = c("glasso", "adglasso"),
  ic.type = c("BIC", "BICP", "AIC", "GCV", "GIC"),
  A0 = NULL,
  V0 = NULL,
  modstr = list(),
  control = list(),
  screening = FALSE
)

Arguments

Y	response matrix
X	covariate matrix
nrank	prespecified rank
method	group lasso or adaptive group lasso
ic.type	information criterion
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
screening	If TRUE, marginal screening via glm is performed before srrr fitting.

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.

Examples

library(rrpack)
p <- 100; n <- 100; nrank <- 3
mydata <- rrr.sim2(n, p, p0 = 10,q = 50, q0 = 10, nrank = 3,
                   s2n = 1, sigma = NULL, rho_X = 0.5, rho_E = 0)
fit1 <- with(mydata, srrr(Y, X, nrank = 3))
summary(fit1)
coef(fit1)
plot(fit1)

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