rssvd: Reduced-rank regression with a sparse singular value...

In rrpack: Reduced-Rank Regression

Reduced-rank regression with a sparse singular value decomposition

Description

Reduced-rank regression with a sparse singular value decomposition using the iterative exclusive extraction algorithm.

Usage

rssvd(
  Y,
  X,
  nrank,
  ic.type = c("BIC", "BICP", "AIC"),
  orthX = FALSE,
  control = list(),
  screening = FALSE
)

Arguments

Y	response matrix
X	covariate matrix
nrank	integer specification of the desired rank
ic.type	character specifying which information criterion to use to select the best: ‘BIC’, ‘BICP’, and ‘AIC’
orthX	logical indicating if X is orthogonal, in which case a faster algorithm is used
control	a list of parameters controlling the fitting process
screening	If TRUE, marginal screening via glm is performed before srrr fitting.

Details

The model fitting can be controled through argument control. The available elements include

• maxit: maximum number of iterations.

• epsilon: convergence tolerance.

• innerMaxit: maximum number of iterations for inner steps.

• innerEpsilon: convergence tolerance for inner steps.

• nlambda: number of tuning parameters.

• adaptive: if Ture, use adaptive penalization.

• gamma0: power parameter for constructing adaptive weights.

• minLambda: multiplicate factor to determine the minimum lambda.

• niter.eea: the number of iterations in the iterative exclusive extraction algorithm.

• df.tol: tolerance.

Value

S3 rssvd.path object, a list consisting of

Upath	solution path of U
Vpath	solution path of V
Dpath	solution path of D
U	estimated left singular matrix that is orthogonal
V	estimated right singular matrix that is orthogonal
D	estimated singular values such that C=UDVt
rank	estimated rank

References

Chen, K., Chan, K.-S. and Stenseth, N. C. (2012) Reduced rank stochastic regression with a sparse singular value decomposition. Journal of the Royal Statistical Society: Series B, 74, 203–221.

Examples

library(rrpack)
## Simulate data from a sparse factor regression model
p <- 50; q <- 50; n <- 100; nrank <- 3
mydata <- rrr.sim1(n, p, q, nrank, s2n = 1, sigma = NULL,
                   rho_X = 0.5, rho_E = 0.3)
fit1 <- with(mydata, rssvd(Y, X, nrank = nrank + 1))
summary(fit1)
plot(fit1)

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