rrpack (version 0.1-13)

cv.mrrr: Mixed-response reduced-rank regression with rank selected by cross validation

Description

Mixed-response reduced-rank regression with rank selected by cross validation

Usage

cv.mrrr(
  Y,
  X,
  is.pca = NULL,
  offset = NULL,
  ctrl.id = c(),
  family = list(gaussian(), binomial(), poisson()),
  familygroup = NULL,
  maxrank = min(ncol(Y), ncol(X)),
  penstr = list(),
  init = list(),
  control = list(),
  nfold = 5,
  foldid = NULL,
  nlam = 20,
  warm = FALSE
)

Arguments

Y

response matrix

X

covariate matrix

is.pca

If TRUE, mixed principal component analysis with X=I

offset

matrix of the same dimension as Y for offset

ctrl.id

indices of unpenalized predictors

family

a list of family functions as used in glm

familygroup

a list of family indices of the responses

maxrank

integer giving the maximum rank allowed.

penstr

a list of penalty structure of SVD.

init

a list of initial values of kappaC0, kappaS0, C0, and S0

control

a list of controling parameters for the fitting

nfold

number of folds in cross validation

foldid

to specify the folds if desired

nlam

number of tuning parameters; not effective when using rank constrained estimation

warm

if TRUE, use warm start in fitting the solution paths

Value

S3 mrrr object, a list containing

fit

the output from the selected model

dev

deviance measures

Examples

# NOT RUN {
library(rrpack)
simdata <- rrr.sim3(n = 100, p = 30, q.mix = c(5, 20, 5),
                    nrank = 2, mis.prop = 0.2)
Y <- simdata$Y
Y_mis <- simdata$Y.mis
X <- simdata$X
X0 <- cbind(1,X)
C <- simdata$C
family <- simdata$family
familygroup <- simdata$familygroup
svdX0d1 <- svd(X0)$d[1]
init1 = list(kappaC0 = svdX0d1 * 5)
offset = NULL
control = list(epsilon = 1e-4, sv.tol = 1e-2, maxit = 2000,
               trace = FALSE, gammaC0 = 1.1, plot.cv = TRUE,
               conv.obj = TRUE)
fit.cv.mrrr <- cv.mrrr(Y_mis, X, family = family,
                       familygroup = familygroup,
                       maxrank = 20,
                       penstr = list(penaltySVD = "rankCon",
                                     lambdaSVD = c(1 : 6)),
                       control = control, init = init1,
                       nfold = 10, nlam = 50)
summary(fit.cv.mrrr)
coef(fit.cv.mrrr)
fit.mrrr <- fit.cv.mrrr$fit

## plot(svd(fit.mrrr$coef[- 1,])$d)
plot(C ~ fit.mrrr$coef[- 1, ])
abline(a = 0, b = 1)
# }