SCGLR (version 3.1.0)

kCompRand: Function that fits the mixed-SCGLR model

Description

Calculates the components to predict all the response variables.

Usage

kCompRand(
  Y,
  family,
  size = NULL,
  X,
  AX = NULL,
  random,
  loffset = NULL,
  k,
  init.sigma = rep(1, ncol(Y)),
  init.comp = c("pca", "pls"),
  method = methodSR("vpi", l = 4, s = 1/2, maxiter = 1000, epsilon = 10^-6, bailout =
    1000)
)

Value

an object of the SCGLR class.

Arguments

Y

the matrix of random responses

family

a vector of character of the same length as the number of response variables: "bernoulli", "binomial", "poisson" or "gaussian" is allowed.

size

describes the number of trials for the binomial dependent variables: a (number of observations * number of binomial response variables) matrix is expected.

X

the matrix of the standardized explanatory variables

AX

the matrix of the additional explanatory variables

random

the vector giving the group of each unit (factor)

loffset

a matrix of size (number of observations * number of Poisson response variables) giving the log of the offset associated with each observation

k

number of components, default is one

init.sigma

a vector giving the initial values of the variance components, default is rep(1, ncol(Y))

init.comp

a character describing how the components (loadings-vectors) are initialized in the PING algorithm: "pca" or "pls" is allowed.

method

Regularization criterion type: object of class "method.SCGLR" built by function methodSR.

Examples

if (FALSE) {
library(SCGLR)
# load sample data
data(dataGen)
k.opt=4
s.opt=0.1
l.opt=10
withRandom.opt=kCompRand(Y=dataGen$Y, family=rep("poisson", ncol(dataGen$Y)),
                        X=dataGen$X, AX=dataGen$AX,
                        random=dataGen$random, loffset=log(dataGen$offset), k=k.opt,
                        init.sigma = rep(1, ncol(dataGen$Y)), init.comp = "pca",
                        method=methodSR("vpi", l=l.opt, s=s.opt,
                                        maxiter=1000, epsilon=10^-6, bailout=1000))
plot(withRandom.opt, pred=TRUE, plane=c(1,2), title="Component plane (1,2)",
     threshold=0.7, covariates.alpha=0.4, predictors.labels.size=6)
}