SCGLR (version 3.0)

multivariateGlm.fit: Multivariate generalized linear regression

Description

multivariateGlm is used to fit multivariate generalized linear models specified by a symbolic formula together with the distributions of the responses. This function performs a simple GLM fit for each dependent variable with the associated distribution.

Usage

multivariateGlm.fit(Y, comp, family, offset, size)

Arguments

Y

matrix of dependent variables.

comp

matrix of covariates.

family

a vector of character giving the family distribution of each response.

offset

used for the poisson dependent variables. A vector or a matrix of size: number of observations * number of Poisson dependent variables is expected.

size

a matrix giving the number of trials for each Binomial dependent variable ncol(size) must be equal to the number of Binomial variables.

Value

the list, each item of which is the glm object associated with each response.

Examples

# NOT RUN {
library(SCGLR)

# load sample data
data(genus)

# get variable names from dataset
n <- names(genus)
ny <- n[grep("^gen",n)]    # Y <- names that begins with "gen"
nx <- n[-grep("^gen",n)]   # X <- remaining names

# remove "geology" and "surface" from nx as surface
# is offset and we want to use geology as additional covariate
nx <-nx[!nx%in%c("geology","surface")]

# build multivariate formula
# we also add "lat*lon" as computed covariate
form <- multivariateFormula(ny,c(nx,"I(lat*lon)"),c("geology"))

# split genus dataset
sub <- sample(1:nrow(genus),100,replace=FALSE)
sub_fit <- (1:nrow(genus))[-sub]

# define family
fam <- rep("poisson",length(ny))

# fit the model
genus.scglr <- scglr(formula=form, data=genus, family=fam, K=4,
 offset=genus$surface, subset=sub_fit)

# xnew, the design matrix associated to sub-sample used for prediction
# note rhs parameter is introduced to take into account that the
# covariate part of the formula is composed of two differents sets
xnew <- model.matrix(form, data=genus[sub,], rhs=1:2)[,-1]

# prediction based on the scglr approch
pred.scglr <- multivariatePredictGlm(xnew,family=fam,
 beta=genus.scglr$beta, offset=genus$surface[sub])
cor.scglr <-diag(cor(pred.scglr,genus[sub,ny]))
plot(cor.scglr, col="red",ylim=c(-1,1))

# prediction based on classical poisson glm
X <- model.matrix(form, data=genus)[,-1]
Y <- genus[,ny]
genus.glm <- multivariateGlm.fit(Y[sub_fit,,drop=FALSE],X[sub_fit,,drop=FALSE],
             family=fam, offset=matrix(genus$surface[sub_fit],
             length(sub_fit),length(ny)),size=NULL)
coefs <- sapply(genus.glm,coef)

# rhs parameter is introduced to take into account that the
# covariate part of the formula is composed of two differents sets
pred.glm <- multivariatePredictGlm(xnew,family=fam,beta=coefs,
 offset=genus$surface[sub])
cor.glm <- diag(cor(pred.glm,genus[sub,ny]))

points(cor.glm, col="blue")
# }