Matrix (version 0.999375-42)

glpModel-class: Class "glpModel" of General Linear Prediction Models

Description

The class "glpModel" conceptionally contains a very large class of General Linear Prediction Models.

Its resp slot (of class "respModule") may model linear, non-linear, generalized linear and non-linear generalized response models.

Objects from the Class

Objects can be created by calls of the form new("glpModel", ...), but typically rather are returned by our modeling functions, e.g., the (experimental, hence currently hidden) glm4().

Methods

There are no methods, currently, but that will change.

See Also

The constituents of this class are respModule and predModule, both of which have several sub classes.

Examples

showClass("glpModel")

### All the following is very experimental -- and probably will change: -------

data(CO2, package="datasets")
## dense linear model
str(Matrix:::glm4(uptake ~ 0 + Type*Treatment, data=CO2, doFit = FALSE), 4)
## sparse linear model
str(Matrix:::glm4(uptake ~ 0 + Type*Treatment, data=CO2, doFit = FALSE,
                  sparse = TRUE), 4)

## From example(glm): -----------------

## Dobson (1990) Page 93: Randomized Controlled Trial :
str(trial <- data.frame(counts=c(18,17,15,20,10,20,25,13,12),
                        outcome=gl(3,1,9,labels=LETTERS[1:3]),
                        treatment=gl(3,3,labels=letters[1:3])))
glm.D93 <- glm(counts ~ outcome + treatment, family=poisson, data=trial)
summary(glm.D93)
glmM <- Matrix:::glm4(counts ~ outcome + treatment, family = poisson, data=trial)
stopifnot( all.equal(glmM@pred@coef, unname(coef(glm.D93))) )

## Watch the iterations --- and use no intercept --> more sparse X
## 1) dense generalized linear model
glmM <- Matrix:::glm4(counts ~ 0+outcome + treatment, poisson, trial,
                      verbose = TRUE)
## 2) sparse generalized linear model
glmS <- Matrix:::glm4(counts ~ 0+outcome + treatment, poisson, trial,
                      verbose = TRUE, sparse = TRUE)
str(glmS, max.lev = 4)
stopifnot( all.equal(glmM@pred@coef, glmS@pred@coef),
           all.equal(glmM@pred@Vtr,  glmS@pred@Vtr) )


## A Gamma example, from McCullagh & Nelder (1989, pp. 300-2)
clotting <- data.frame(u = c(5,10,15,20,30,40,60,80,100),
                       lot1 = c(118,58,42,35,27,25,21,19,18),
                       lot2 = c(69,35,26,21,18,16,13,12,12))
str(gMN <- Matrix:::glm4(lot1 ~ log(u), data=clotting, family=Gamma, verbose=TRUE))
glm. <- glm(lot1 ~ log(u), data=clotting, family=Gamma)
stopifnot( all.equal(gMN@pred@coef, unname(coef(glm.))) )