model.matrixqrrvglm: Construct the Model Matrix of a QRR-VGLM Object

Description

Creates a model matrix. Two types can be returned: a large one (class "vlm" or one that inherits from this such as "vglm") or a small one (such as returned if it were of class "lm").

Usage

model.matrixqrrvglm(object, type = c("latvar", "lm", "vlm"), ...)

Value

The design matrix after scaling

for a regression model with the specified formula and data. By after scaling, it is meant that it matches the output of coef(qrrvglmObject) rather than the original scaling of the fitted object.

Arguments

object

an object of a class "qrrvglm", i.e., a cqo object.

type

Type of model (or design) matrix returned. The first is the default. The value "latvar" is model matrix mainly comprising of the latent variable values (sometimes called the site scores). The value "lm" is the LM matrix directly corresponding to the formula argument. The value "vlm" is the big VLM model matrix given C.

...

further arguments passed to or from other methods.

Details

This function creates one of several design matrices from object. For example, this can be a small LM object or a big VLM object.

When type = "vlm" this function calls fnumat2R() to construct the big model matrix given C. That is, the constrained coefficients are assumed known, so that something like a large Poisson or logistic regression is set up. This is because all responses are fitted simultaneously here. The columns are labelled in the following order and with the following prefixes: "A" for the \(A\) matrix (linear in the latent variables), "D" for the \(D\) matrix (quadratic in the latent variables), "x1." for the \(B1\) matrix (usually contains the intercept; see the argument noRRR in qrrvglm.control).

Examples

Run this code

if (FALSE) {
set.seed(1); n <- 40; p <- 3; S <- 4; myrank <- 1
mydata <- rcqo(n, p, S, Rank = myrank, es.opt = TRUE, eq.max = TRUE)
(myform <- attr(mydata, "formula"))
mycqo <- cqo(myform, poissonff, data = mydata,
             I.tol = TRUE, Rank = myrank, Bestof = 5)
model.matrix(mycqo, type = "latvar")
model.matrix(mycqo, type = "lm")
model.matrix(mycqo, type = "vlm")
}