VGAM (version 1.1-9)

vcovvlm: Calculate Variance-Covariance Matrix for a Fitted VLM or RR-VGLM or QRR-VGLM Object

Description

Returns the variance-covariance matrix of the parameters of a fitted vlm-class object or a fitted rrvglm-class object.

Usage

vcov(object, ...)
vcovvlm(object, dispersion = NULL, untransform = FALSE,
        complete = TRUE, ...)
vcovqrrvglm(object, ...)

Value

Same as vcov.

Arguments

object

A fitted model object, having class vlm-class or rrvglm-class or qrrvglm-class or a superclass of such. The former includes a vglm object.

dispersion

Numerical. A value may be specified, else it is estimated for quasi-GLMs (e.g., method of moments). For almost all other types of VGLMs it is usually unity. The value is multiplied by the raw variance-covariance matrix.

untransform

logical. For intercept-only models with trivial constraints; if set TRUE then the parameter link function is inverted to give the answer for the untransformed/raw parameter.

complete

An argument that is currently ignored. Added only so that linearHypothesis() in car can be called.

...

Same as vcov.

Author

Thomas W. Yee

Details

This methods function is based on the QR decomposition of the (large) VLM model matrix and working weight matrices. Currently vcovvlm operates on the fundamental vlm-class objects because pretty well all modelling functions in VGAM inherit from this. Currently vcovrrvglm is not entirely reliable because the elements of the A--C part of the matrix sometimes cannot be computed very accurately, so that the entire matrix is not positive-definite.

For "qrrvglm" objects, vcovqrrvglm is currently working with Rank = 1 objects or when I.tolerances = TRUE. Then the answer is conditional given C. The code is based on model.matrixqrrvglm so that the dimnames are the same.

See Also

confintvglm, summaryvglm, vcov, hdeff.vglm, lrt.stat.vlm, model.matrixqrrvglm.

Examples

if (FALSE) {
ndata <- data.frame(x2 = runif(nn <- 300))
ndata <- transform(ndata, y1 = rnbinom(nn, mu = exp(3+x2), exp(1)),
                          y2 = rnbinom(nn, mu = exp(2-x2), exp(0)))
fit1 <- vglm(cbind(y1, y2) ~ x2, negbinomial, ndata, trace = TRUE)
fit2 <- rrvglm(y1 ~ x2, negbinomial(zero = NULL), data = ndata)
coef(fit1, matrix = TRUE)
vcov(fit1)
vcov(fit2)
}