VGAM (version 1.0-1)

confintvglm: Confidence Intervals for VGLM Parameters

Description

Computes confidence intervals for one or more parameters in a fitted model. Currently the object must be a "vglm" object.

Usage

confint(object, parm, level = 0.95, ...)

Arguments

object

A fitted model object.

parm, level, ...

Same as confint.

Value

• Same as confint.

Details

This methods function is based on confint.default and assumes asymptotic normality. In particular, the coef and vcov methods functions are used for vglm-class objects. Unlike for glm objects, there is no profiling currently implemented.

For rrvglm-class objects, currently an error message is produced because I haven't gotten around to write the methods function; it's not too hard, but am too busy! An interim measure is to coerce the object into a "vglm" object, but then the confidence intervals will tend to be too narrow because the estimated constraint matrices are treated as known. For vgam-class objects, currently an error message is produced because the theory is undeveloped.

See Also

vcovvlm, summaryvglm, confint.

Examples

# Example 1: this is based on a glm example
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3, 1, 9); treatment <- gl(3, 3)
 glm.D93 <-  glm(counts ~ outcome + treatment, family = poisson())
vglm.D93 <- vglm(counts ~ outcome + treatment, family = poissonff)
confint(glm.D93) # needs MASS to be present on the system
confint.default(glm.D93)  # based on asymptotic normality
confint(vglm.D93)
confint(vglm.D93) - confint(glm.D93)    # Should be all 0s
confint(vglm.D93) - confint.default(glm.D93)  # based on asymptotic normality

# Example 2: simulated negative binomial data with multiple responses
ndata <- data.frame(x2 = runif(nn <- 300))
ndata <- transform(ndata, y1 = rnbinom(nn, mu = exp(3+x2), size = exp(1)),
                          y2 = rnbinom(nn, mu = exp(2-x2), size = exp(0)))
fit1 <- vglm(cbind(y1, y2) ~ x2, negbinomial, data = ndata, trace = TRUE)
coef(fit1, matrix = TRUE)
confint(fit1)
confint(fit1, "x2:1")  #  This might be improved to "x2" some day...
fit2 <- rrvglm(y1 ~ x2, negbinomial(zero = NULL), data = ndata)
confint(as(fit2, "vglm"))  # Too narrow (SEs are biased downwards)