confintvglm {VGAM} | R Documentation |
Computes confidence intervals for one or more parameters in a fitted
model. Currently the object must be a
"vglm"
object.
confint(object, parm, level = 0.95, ...)
object |
A fitted model object. |
parm, level, ... |
Same as |
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.
Same as confint
.
Thomas W. Yee
vcovvlm
,
summaryvglm
,
confint
.
# 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)