score.stat {VGAM} | R Documentation |
Generic function that computes Rao's score test statistics evaluated at the null values (consequently they do not suffer from the Hauck-Donner effect).
score.stat(object, ...) score.stat.vlm(object, values0 = 0, subset = NULL, omit1s = TRUE, all.out = FALSE, iterate = TRUE, trace = FALSE, ...)
object, values0, subset |
Same as in |
omit1s, all.out, iterate |
Same as in |
trace |
Same as in |
... |
Ignored for now. |
The (Rao) score test
(also known as the Lagrange multiplier test in econometrics)
is a third general method for
hypothesis testing under a likelihood-based framework
(the others are the likelihood ratio test and
Wald test; see lrt.stat
and
wald.stat
).
Asymptotically, the three tests are equivalent.
The Wald test is not invariant to parameterization, and
the usual Wald test statistics computed at the estimates
make it vulnerable to the Hauck-Donner effect
(HDE; see hdeff
).
This function is similar to wald.stat
in that
one coefficient is set to 0 (by default) and the other
coefficients are iterated by IRLS to get their MLE subject to this
constraint.
By default the
signed square root of the
Rao score statistics are returned.
If all.out = TRUE
then a list is returned with the
following components:
score.stat
the score statistic,
SE0
the standard error of that coefficient,
values0
the null values.
Approximately, the default score statistics output are
standard normal random variates if each null hypothesis is true.
See wald.stat.vlm
.
Thomas W. Yee
wald.stat
,
lrt.stat
,
summaryvglm
,
summary.glm
,
anova.vglm
,
vglm
,
hdeff
.
set.seed(1) pneumo <- transform(pneumo, let = log(exposure.time), x3 = rnorm(nrow(pneumo))) (pfit <- vglm(cbind(normal, mild, severe) ~ let + x3, propodds, data = pneumo)) score.stat(pfit) # No HDE here; should be similar to the next line: coef(summary(pfit))[, "z value"] # Wald statistics computed at the MLE summary(pfit, score0 = TRUE)