Rao's Score Test Statistics Evaluated at the Null Values
Generic function that computes Rao's score test statistics evaluated at the null values.
score.stat(object, ...)
score.stat.vlm(object, values0 = 0, subset = NULL, omit1s = TRUE,
all.out = FALSE, orig.SE = FALSE, iterate.SE = TRUE,
iterate.score = TRUE, trace = FALSE, ...)object, values0, subset |
Same as in |
omit1s, all.out |
Same as in |
orig.SE, iterate.SE |
Same as in |
iterate.score |
Logical. The score vector is evaluated at one value of
|
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.
The SE is almost always based on the expected information matrix
(EIM) rather than the OIM, and for some models
the EIM and OIM coincide.
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.
Altogether,
by the eight combinations of iterate.SE, iterate.score
and orig.SE,
there are six different variants of the Rao score statistic
that can be returned because the score vector has 2 and
the SEs have 3 subvariants.
See wald.stat.vlm.
Thomas W. Yee
set.seed(1)
pneumo <- transform(pneumo, let = log(exposure.time),
x3 = rnorm(nrow(pneumo)))
(pfit <- vglm(cbind(normal, mild, severe) ~ let + x3, propodds, 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.