VGAM (version 1.1-1)

wald.stat: Wald Test Statistics Evaluated at the Null Values


Generic function that computes Wald test statistics evaluated at the null values (consequently they do not suffer from the Hauck-Donner effect).


wald.stat(object, ...)
wald.stat.vlm(object, values0 = 0, subset = NULL, omit1s = TRUE,
          all.out = FALSE, iterate = TRUE, trace = FALSE,
          as.summary = FALSE, ...)



A vglm fit.


Numeric vector. The null values corresponding to the null hypotheses. Recycled if necessary.


Same as in hdeff.


Logical. Does one omit the intercepts? Because the default would be to test that each intercept is equal to 0, which often does not make sense or is unimportant, the intercepts are not tested by default. If they are tested then each linear predictor must have at least one coefficient (from another variable) to be estimated.


Logical. If TRUE then a list is returned containing various quantities such as the SEs, instead of just the Wald statistics.


Logical. If TRUE then IRLS iterations are performed to get MLEs of the other regression coefficients, subject to that one coefficient equal to the appropriate values0 value. If FALSE then the other regression coefficients have values obtained at the original fit. It is recommended that a TRUE be used as the answer tends to be more accurate. If the large model matrix only has one column and iterate = TRUE then an error will occur because there are no other regression coefficients to estimate.


Logical. If TRUE then some output is produced as the IRLS iterations proceed. The value NULL means to use the trace value of the fitted object; see vglm.control.


Logical. If TRUE then the usual (or unmodified) Wald statistics etc. are extracted from summary(object). These may suffer from the HDE; and the SEs are evaluated at the MLE of the original object so that effectively iterate = FALSE. If TRUE then most other arguments will be ignored or overwritten.

This argument may be renamed to as.glm because the Wald statistics will be computed in the same way as summary(glm()).

Ignored for now.


By default the signed square root of the Wald statistics whose SEs are computed at one each of the null values. If all.out = TRUE then a list is returned with the following components: wald.stat the Wald statistic, SE0 the standard error of that coefficient, values0 the null values. Approximately, the default Wald statistics output are standard normal random variates if each null hypothesis is true.


This function has not yet been thoroughly tested. Convergence failure is possible for some models applied to certain data sets; it is a good idea to set trace = TRUE to monitor convergence. For example, for a particular explanatory variable, the estimated regression coefficients of a non-parallel cumulative logit model (see cumulative) are ordered, and perturbing one coefficient might disrupt the order and create numerical problems.


By default, summaryvglm and most regression modelling functions such as summary.glm compute the standard errors (SEs) of the estimates at the MLE and not at 0. This make it vulnerable to the Hauck-Donner effect (HDE) (see hdeff). One solution is to compute the SEs at 0 (or more generally, at the values of the argument values0). This function does that. The two variants of Wald statistics are asymptotically equivalent; however in small samples there can be an appreciable difference, and the difference can be large if the estimates are near to the boundary of the parameter space.

None of the tests here are joint, hence the degrees of freedom is always unity. For a factor with more than 2 levels one can use anova.vglm to test for the significance of the factor. If iterate = FALSE then one retains the MLEs of the original fit for the values of the other coefficients, and replaces one coefficient at a time by the value 0 (or whatever specified by values0). One alternative would be to recompute the MLEs of the other coefficients after replacing one of the values; this is the default because iterate = TRUE. Just like the original IRLS iterations, note that the iterations here are not guaranteed to converge.

Almost all VGAM family functions use the EIM and not the OIM; this affects the resulting standard errors. Also, regularity conditions are assumed for the Wald, likelihood ratio and score tests; some VGAM family functions such as alaplace1 are experimental and do not satisfy such conditions, therefore naive inference is hazardous.

The default output of this function can be seen by setting wald0.arg = TRUE in summaryvglm.

See Also

lrt.stat, score.stat, summaryvglm, summary.glm, anova.vglm, vglm, hdeff, hdeffsev.


Run this code
pneumo <- transform(pneumo, let = log(exposure.time),
                            x3 = rnorm(nrow(pneumo)))
(fit <- vglm(cbind(normal, mild, severe) ~ let + x3,
             propodds, data = pneumo))
wald.stat(fit)  # No HDE here
summary(fit, wald0 = TRUE)  # See them here
coef(summary(fit))  # Usual Wald statistics evaluated at the MLE
wald.stat(fit, as.summary = TRUE)  # Same as previous line
# }

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