wald.stat {VGAM} | R Documentation

## Wald Test Statistics Evaluated at the Null Values

### Description

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

### Usage

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

### Arguments

`object` | A `vglm` fit.
`values0` | Numeric vector. The null values corresponding to the null hypotheses. Recycled if necessary.
`subset` | Same as in `hdeff`.
`omit1s` | 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.
`all.out` | Logical. If `TRUE` then a list is returned containing various quantities such as the SEs, instead of just the Wald statistics.

`iterate`

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.

`trace` | 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`.

`as.summary`

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.

### Details

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`.

### Value

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.

### Warning

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.

### Author(s)

Thomas W. Yee

### See Also

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

### Examples

    set.seed(1)
    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

[Package VGAM version 1.1-1 Index]