R: Wald test statistics method

wald {asreml} | R Documentation

## Wald test statistics method

### Description

Function that calculates Wald test statistics for fixed effects terms of a fitted model. Presented as a pseudo analysis of variance (ANOVA) using incremental Wald statistics or conditional F-tests. The available method is for `asreml` class objects.

### Usage

    wald(object, ...)

### Arguments

`object` | An object of class `asreml`.
`...` | Arguments to `wald.asreml`

### Details

The method `wald.asreml()` produces two styles of analysis of variance table depending on the settings of `denDF` and `ssType`. If `denDF = "none"` and `ssType = "incremental"` (the defaults), a pseudo analysis of variance table is returned based on incremental sums of squares with rows corresponding to each of the fixed terms in the object, plus an additional row for the residual. The model sum of squares is partitioned into its fixed term components, and the sum of squares for each term listed in the table of Wald statistics is adjusted for the terms listed in the rows above. The denominator degrees of freedom are not computed and consequently Wald tests are provided.

If either `denDF` or `ssType` are not set at their default values, a data frame is returned that will include columns for the approximate denominator degrees of freedom (ddf) and incremental and conditional approximated F-statistics depending on the combination of options chosen. In all cases, `update.asreml` is called to complete calculations.

The principle used in determining the conditional tests is that a term cannot be adjusted for another term which encompasses it explicitly (for example, A:C cannot be adjusted for A:B:C) or implicitly (for example, REGION cannot be adjusted for LOCATION when locations are nested in regions although coded independently).

The numerator degrees of freedom (ndf) for each term is determined as the number of non-singular equations involved in the term. However, the calculation of the ddf is in general not trivial and is computationally expensive. Numerical derivatives require an extra evaluation of the mixed model equations for every variance parameter while algebraic derivatives require a large dense matrix, potentially in the order of the number of equations plus the number of observations. The calculations are suppressed by default.

### See Also

`wald.asreml`

[Package asreml version 4.2.0.480 Index]