| 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 |
... |
Arguments to |
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.