DoF: Degrees of freedom extractor

In spaMM: Mixed-Effect Models, with or without Spatial Random Effects

Degrees of freedom extractor

Description

This extracts the number of degrees of freedom for a model, in the usual sense for likelihood-ratio tests: a count of number of fitted parameters, distinguishing different classes of parameters (see Value).

Usage

DoF(object)

Arguments

object

A fitted-model object, of class "HLfit".

Details

The output distinguishes counts of random-effect vs residual-dispersion parameters, following the conceptual distinction between effects that induce correlations between different levels of the resonse vs. observation-level effects. However, a residual-dispersion component can be declared as a random effect, so that the counts for logically equivalent models may differ according to the way a model was declared. For example if residual dispersion for an LLM is declared as an observation-level random effect while phi is fixed, the p_lambda component will include 1 df for what would otherwise be accounted by the p_rdisp component. A more involved case where the same contrast happens is when a negative-binomial model (with a residual-dispersion shape parameter) is declared as a Poisson-gamma mixture model (with a varaince parameter for the Gamma-distributed individual-level random effect).

Value

A vector with possible elements p_fixef, p_lambda, p_corrPars and p_rdisp for, respectively, the number of fixed-effect coefficients of the main-response model, the number of random-effect variance parameters, the number of random-effect correlation parameters, and the number of residual dispersion parameters (the latter being itself, for a mixed-effect residual-dispersion model, the sum of such components).

See Also

df.residual.HLfit; get_any_IC for extracting effective degrees of freedom considered in the model-selection literature; as_LMLT for access to the effective degrees of freedom considered in Satterthwaite's test and its extentions.

spaMM documentation built on Aug. 30, 2023, 1:07 a.m.