sigma.glmmTMB: Extract residual standard deviation or dispersion parameter

Description Usage Arguments Details References

View source: R/VarCorr.R

Description

For Gaussian models, sigma returns the value of the residual standard deviation; for other families, it returns the dispersion parameter, however it is defined for that particular family. See details for each family below.

Usage

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## S3 method for class 'glmmTMB'
sigma(object, ...)

Arguments

object

a “glmmTMB” fitted object

...

(ignored; for method compatibility)

Details

The value returned varies by family:

gaussian

returns the maximum likelihood estimate of the standard deviation (i.e., smaller than the results of sigma(lm(...)) by a factor of (n-1)/n)

nbinom1

returns a dispersion parameter (usually denoted alpha as in Hardin and Hilbe (2007)): such that the variance equals mu(1+alpha).

nbinom2

returns a dispersion parameter (usually denoted theta or k); in contrast to most other families, larger theta corresponds to a lower variance which is mu(1+mu/theta).

Gamma

Internally, glmmTMB fits Gamma responses by fitting a mean and a shape parameter; sigma is estimated as (1/sqrt(shape)), which will typically be close (but not identical to) that estimated by stats:::sigma.default, which uses sqrt(deviance/df.residual)

beta

returns the value of phi, where the conditional variance is mu*(1-mu)/(1+phi) (i.e., increasing phi decreases the variance.) This parameterization follows Ferrari and Cribari-Neto (2004) (and the betareg package):

betabinomial

This family uses the same parameterization (governing the Beta distribution that underlies the binomial probabilities) as beta.

genpois

returns the index of dispersion phi^2, where the variance is mu*phi^2 (Consul & Famoye 1992)

compois

returns the value of 1/nu, When nu=1, compois is equivalent to the Poisson distribution. There is no closed form equation for the variance, but it is approximately undersidpersed when 1/nu <1 and approximately oversidpersed when 1/nu>1. In this implementation, mu is exactly the mean (Huang 2017), which differs from the COMPoissonReg package (Sellers & Lotze 2015).

tweedie

returns the value of phi, where the variance is phi*mu^p. The value of p can be extracted using the internal function glmmTMB:::.tweedie_power.

The most commonly used GLM families (binomial, poisson) have fixed dispersion parameters which are internally ignored.

References


glmmTMB documentation built on July 20, 2021, 9:06 a.m.