Skip to contents

Check overdispersion (and underdispersion) of GL(M)M's

Source: R/check_overdispersion.R
check_overdispersion.Rd

check_overdispersion() checks generalized linear (mixed) models for overdispersion (and underdispersion).

Usage

check_overdispersion(x, ...)

# S3 method for class 'performance_simres'
check_overdispersion(x, alternative = "two.sided", ...)

Arguments

x

Fitted model of class merMod, glmmTMB, glm, or glm.nb (package MASS), or an object returned by simulate_residuals().

...

Arguments passed down to simulate_residuals(). This only applies for models with zero-inflation component, or for models of class glmmTMB from nbinom1 or nbinom2 family.

alternative

A character string specifying the alternative hypothesis. Can be one of "two.sided", "less", or "greater".

Value

A list with results from the overdispersion test, like chi-squared statistics, p-value or dispersion ratio.

Details

Overdispersion occurs when the observed variance is higher than the variance of a theoretical model. For Poisson models, variance increases with the mean and, therefore, variance usually (roughly) equals the mean value. If the variance is much higher, the data are "overdispersed". A less common case is underdispersion, where the variance is much lower than the mean.

Interpretation of the Dispersion Ratio

If the dispersion ratio is close to one, a Poisson model fits well to the data. Dispersion ratios larger than one indicate overdispersion, thus a negative binomial model or similar might fit better to the data. Dispersion ratios much smaller than one indicate underdispersion. A p-value < .05 indicates either overdispersion or underdispersion (the first being more common).

Overdispersion in Poisson Models

For Poisson models, the overdispersion test is based on the code from Gelman and Hill (2007), page 115.

Overdispersion in Negative Binomial or Zero-Inflated Models

For negative binomial (mixed) models or models with zero-inflation component, the overdispersion test is based simulated residuals (see simulate_residuals()).

Overdispersion in Mixed Models

For merMod- and glmmTMB-objects, check_overdispersion() is based on the code in the GLMM FAQ, section How can I deal with overdispersion in GLMMs?. Note that this function only returns an approximate estimate of an overdispersion parameter. Using this approach would be inaccurate for zero-inflated or negative binomial mixed models (fitted with glmmTMB), thus, in such cases, the overdispersion test is based on simulate_residuals() (which is identical to check_overdispersion(simulate_residuals(model))).

How to fix Overdispersion

Overdispersion can be fixed by either modeling the dispersion parameter, or by choosing a different distributional family (like Quasi-Poisson, or negative binomial, see Gelman and Hill (2007), pages 115-116).

Tests based on simulated residuals

For certain models, resp. model from certain families, tests are based on simulated residuals (see simulate_residuals()). These are usually more accurate for testing such models than the traditionally used Pearson residuals. However, when simulating from more complex models, such as mixed models or models with zero-inflation, there are several important considerations. Arguments specified in ... are passed to simulate_residuals(), which relies on DHARMa::simulateResiduals() (and therefore, arguments in ... are passed further down to DHARMa). The defaults in DHARMa are set on the most conservative option that works for all models. However, in many cases, the help advises to use different settings in particular situations or for particular models. It is recommended to read the 'Details' in ?DHARMa::simulateResiduals closely to understand the implications of the simulation process and which arguments should be modified to get the most accurate results.

References

  • Bolker B et al. (2017): GLMM FAQ.

  • Gelman, A., and Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge; New York: Cambridge University Press.

See also

Other functions to check model assumptions and and assess model quality: check_autocorrelation(), check_collinearity(), check_convergence(), check_heteroscedasticity(), check_homogeneity(), check_model(), check_outliers(), check_predictions(), check_singularity(), check_zeroinflation()

Examples

data(Salamanders, package = "glmmTMB")
m <- glm(count ~ spp + mined, family = poisson, data = Salamanders)
check_overdispersion(m)
#> # Overdispersion test
#> 
#>        dispersion ratio =    2.946
#>   Pearson's Chi-Squared = 1873.710
#>                 p-value =  < 0.001
#> 
#> Overdispersion detected.