Overdispersed count and proportion models (aods3-free)

Fits Poisson, quasi-Poisson, negative binomial, binomial or quasi-binomial GLMs with a tidy interface so callers never need aods3, and need MASS only when family = "negbin" (Suggests). Designed for the abundance / richness models in Moses et al. (2021), which switched to negative binomial after detecting overdispersion.

Usage

model_count(
  data,
  formula,
  family = c("poisson", "quasipoisson", "negbin", "binomial", "quasibinomial"),
  weights = NULL,
  offset = NULL,
  ...
)

# S3 method for class 'td_count_model'
tidy(x, ...)

# S3 method for class 'td_count_model'
glance(x, ...)

Arguments

data: A data frame (for example a site table of abundance or Hill richness).
formula: A model formula, such as abundance ~ poly(elevation, 2) * litter.
family: One of "poisson", "quasipoisson", "negbin", "binomial", "quasibinomial".
weights, offset: Optional columns (bare names or expressions) passed to stats::glm() / MASS::glm.nb().
...: Additional arguments forwarded to the fitter.
x: A td_count_model from model_count().

Value

A td_count_model list with fit, family, formula, data, and dispersion diagnostics. generics::tidy() / generics::glance() methods are provided as tidy.td_count_model() and glance.td_count_model().

References

Moses, J., Fayle, T. M., Novotny, V. and Klimes, P. (2021). Elevation and leaf litter interact in determining the structure of ant communities on a tropical mountain. Biotropica, 53, 906-919. doi:10.1111/btp.12914

Examples

ants <- png_ants |>
  dplyr::group_by(site, elevation) |>
  dplyr::summarise(richness = dplyr::n_distinct(species), .groups = "drop")
fit <- model_count(ants, richness ~ poly(elevation, 2), family = "poisson")
tidy(fit)
#> # A tibble: 3 x 5
#>   term                estimate std.error statistic   p.value
#>   <chr>                  <dbl>     <dbl>     <dbl>     <dbl>
#> 1 (Intercept)            2.95      0.112     26.3  2.32e-152
#> 2 poly(elevation, 2)1   -1.22      0.277     -4.39 1.13e-  5
#> 3 poly(elevation, 2)2   -0.486     0.241     -2.02 4.38e-  2
glance(fit)
#> # A tibble: 1 x 8
#>   family dispersion overdispersed null.deviance deviance   aic df.residual  nobs
#>   <chr>       <dbl> <lgl>                 <dbl>    <dbl> <dbl>       <int> <int>
#> 1 poiss~       1.35 FALSE                  27.9     2.84  32.8           2     5