Fit a distributional regression model with TMB

drmTMB() is the main model-fitting entry point. The current implementation supports univariate Gaussian location-scale models, fixed-effect univariate Student-t location-scale-shape models, fixed-effect lognormal location-scale models, Gamma mean-CV models for positive responses, fixed-effect beta mean-scale models for strict proportions, fixed-effect beta-binomial mean-overdispersion models for success counts, fixed-effect cumulative-logit ordinal location models, fixed-effect Poisson mean, zero-inflated Poisson, negative-binomial mean-dispersion, zero-inflated negative-binomial mean-dispersion, zero-truncated negative-binomial mean-dispersion, and hurdle negative-binomial mean-dispersion models for counts. Poisson and negative-binomial mu formulas may include standard R offset(log(exposure)) terms for exposure or effort, Gaussian random intercepts, independent numeric random slopes, and labelled or unlabelled correlated numeric random intercept-slope blocks in the location formula, known sampling covariance through meta_V(V = V) with meta_known_V(V = V) as a compatibility alias, residual-scale random intercepts and independent numeric random slopes in the scale formula, labelled mu/sigma random-intercept covariance blocks, and one or more group-level random-effect scale formulae such as sd(id) ~ x_group, plus intercept-only phylogenetic random effects and sd_phylo(species) ~ x_species direct-SD models in univariate Gaussian location formulas, matching bivariate Gaussian mu1/mu2 location formulas, and matching labelled bivariate Gaussian mu1/mu2/sigma1/sigma2 phylogenetic location-scale blocks, coordinate-based spatial random intercepts and one numeric coordinate-spatial slope in univariate Gaussian mu, fixed-effect bivariate Gaussian distributional models, and matched labelled bivariate Gaussian mu1/mu2, sigma1/sigma2, and same-response mu/sigma random-intercept covariance blocks, including the first all-four q=4 ordinary random-intercept covariance blocks and predictor-dependent q=2 ordinary or phylogenetic corpair() regressions. Bivariate Gaussian location formulas may be written explicitly as mu1 = y1 ~ ..., mu2 = y2 ~ ..., or with mvbind(y1, y2) ~ ... shorthand when both responses share the same location predictors.

Usage

drmTMB(
  formula,
  family = stats::gaussian(),
  data,
  weights = NULL,
  control = list(),
  ...
)

Arguments

formula: A drm_formula object created by drm_formula() or bf().
family: A response family, such as stats::gaussian(), student(), lognormal(), stats::Gamma() with link = "log", beta(), beta_binomial(), cumulative_logit(), stats::poisson() with link = "log", nbinom2(), truncated_nbinom2(), or biv_gaussian(). Adding zi ~ predictors to a Poisson or nbinom2() model fits the corresponding zero-inflated count model. Adding hu ~ predictors to a truncated_nbinom2() model fits a hurdle count model whose nonzero counts use the zero-truncated NB2 component. The current bivariate Gaussian engine also accepts family = c(gaussian(), gaussian()) and family = list(gaussian(), gaussian()).
data: A data frame.
weights: Optional non-negative likelihood weights. These are row log-likelihood multipliers, not known sampling variances. For meta-analytic sampling variance or covariance, use meta_V() in the model formula instead.
control: Optional list passed to stats::nlminb(), or a drm_control() object when optimizer settings and fitted-object storage choices should be supplied together.
...: Reserved for future model options.

Value

A drmTMB fit object.