bellreg: Bell regression model

In bellreg: Count Regression Models Based on the Bell Distribution

Description

Fits the Bell regression model to overdispersed count data.

Usage

bellreg(
  formula,
  data,
  approach = c("mle", "bayes"),
  hessian = TRUE,
  hyperpars = list(mu_beta = 0, sigma_beta = 10),
  ...
)

Arguments

formula	an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted.
data	an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which ypbp is called.
approach	approach to be used to fit the model (mle: maximum likelihood; bayes: Bayesian approach).
hessian	hessian logical; If TRUE (default), the hessian matrix is returned when approach="mle".
hyperpars	a list containing the hyperparameters associated with the prior distribution of the regression coefficients; if not specified then default choice is hyperpars = c(mu_beta = 0, sigma_beta = 10).
...	further arguments passed to either 'rstan::optimizing' or 'rstan::sampling'.

Value

bellreg returns an object of class "bellreg" containing the fitted model.

Examples

# ML approach:
mle <- bellreg(nf ~ lroll, data = faults, approach = "mle")
summary(mle)

# Bayesian approach:
bayes <- bellreg(nf ~ lroll, data = faults, approach = "bayes")
summary(bayes)

bellreg documentation built on July 1, 2020, 8:29 p.m.