glm.cmp {COMPoissonReg}R Documentation

COM-Poisson and Zero-Inflated COM-Poisson regression

Description

Fit COM-Poisson regression using maximum likelihood estimation. Zero-Inflated COM-Poisson can be fit by specifying a regression for the overdispersion parameter.

Usage

glm.cmp(formula.lambda, formula.nu = NULL, formula.p = NULL,
  beta.init = NULL, gamma.init = NULL, zeta.init = NULL, ...)

Arguments

formula.lambda

regression formula linked to log(lambda).

formula.nu

regression formula linked to log(nu). If NULL (the default), is taken to be intercept only.

formula.p

regression formula linked to logit(p). If NULL (the default), zero-inflation term is excluded from the model.

beta.init

initial values for regression coefficients of lambda.

gamma.init

initial values for regression coefficients of nu.

zeta.init

initial values for regression coefficients of p.

...

other model parameters, such as data.

Details

The COM-Poisson regression model is

y_i ~ CMP(lambda_i, nu_i), log lambda_i = x_i^T beta, log nu_i = s_i^T gamma.

The Zero-Inflated COM-Poisson regression model assumes that y_i is 0 with probability p_i or y_i^* with probability 1 - p_i, where

y_i^* ~ CMP(lambda_i, nu_i), log lambda_i = x_i^T beta, log nu_i = s_i^T gamma, log p_i = w_i^T zeta.

Value

glm.cmp produces an object of either class 'cmp' or 'zicmp', depending on whether zero-inflation is used in the model. From this object, coefficients and other information can be extracted.

Author(s)

Kimberly Sellers, Thomas Lotze, Andrew Raim

References

Kimberly F. Sellers & Galit Shmueli (2010). A Flexible Regression Model for Count Data. Annals of Applied Statistics, 4(2), 943-961.

Kimberly F. Sellers and Andrew M. Raim (2016). A Flexible Zero-Inflated Model to Address Data Dispersion. Computational Statistics and Data Analysis, 99, 68-80.


[Package COMPoissonReg version 0.7.0 Index]