tidy.mixpoissonreg

tidy

A <code>mixpoissonreg</code> object.

Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.

conf.int

The confidence level to use for the confidence interval if conf.int = TRUE.
Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.

conf.level

Additional arguments. Currently not used.

Tidy returns a <code><a rd-options="tibble:tibble" href="/link/tibble%3A%3Atibble()?package=mixpoissonreg&version=1.0.0&to=tibble%3Atibble" data-mini-rdoc="tibble:tibble::tibble::tibble()">tibble::tibble()</a></code> containing
informations on the coefficients of the model, such as the estimated parameters,
standard errors, z-statistics and p-values. Additionally, it may return confidence
intervals for the model parameters.

Fits mixed Poisson regression models (Poisson-Inverse Gaussian or Negative-Binomial) on data sets with response variables being count data. The models can have varying precision parameter, where a linear regression structure (through a link function) is assumed to hold on the precision parameter. The Expectation-Maximization algorithm for both these models (Poisson Inverse Gaussian and Negative Binomial) is an important contribution of this package. Another important feature of this package is the set of functions to perform global and local influence analysis. See Barreto-Souza and Simas (2016) <doi:10.1007/s11222-015-9601-6> for further details.

Alexandre B. Simas

mixpoissonreg

Mixed Poisson Regression for Overdispersed Count Data

Wagner Barreto-Souza

tidy.mixpoissonreg function

Tidy returns a <code><a rd-options='tibble:tibble' href='tibble::tibble()'>tibble::tibble()</a></code> containing
informations on the coefficients of the model, such as the estimated parameters,
standard errors, z-statistics and p-values. Additionally, it may return confidence
intervals for the model parameters.

tidy.mixpoissonreg: Tidy a `mixpoissonreg` object

Description

Usage

Arguments

Value

See Also