# multicmpests: Bivariate COM-Poisson Parameter Estimation In multicmp: Flexible Modeling of Multivariate Count Data via the Multivariate Conway-Maxwell-Poisson Distribution

## Description

`multicmpests` computes the maximum likelihood estimates of a bivariate COM-Poisson distribution (based on the model described in Sellers et al. (2016)) for given count data and conducts a test for significant data dispersion, relative to a bivariate Poisson model. The bivariate Poisson case is addressed via the bivpois package by Karlis and Ntzoufras (2009).

## Usage

 `1` ```multicmpests(data, max = 100, startvalues = NULL) ```

## Arguments

 `data` A two-column dataset of counts. `max` Truncation term for infinite summation associated with the Z function. See Sellers et al. (2016) for details. `startvalues` A vector of starting values for maximum likelihood estimation. The values are read as follows: c(lambda, nu, p00, p10, p01, p11). The default is c(1,1, 0.25, 0.25, 0.25, 0.25).

## Value

`multicmpests` will return a list of four elements: \$par (Parameter Estimates), \$negll (Negative Log-Likelihood), \$LRTbpd (Dispersion Test Statistic), and \$pbpd (Dispersion Test P-Value).

## References

Sellers KF, Morris DS, Balakrishnan N (2016) Bivariate Conway-Maxwell-Poisson Distribution: Formulation, Properties, and Inference, Journal of Multivariate Analysis 150:152-168.

Karlis D., Ntzoufras I. (2009) bivpois: Bivariate Poisson Models Using the EM Algorithm, Version 0.50-3.1. http://cran.wustl.edu/web/packages/bivpois/index.html

## Examples

 ```1 2 3 4 5 6``` ``` x1 <- c(3,2,5,4,1) x2 <- c(0,4,1,0,1) ex.data <- cbind(x1,x2) # starting close to the optimum for sake of run time multicmpests(ex.data, startvalues = c(12.5 , 1.7 , 0, 0.25, 0.75, 0)) ```

multicmp documentation built on May 1, 2019, 8:08 p.m.