# dcom: The COM-Poisson Distribution In compoisson: Conway-Maxwell-Poisson Distribution

## Description

Probability mass function and random generation for the COM-Poisson distribution for given values of the parameters.

## Usage

 ```1 2``` ``` dcom(x, lambda, nu, z = NULL) rcom(n, lambda, nu, log.z = NULL) ```

## Arguments

 `x` level to evaluate the PMF at `lambda` value of lambda parameter `nu` value of nu parameter `z` normalizing constant, computed if not specified `n` number of random values to return `log.z` natural log of z

## Details

Computes the probability mass function of the COM-Poisson distribution

f(x) = (1/Z) (lambda^x)/(x!^nu).

## Value

`dcom` gives the probability that a random COM-Poisson variable X takes value x. `rcom` gives a vector of `n` random values sampled from the COM-Poisson distribution.

Jeffrey Dunn

## References

Shmueli, G., Minka, T. P., Kadane, J. B., Borle, S. and Boatwright, P., “A useful distribution for fitting discrete data: Revival of the Conway-Maxwell-Poisson distribution,” J. Royal Statist. Soc., v54, pp. 127-142, 2005.

`com.loglikelihood`, `com.log.density`

## Examples

 ```1 2 3 4``` ``` data(insurance); fit = com.fit(Lemaire); dcom(0, fit\$lambda, fit\$nu, fit\$z); r = rcom(10, fit\$lambda, fit\$nu); ```

### Example output

```Loading required package: MASS
 0.9066425
```

compoisson documentation built on May 1, 2019, 11:17 p.m.