dcom: The COM-Poisson Distribution

In jarad/compoisson: Conway-Maxwell-Poisson Distribution

Description

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

Usage

	dcom(x, lambda, nu, z = NULL)
        qcom(p, lambda, nu, log.z = NULL, lower.tail = TRUE, log.p = FALSE)
	rcom(n, lambda, nu, log.z = NULL)

Arguments

x	vector of (non-negative integer) quantiles.
p	vector of probabilities.
lambda	scalar value of lambda parameter
nu	scalar value of nu parameter
z	normalizing constant, computed if not specified
n	number of random values to return
log.z	natural log of z
log.p	if TRUE, probabilities p are given as log(p).
lower.tail	if TRUE (default), probabilities are P[X <= x], otherwise, P[X > x].

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. qcom gives the quantile function

Author(s)

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.

See Also

com.loglikelihood, com.log.density

Examples

	data(insurance);
	fit = com.fit(Lemaire);
	dcom(0, fit$lambda, fit$nu, fit$z);
	r = rcom(10, fit$lambda, fit$nu);

jarad/compoisson documentation built on May 18, 2019, 3:45 p.m.