degreenet (version 1.3-6)

acmpmle: Conway Maxwell Poisson Modeling of Discrete Data

Description

Functions to Estimate the Conway Maxwell Poisson Discrete Probability Distribution via maximum likelihood.

Usage

acmpmle(x, cutoff = 1, cutabove = 1000, guess=c(7,3),
    method="BFGS", conc=FALSE, hellinger=FALSE, hessian=TRUE)

Value

theta

vector of MLE of the parameters.

asycov

asymptotic covariance matrix.

asycor

asymptotic correlation matrix.

se

vector of standard errors for the MLE.

conc

The value of the concentration index (if calculated).

Arguments

x

A vector of counts (one per observation).

cutoff

Calculate estimates conditional on exceeding this value.

cutabove

Calculate estimates conditional on not exceeding this value.

guess

Initial estimate at the MLE.

method

Method of optimization. See "optim" for details.

conc

Calculate the concentration index of the distribution?

hellinger

Minimize Hellinger distance of the parametric model from the data instead of maximizing the likelihood.

hessian

Calculate the hessian of the information matrix (for use with calculating the standard errors.

References

compoisson: Conway-Maxwell-Poisson Distribution, Jeffrey Dunn, 2008, R package version 0.3

See Also

ayulemle, awarmle, simcmp

Examples

# Simulate a Conway Maxwell Poisson distribution over 100
# observations with mean of 7 and variance of 3
# This leads to a mean of 1

set.seed(1)
s4 <- simcmp(n=100, v=c(7,3))
table(s4)

#
# Calculate the MLE and an asymptotic confidence
# interval for the parameters
#

acmpmle(s4)