comp_expected_values: Functions to Compute Various Expected Values for the...

In thomas-fung/mpcmp: Mean-Parametrized Conway-Maxwell Poisson (COM-Poisson) Regression

Functions to Compute Various Expected Values for the COM-Poisson Distribution

Description

Functions to approximate the various expected values for the COM-Poisson distribution via truncation. The standard COM-Poisson parametrization is being used here. The lambda and nu values are recycled to match the length of the longer one and that would determine the length of the results.

Usage

comp_mean_logfactorialy(lambda, nu, log.Z, summax = 100)

comp_mean_ylogfactorialy(lambda, nu, log.Z, summax = 100)

comp_means(lambda, nu, log.Z, summax = 100)

comp_variances(lambda, nu, log.Z, summax = 100)

comp_variances_logfactorialy(lambda, nu, log.Z, summax = 100)

Arguments

lambda, nu,	rate and dispersion parameters. Must be positives.
log.Z,	an optional vector specifying normalizing constant Z in log scale.
summax	maximum number of terms to be considered in the truncated sum. The default is to sum to 100.

Value

comp_mean_logfactorialy gives the mean of log(Y!).

comp_mean_ylogfactorialy gives the mean of ylog(Y!).

comp_means gives the mean of Y.

comp_variances gives the variance of Y.

comp_variances_logfactorialy gives the variance of log(Y!).

thomas-fung/mpcmp documentation built on June 13, 2022, 6:20 p.m.