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Functions to Estimate the Conway Maxwell Poisson Discrete Probability Distribution via maximum likelihood.
acmpmle(x, cutoff = 1, cutabove = 1000, guess=c(7,3),
method="BFGS", conc=FALSE, hellinger=FALSE, hessian=TRUE)
vector of MLE of the parameters.
asymptotic covariance matrix.
asymptotic correlation matrix.
vector of standard errors for the MLE.
The value of the concentration index (if calculated).
A vector of counts (one per observation).
Calculate estimates conditional on exceeding this value.
Calculate estimates conditional on not exceeding this value.
Initial estimate at the MLE.
Method of optimization. See "optim" for details.
Calculate the concentration index of the distribution?
Minimize Hellinger distance of the parametric model from the data instead of maximizing the likelihood.
Calculate the hessian of the information matrix (for use with calculating the standard errors.
compoisson: Conway-Maxwell-Poisson Distribution, Jeffrey Dunn, 2008, R package version 0.3
ayulemle, awarmle, simcmp
# 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)
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