rplnmle {degreenet}R Documentation

Rounded Poisson Lognormal Modeling of Discrete Data

Description

Functions to Estimate the Rounded Poisson Lognormal Discrete Probability Distribution via maximum likelihood.

Usage

rplnmle(x, cutoff = 1, cutabove = 1000, guess = c(-1,1),
    method = "BFGS", conc = FALSE, hellinger = FALSE, hessian=TRUE)

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.
conc Calculate the concentration index of the distribution?
method Method of optimization. See "optim" for details.
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.

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).

Note

See the working papers on http://www.csss.washington.edu/Papers for details

References

Jones, J. H. and Handcock, M. S. "An assessment of preferential attachment as a mechanism for human sexual network formation," Proceedings of the Royal Society, B, 2003, 270, 1123-1128.

See Also

aplnmle

Examples


# Simulate a Poisson Lognormal distribution over 100
# observations with lognormal mean of -1 and lognormal variance of 1
# This leads to a mean of 1

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

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

s4est <- rplnmle(s4)
s4est


[Package degreenet version 1.0 Index]