rplnmle {degreenet}

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(0.6,1.2),
    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 papers on https://handcock.github.io/?q=Holland 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