pbirth: Fit Overdispersed Count Data as a Birth Process

In event: Event History Procedures and Models

Description

pbirth fits binomial, binomial exponential, binomial logistic, binomial total, Poisson, Poisson exponential, negative binomial, gen(eralized) negative binomial, and generalized negative binomial processes as a birth process.

Usage

pbirth(frequencies, p, intensity="negative binomial",
	type="spectral decomposition", print.level=0, ndigit=10,
	gradtol=0.00001, steptol=0.00001, fscale=1, iterlim=100,
	typsize=abs(p), stepmax=10*sqrt(p%*%p))

Arguments

frequencies	Vector of frequencies or a matrix with each row a different series of frequencies.
p	Vector of initial estimates.
intensity	The intensity function of the process: binomial, binomial exdponential, binomial logistic, binomial total, Poisson, Poisson exponential, negative binomial, or gen(eralized) negative binomial.
type	Algorithm used for matrix exponentiation: spectral decomposition or series approximation.
print.level	nlm control options.
ndigit	nlm control options.
gradtol	nlm control options.
steptol	nlm control options.
iterlim	nlm control options.
fscale	nlm control options.
typsize	nlm control options.
stepmax	nlm control options.

Author(s)

J.K. Lindsey

References

Faddy, M.J. and Fenlon, J.S. (1999) Stochastic modelling of the invasion process of nematodes in fly larvae. Applied Statistics 48: 31-37.

Examples

y <- rnbinom(100,2,0.6)
fr <- tabulate(y)
pbirth(fr, p=log(-log(0.7)), intensity="Poisson", type="series")
pbirth(fr, p=c(log(-log(0.7)),log(5)),
	intensity="negative binomial", type="series")
pbirth(fr, p=c(log(-log(0.7)),log(5),-1),
	intensity="gen negative binomial", type="series")

event documentation built on May 2, 2019, 4:07 a.m.