marginal.gctsc: Marginal Models for Copula Time Series

Description

The following marginal models are currently available:

poisson.marg(link = "log"): Poisson distribution.
negbin.marg(link = "log"): Negative binomial distribution.
binom.marg(link = "logit", size): Binomial distribution with fixed number of trials.
bbinom.marg(link = "logit", size): Beta-binomial with overdispersion.
zip.marg(link = "log"): Zero-inflated Poisson model.
zib.marg(link = "logit", size): Zero-inflated binomial.
zibb.marg(link = "logit", size): Zero-inflated beta-binomial with separate covariates for zero inflation.

Usage

poisson.marg(link = "identity", lambda.lower = NULL, lambda.upper = NULL)
binom.marg(link = "logit", size = NULL, lambda.lower = NULL, lambda.upper = NULL)
zib.marg(link = "logit", size = NULL, lambda.lower = NULL, lambda.upper = NULL)
negbin.marg(link = "identity", lambda.lower = NULL, lambda.upper = NULL)
zip.marg(link = "identity", lambda.lower = NULL, lambda.upper = NULL)
bbinom.marg(link = "logit", size, lambda.lower = NULL, lambda.upper = NULL)
zibb.marg(link = "logit", size,  lambda.lower = NULL, lambda.upper = NULL)

Value

A list of class "marginal.gctsc" representing the marginal model.

Arguments

link: The link function for the mean (e.g., "log", "logit", "identity").
lambda.lower: Optional lower bounds on parameters.
lambda.upper: Optional upper bounds on parameters.
size: Number of trials (for binomial-type models).

Details

These functions define the marginal distributions used in copula-based count time series models.

Each marginal constructor returns an object of class "marginal.gctsc" which defines:

start: a function to compute starting values.
npar: number of parameters.
bounds: truncation bounds on the latent Gaussian.

These marginals are designed to work with gctsc() and its related methods.

References

Cribari-Neto, F. and Zeileis, A. (2010). Beta regression in R. Journal of Statistical Software, 34(2): 1–24.

Ferrari, S.L.P. and Cribari-Neto, F. (2004). Beta regression for modeling rates and proportions. Journal of Applied Statistics, 31(7): 799–815.

Masarotto, G. and Varin, C. (2012). Gaussian copula marginal regression. Electronic Journal of Statistics, 6: 1517–1549.

Examples

Run this code

poisson.marg(link = "identity")
zibb.marg(link = "logit", size = 24)

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