fit.MNB: Maximum likelihood estimation

In carrascojalmar/MNB: Diagnostic Tools for a Multivariate Negative Binomial Model

Maximum likelihood estimation

Description

Estimate parameters by quasi-Newton algorithms.

Usage

fit.MNB(star, formula, dataSet, tab = TRUE)

Arguments

star	Initial values for the parameters to be optimized over.
formula	The structure matrix of covariates of dimension n x p (in models that include an intercept x should contain a column of ones).
dataSet	data
tab	Logical. Print a summary of the coefficients, standard errors and p-value for class "MNB".

Details

Method "BFGS" is a quasi-Newton method, specifically that published simultaneously in 1970 by Broyden, Fletcher, Goldfarb and Shanno. This uses function values and gradients to build up a picture of the surface to be optimized.

Value

Returns a list of summary statistics of the fitted multivariate negative binomial model.

Author(s)

Jalmar M F Carrasco <[e-mail]>, Cristian M Villegas Lobos <[e-mail]> and Lizandra C Fabio <[e-mail]>

References

• Fabio, L., Paula, G. A., and de Castro, M. (2012). A Poisson mixed model with nonormal random effect distribution. Computational Statistics and Data Analysis, 56, 1499-1510.

• Fabio, L. C., Villegas, C., Carrasco, J. M. F., and de Castro, M. (2021). D Diagnostic tools for a multivariate negative binomial model for fitting correlated data with overdispersion. Communications in Statistics - Theory and Methods. https://doi.org/10.1080/03610926.2021.1939380.

Examples

data(seizures)
head(seizures)

star <-list(phi=1, beta0=1, beta1=1, beta2=1, beta3=1)

mod1 <- fit.MNB(formula=Y ~ trt + period +
trt:period + offset(log(weeks)), star=star, dataSet=seizures)

mod1

seizures49 <- seizures[-c(241,242,243,244,245),]

mod2 <- fit.MNB(formula=Y ~ trt + period +
trt:period + offset(log(weeks)), star=star, dataSet=seizures49)

mod2

carrascojalmar/MNB documentation built on May 15, 2022, 4:41 a.m.