fit.MNB {MNB} | R Documentation |
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 <carrascojalmar@gmail.com>, Cristian M Villegas Lobos <master.villegas@gmail.com> and Lizandra C Fabio <lizandrafabio@gmail.com>
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. (2023). Diagnostic tools for a multivariate negative binomial model for fitting correlated data with overdispersion. Communications in Statistics - Theory and Methods, 52, 1833–1853.
Fabio, L. C., Villegas, C., Mamun, A. S., and Carrasco, J. M. F. (2025). Residual analysis for discrete correlated data in the multivariate approach. Brazilian Journal of Biometrics, 43, e43728.
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