| 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. (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