A list containing the following elements: function's call (call);
maximum likelihood estimate (mle); value of the
loglikelihood at the mle (logl); convergence value (if 0, the optimization converged);
the observed Fisher information (fisher.info) and the starting values
used in the optimization (theta.init)
Arguments
formula
A formula for the fixed effects part of the model. It should be in the form y ~ x1 + x2
offset
An offset to be added to the linear predictor. Default is NULL.
data
A data frame containing the variables declared in formula.
maxit
Vector containing the maximum number of iterations used in optim by
the BFGS method and, if this fails, by the Nelder-Mead method
trace
Logical value. If TRUE, additional information is printed during the optimization. Default is TRUE.
theta.start
Numeric vector comprising initial parameter values for the
vector of regression coefficients and the dispersion parameter
Author
Mirko Signorelli
Details
Maximum likelihood estimation of a negative binomial GLM
(the NB distribution is obtained as special case of the Poisson-Tweedie distribution when a = 0).
References
Signorelli, M., Spitali, P., Tsonaka, R. (2021). Poisson-Tweedie
mixed-effects model: a flexible approach for the analysis of longitudinal RNA-seq
data. Statistical Modelling, 21 (6), 520-545. URL: https://doi.org/10.1177/1471082X20936017