nbglm | R Documentation |
Estimates a negative binomial generalized linear model.
nbglm(formula, offset = NULL, data, maxit = c(500, 1e+05), trace = T, theta.start = NULL)
formula |
A formula for the fixed effects part of the model. It should be in the form |
offset |
An offset to be added to the linear predictor. Default is |
data |
A data frame containing the variables declared in |
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 |
theta.start |
Numeric vector comprising initial parameter values for the vector of regression coefficients and the dispersion parameter |
Maximum likelihood estimation of a negative binomial GLM (the NB distribution is obtained as special case of the Poisson-Tweedie distribution when a = 0).
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
)
Mirko Signorelli
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
ptmixed
for the Poisson-Tweedie GLMM
data(df1, package = 'ptmixed') # estimate the model fit1 = nbglm(formula = y ~ group*time, data = df1) # view model summary summary(fit1)
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