feglm.nb {alpaca} | R Documentation |
feglm.nb
can be used to fit negative binomial generalized linear models with many
high-dimensional fixed effects (see feglm
).
feglm.nb(formula = NULL, data = NULL, beta.start = NULL, eta.start = NULL, init.theta = NULL, link = c("log", "identity", "sqrt"), control = NULL)
formula, data, beta.start, eta.start, control |
see |
init.theta |
an optional initial value for the theta parameter (see |
link |
the link function. Must be one of |
If feglm.nb
does not converge this is usually a sign of linear dependence between one or
more regressors and a fixed effects category. In this case, you should carefully inspect your
model specification.
The function feglm.nb
returns a named list of class "feglm"
.
Gaure, S. (2013). "OLS with Multiple High Dimensional Category Variables". Computational Statistics and Data Analysis. 66.
Stammann, A., F. Heiss, and D. McFadden (2016). "Estimating Fixed Effects Logit Models with Large Panel Data". Working paper.
Stammann, A. (2018). "Fast and Feasible Estimation of Generalized Linear Models with High-Dimensional k-Way Fixed Effects". ArXiv e-prints.