feglm.nb {alpaca} | R Documentation |
Efficiently fit negative binomial glm's with high-dimensional k
-way fixed effects
Description
feglm.nb
can be used to fit negative binomial generalized linear models with many
high-dimensional fixed effects (see feglm
).
Usage
feglm.nb(
formula = NULL,
data = NULL,
weights = NULL,
beta.start = NULL,
eta.start = NULL,
init.theta = NULL,
link = c("log", "identity", "sqrt"),
control = NULL
)
Arguments
formula , data , weights , 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 |
Details
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.
Value
The function feglm.nb
returns a named list of class "feglm"
.
References
Gaure, S. (2013). "OLS with Multiple High Dimensional Category Variables". Computational Statistics and Data Analysis. 66.
Marschner, I. (2011). "glm2: Fitting generalized linear models with convergence problems". The R Journal, 3(2).
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.