feglm.nb: Efficiently fit negative binomial glm's with high-dimensional...

In amrei-stammann/alpaca: Fit GLM's with High-Dimensional k-Way Fixed Effects

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 feglm.
init.theta	an optional initial value for the theta parameter (see glm.nb).
link	the link function. Must be one of "log", "sqrt", or "identity".

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.

See Also

glm.nb, feglm

amrei-stammann/alpaca documentation built on Sept. 26, 2024, 7:19 a.m.