feglm {alpaca}  R Documentation 
feglm
can be used to fit generalized linear models with many highdimensional fixed
effects. The estimation procedure is based on unconditional maximum likelihood and can be
interpreted as a “weighted demeaning” approach that combines the work of Gaure (2013) and
Stammann et. al. (2016). For technical details see Stammann (2018). The routine is well suited
for large data sets that would be otherwise infeasible to use due to memory limitations.
Remark: The term fixed effect is used in econometrician's sense of having intercepts for each level in each category.
feglm( formula = NULL, data = NULL, family = binomial(), beta.start = NULL, eta.start = NULL, control = NULL )
formula 
an object of class 
data 
an object of class 
family 
a description of the error distribution and link function to be used in the model.
Similar to 
beta.start 
an optional vector of starting values for the structural parameters in the linear predictor. Default is β = 0. 
eta.start 
an optional vector of starting values for the linear predictor. 
control 
a named list of parameters for controlling the fitting process. See

If feglm
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
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 HighDimensional kWay Fixed Effects". ArXiv eprints.
# Generate an artificial data set for logit models library(alpaca) data < simGLM(1000L, 20L, 1805L, model = "logit") # Fit 'feglm()' mod < feglm(y ~ x1 + x2 + x3  i + t, data) summary(mod)