feglm {capybara} | R Documentation |
GLM fitting with high-dimensional k-way fixed effects
Description
feglm
can be used to fit generalized linear models
with many high-dimensional fixed effects. The estimation procedure is based
on unconditional maximum likelihood and can be interpreted as a
“weighted demeaning” approach.
Remark: The term fixed effect is used in econometrician's sense of having intercepts for each level in each category.
Usage
feglm(
formula = NULL,
data = NULL,
family = gaussian(),
weights = NULL,
beta_start = NULL,
eta_start = NULL,
control = NULL
)
Arguments
formula |
an object of class |
data |
an object of class |
family |
the link function to be used in the model. Similar to
|
weights |
an optional string with the name of the 'prior weights'
variable in |
beta_start |
an optional vector of starting values for the structural
parameters in the linear predictor. Default is
|
eta_start |
an optional vector of starting values for the linear predictor. |
control |
a named list of parameters for controlling the fitting
process. See |
Details
If feglm
does not converge this is often 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
A named list of class "feglm"
. The list contains the following
fifteen elements:
coefficients |
a named vector of the estimated coefficients |
eta |
a vector of the linear predictor |
weights |
a vector of the weights used in the estimation |
hessian |
a matrix with the numerical second derivatives |
deviance |
the deviance of the model |
null_deviance |
the null deviance of the model |
conv |
a logical indicating whether the model converged |
iter |
the number of iterations needed to converge |
nobs |
a named vector with the number of observations used in the estimation indicating the dropped and perfectly predicted observations |
lvls_k |
a named vector with the number of levels in each fixed effects |
nms_fe |
a list with the names of the fixed effects variables |
formula |
the formula used in the model |
data |
the data used in the model after dropping non-contributing observations |
family |
the family used in the model |
control |
the control list used in the model |
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.
Examples
mod <- feglm(mpg ~ wt | cyl, mtcars, family = poisson(link = "log"))
summary(mod)
mod <- feglm(mpg ~ wt | cyl | am, mtcars, family = poisson(link = "log"))
summary(mod, type = "clustered")