fenegbin: Negative Binomial model fitting with high-dimensional k-way...

In capybara: Fast and Memory Efficient Fitting of Linear Models with High-Dimensional Fixed Effects

Negative Binomial model fitting with high-dimensional k-way fixed effects

Description

A routine that uses the same internals as feglm.

Usage

fenegbin(
  formula = NULL,
  data = NULL,
  weights = NULL,
  beta_start = NULL,
  eta_start = NULL,
  init_theta = NULL,
  link = c("log", "identity", "sqrt"),
  offset = NULL,
  control = NULL
)

Arguments

formula	an object of class "formula": a symbolic description of the model to be fitted. formula must be of type response ~ slopes | fixed_effects | cluster.
data	an object of class "data.frame" containing the variables in the model. The expected input is a dataset with the variables specified in formula and a number of rows at least equal to the number of variables in the model.
weights	an optional string with the name of the prior weights variable in data.
beta_start	an optional vector of starting values for the structural parameters in the linear predictor. Default is \boldsymbol{\beta} = \mathbf{0}.
eta_start	an optional vector of starting values for the linear predictor.
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".
offset	an optional formula or numeric vector specifying an a priori known component to be included in the linear predictor. If a formula, it should be of the form ~ variable.
control	a named list of parameters for controlling the fitting process. See fit_control for details.

Value

A named list of class "feglm". The list contains the following eighteen 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
theta	the estimated theta parameter
iter_outer	the number of outer iterations
conv_outer	a logical indicating whether the outer loop converged
nobs	a named vector with the number of observations used in the estimation indicating the dropped and perfectly predicted observations
fe_levels	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

Examples

# check the felm examples for the details about clustered standard errors

ross2004_subset <- ross2004[ross2004$year == 1999, ]
ross2004_subset <- ross2004_subset[ross2004_subset$ltrade >
  quantile(ross2004_subset$ltrade, 0.75), ]

fit <- fenegbin(ltrade ~ ldist | ctry1, ross2004_subset)

summary(fit)

capybara documentation built on June 29, 2026, 5:07 p.m.