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"),
  control = NULL
)

Arguments

formula	an object of class "formula": a symbolic description of the model to be fitted. formula must be of type y ~ x | k, where the second part of the formula refers to factors to be concentrated out. It is also possible to pass clustering variables to feglm as y ~ x | k | c.
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".
control	a named list of parameters for controlling the fitting process. See feglm_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
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

Examples

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

# subset trade flows to avoid fitting time warnings during check
set.seed(123)
trade_2006 <- trade_panel[trade_panel$year == 2006, ]
trade_2006 <- trade_2006[sample(nrow(trade_2006), 700), ]

mod <- fenegbin(
  trade ~ log_dist + lang + cntg + clny | exp_year + imp_year,
  trade_2006
)

summary(mod)

capybara documentation built on April 11, 2025, 5:41 p.m.