fepoisson: Poisson model fitting high-dimensional with k-way fixed...

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

Poisson model fitting high-dimensional with k-way fixed effects

Description

A wrapper for feglm with family = poisson().

Usage

fepoisson(
  formula = NULL,
  data = NULL,
  weights = NULL,
  beta_start = NULL,
  eta_start = NULL,
  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.
control	a named list of parameters for controlling the fitting process. See feglm_control for details.

Value

A named list of class "feglm".

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), 500), ]

mod <- fepoisson(
  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.