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

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
mod <- fenegbin(mpg ~ wt | cyl, mtcars)
summary(mod)
#> Formula: mpg ~ wt | cyl
#> <environment: 0x592389217108>
#> 
#> Family: Negative Binomial(3272.74)
#> 
#> Estimates:
#> 
#> |    | Estimate | Std. Error | z value | Pr(>|z|) |
#> |----|----------|------------|---------|----------|
#> | wt |  -0.1799 |     0.0718 | -2.5054 | 0.0122 * |
#> 
#> Significance codes: *** 99.9%; ** 99%; * 95%; . 90%
#> 
#> Number of observations: Full 32; Missing 0; Perfect classification 0 
#> 
#> Number of Fisher Scoring iterations: 1
#> Number of outer iterations: 2
#> theta= 3272.74, std. error= 4649.04