simGLM: Generate an artificial data set for some GLM's with two-way...

In alpaca: Fit GLM's with High-Dimensional k-Way Fixed Effects

Generate an artificial data set for some GLM's with two-way fixed effects

Description

Constructs an artificial data set with n cross-sectional units observed for t time periods for logit, poisson, or gamma models. The “true” linear predictor (η) is generated as follows:

η = X β + α + γ,

where X consists of three independent standard normally distributed regressors. Both parameter referring to the unobserved heterogeneity (α and γ) are generated as iid. standard normal and the structural parameters are set to β = [1, - 1, 1]'.

Note: The poisson and gamma model are based on the logarithmic link function.

Usage

simGLM(n = NULL, t = NULL, seed = NULL, model = c("logit", "poisson", "gamma"))

Arguments

n	a strictly positive integer equal to the number of cross-sectional units.
t	a strictly positive integer equal to the number of time periods.
seed	a seed to ensure reproducibility.
model	a string equal to "logit", "poisson", or "gamma". Default is "logit".

Value

The function simGLM returns a data.frame with 6 variables.

See Also

feglm

alpaca documentation built on Aug. 10, 2022, 5:08 p.m.