Regression simulation function
This package can be directly installed through CRAN:
The development version of the package can be installed by using the devtools package.
Introduction to the simglm package
The best way to become oriented with the
simglm package is through the package vignette. There are two ways to get to the vignettes (both will open a browser to view the vignette). Below is an example loading the "Intro" vignette directly:
browseVignettes() vignette("Intro", package = "simglm")
Note: If you install the development version of the package, you may need to tell R to build the vignettes when installing the
simglm package by doing the following:
install_github("lebebr01/simglm", build_vignettes = TRUE)
A flexible suite of functions to simulate nested data.
Currently supports the following features:
- Longitudinal data simulation
- Three levels of nesting
- Specification of distribution of random components (random effects and random error)
- Specification of serial correlation
- Specification of the number of variables
- Ability to add time-varying covariates
- Specify the mean and variance of fixed covariate variables
- Factor variable simulation
- Ordinal variable simulation
- Generation of mixture normal distributions
- Cross sectional data simulation
- Single level simulation
- Power by simulation
- Vary parameters for a factorial simulation design.
- Can vary model fitted to the data to misspecify directly.
- Simulation of missing data
- Include other distributions for covariate simulation.
- Continuous, Logistic (dichotomous), and Poisson (count) outcome variables.
- Cross classified simulation and power
Bugs and feature requests are welcomed. Please track these on GitHub here: https://github.com/lebebr01/simglm/issues. I'm also open to pull requests.