HLM7-style hierarchical linear models in R
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
R
data
man
.Rbuildignore
.travis.yml
DESCRIPTION
NAMESPACE
README.md

README.md

README

Build Status

  • hlmer R package
  • Version 0.1.0

Author

  • Jeffrey A. Dahlke

Description

The hlmer package is a companion to the lme4 and lmerTest packages that automates the formulation of mixed-effects linear equations from user-supplied model characteristics. Output of the hlmer() function is meant to approximate the output of the HLM7 program by Raudenbush, Bryk, and Congdon (2014; Scientific Software International, Inc.) by supplementing lmer() output with statistics such as reliability estimates for random effects, intraclass correlation coefficients (ICCs), and chi-square tests for random-effect variance.

Installation

To install hlmer, make sure you have the devtools library (and its dependencies) installed. Run the following to install those libraries:

install.packages("devtools", dependencies = TRUE)

Once devtools is installed, run the following command to install the hlmer library from GitHub:

devtools::install_github("jadahlke/hlmer")

With hlmer installed, you can load the library:

library(hlmer)

Take hlmer for a test drive! Here's the code to replicate the analysis in Raudenbush and Bryk's (2002) Table 4.5:

hlmer(y_lvl1 = "MATHACH", cluster = "ID", x_lvl1 = "SES", x_lvl2 = "SECTOR", center_lvl1 = "cluster", y_lvl2 = "all", y_lvl1means = "all", model_type = 4, data = hsb)