https://github.com/tmsalab/iccbeta

A function and vignettes for computing an intraclass correlation described in Aguinis & Culpepper (2015) <doi:10.1177/1094428114563618>. This package quantifies the share of variance in a dependent variable that is attributed to group heterogeneity in slopes.
https://github.com/tmsalab/iccbeta

armadillo correlation intraclass-correlation r rcpp rcpparmadillo

Last synced: 18 days ago
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A function and vignettes for computing an intraclass correlation described in Aguinis & Culpepper (2015) <doi:10.1177/1094428114563618>. This package quantifies the share of variance in a dependent variable that is attributed to group heterogeneity in slopes.

• Host: GitHub
• URL: https://github.com/tmsalab/iccbeta
• Owner: tmsalab
• Created: 2017-04-08T04:01:55.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2019-11-06T02:23:22.000Z (about 5 years ago)
• Last Synced: 2024-09-21T14:19:44.724Z (3 months ago)
• Topics: armadillo, correlation, intraclass-correlation, r, rcpp, rcpparmadillo
• Language: R
• Homepage: https://tmsalab.github.io/iccbeta
• Size: 108 KB
• Stars: 2
• Watchers: 3
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.Rmd

Awesome Lists containing this project

README

        
---

output: github_document

---

```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "README-"

)

```

# iccbeta: Intraclass Correlation

[![Build Status](https://travis-ci.org/tmsalab/iccbeta.svg)](https://travis-ci.org/tmsalab/iccbeta)

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[![CRAN Status](https://cranchecks.info/badges/worst/iccbeta)](https://cran.r-project.org/web/checks/check_results_iccbeta.html)

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[![Coverage status](https://codecov.io/gh/tmsalab/iccbeta/branch/master/graph/badge.svg)](https://codecov.io/github/tmsalab/iccbeta?branch=master)

A function and vignettes for computing an intraclass correlation

described in Aguinis & Culpepper (in press). `iccbeta` quantifies the

share of variance in a dependent variable that is attributed to group

heterogeneity in slopes.

## Installation

You can install `iccbeta` from CRAN using:

```{r cran-installation, eval = FALSE}

install.packages("iccbeta")

```

Or, you can be on the cutting-edge development version on GitHub using:

```{r gh-installation, eval = FALSE}

if(!requireNamespace("devtools")) install.packages("devtools")

devtools::install_github("tmsalab/iccbeta")

```

## Usage

To use the `iccbeta` package, load it into _R_ using:

```{r example, message = FALSE}

library("iccbeta")

```

From there, calling the `icc_beta()` function with either a `lmer()`

model object or the desired individual components will

compute the intraclass correlation:

```{r sample-call, eval = FALSE}

# Automatically calculate icc from model

results_model = icc_beta()

# Calculate icc from individual terms.

results_component = icc_beta(X, l2id, T, vy)

```

## Authors

Steven Andrew Culpepper and Herman Aguinis

## Citing the `iccbeta` package

To ensure future development of the package, please cite `iccbeta`

package if used during an analysis or simulation studies. Citation information

for the package may be acquired by using in *R*:

```{r, eval = FALSE}

citation("iccbeta")

```

## License

GPL (>= 2)