Initial commit

.Rbuildignore

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+^.*\.Rproj$
+^\.Rproj\.user$
+^README\.Rmd$
+^README-.*\.png$
+^\.travis\.yml$

.gitignore

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+.Rproj.user
+.Rhistory
+.RData
+.Ruserdata
+src/*.o
+src/*.so
+src/*.dll

.travis.yml

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+# R for travis: see documentation at https://docs.travis-ci.com/user/languages/r
+
+language: R
+sudo: false
+cache: packages

DESCRIPTION

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+Package: iccbeta
+Type: Package
+Title: Multilevel model intraclass correlation for slope heterogeneity
+Version: 1.0
+Date: 2014-11-21
+Authors@R: c(person("Steven Andrew", "Culpepper", role = c("aut", "cph","cre"), email =
+    "sculpepp@illinois.edu"),person("Herman", "Aguinis", role = c("aut", "cph"), email =
+    "haguinis@indiana.edu"))
+Maintainer: Steven Andrew Culpepper <sculpepp@illinois.edu>
+Description: 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.
+License: GPL (>= 2)
+Imports: Rcpp (>= 0.11.1)
+LinkingTo: Rcpp (>= 0.11.1), RcppArmadillo
+Depends: R (>= 3.0.2), lme4
+Packaged: 2014-11-25 02:21:38 UTC; sculpepp
+Author: Steven Andrew Culpepper [aut, cph, cre],
+  Herman Aguinis [aut, cph]
+NeedsCompilation: yes
+Repository: CRAN
+Date/Publication: 2014-11-25 08:52:58

NAMESPACE

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+useDynLib(iccbeta)
+#import(RcppArmadillo)
+import(lme4)
+importFrom("Rcpp", evalCpp)
+exportPattern("^[[:alpha:]]+")
+

R/RcppExports.R

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+# This file was generated by Rcpp::compileAttributes
+# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
+
+#' @title Compute ICCbeta intraclass correlation
+#' @description Computes the proportion of variance attributed to group heterogeneity in slopes as described by Aguinis & Culpepper (2015).
+#' The function uses lmer model information. 
+#' @usage icc_beta(X,l2id,T,vy)
+#' @param X The design \code{matrix} of fixed effects from a lmer model.  
+#' @param l2id A \code{vector} that identifies group membership. The vector must be coded as a sequence from 1 to J, the number of groups.
+#' @param T A \code{matrix} of the estimated variance-covariance matrix of a lmer model fit.
+#' @return vy The variance of the dependent variable.
+#' @author Steven A Culpepper
+NULL
+
+icc_beta <- function(X, l2id, T, vy) {
+    .Call('iccbeta_icc_beta', PACKAGE = 'iccbeta', X, l2id, T, vy)
+}
+

README.Rmd

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+---
+output: github_document
+---
+
+<!-- README.md is generated from README.Rmd. Please edit that file -->
+
+```{r, echo = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  fig.path = "README-"
+)
+```
+
+[![Travis-CI Build Status](https://travis-ci.org/tmsalab/iccbeta.svg?branch=master)](https://travis-ci.org/tmsalab/iccbeta)[![CRAN RStudio mirror downloads](http://cranlogs.r-pkg.org/badges/iccbeta)](http://www.r-pkg.org/pkg/iccbeta)[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/iccbeta)](https://cran.r-project.org/package=iccbeta)
+
+# `iccbeta` R Package
+
+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.
+

README.md

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+
+<!-- README.md is generated from README.Rmd. Please edit that file -->
+[![Travis-CI Build Status](https://travis-ci.org/tmsalab/iccbeta.svg?branch=master)](https://travis-ci.org/tmsalab/iccbeta)[![CRAN RStudio mirror downloads](http://cranlogs.r-pkg.org/badges/iccbeta)](http://www.r-pkg.org/pkg/iccbeta)[![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/iccbeta)](https://cran.r-project.org/package=iccbeta)
+
+`iccbeta` R Package
+===================
+
+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.

iccbeta.Rproj

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+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 4
+Encoding: UTF-8
+
+RnwWeave: knitr
+LaTeX: pdfLaTeX
+
+BuildType: Package
+PackageUseDevtools: Yes
+PackageInstallArgs: --no-multiarch --with-keep.source

inst/CITATION

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+citHeader("To cite package iccbeta in publications use:")
+
+citEntry(entry="Article",
+      title = "An expanded decision making procedure for examining cross-level interaction effects with multilevel modeling",
+         author = personList(as.person("Herman Aguinis"),
+         as.person("Steven Andrew Culpepper")),
+	 journal = "Organization Research Methods",
+         year = "in press",
+         url = "http://mypage.iu.edu/~haguinis/pubs.html",
+         pdf = "http://mypage.iu.edu/~haguinis/ORMinpress.pdf",
+         textVersion = "Agunis, H. & Culpepper, S. A. (in press). An expanded decision making procedure for examining cross-level interaction effects with multilevel modeling. Organizational Research Methods.")
+

man/Hofmann.Rd

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+\name{Hofmann}
+\alias{Hofmann}
+\docType{data}
+\title{
+A multilevel dataset from Hofmann, Griffin, and Gavin (2000).
+}
+\description{
+A multilevel dataset from Hofmann, Griffin, and Gavin (2000). 
+}
+\usage{data(Hofmann)}
+\format{
+  A data frame with 1,000 observations and 7 variables.
+  \describe{
+    \item{\code{id}}{a numeric vector of group ids.}
+    \item{\code{helping}}{a numeric vector of the helping outcome variable construct.}
+    \item{\code{mood}}{a level 1 mood predictor.}
+    \item{\code{mood_grp_mn}}{a level 2 variable of the group mean of mood.}
+    \item{\code{cohesion}}{a level 2 covariate measuring cohesion.}
+    \item{\code{mood_grp_cent}}{group-mean centered mood predictor.}
+    \item{\code{mood_grd_cent}}{grand-mean centered mood predictor.}
+  }
+}
+%\details{
+%%  ~~ If necessary, more details than the __description__ above ~~
+%}
+\author{
+Steven Andrew Culpepper,
+Herman Aguinis
+
+Maintainer: Steven Andrew Culpepper <sculpepp@illinois.edu>
+}
+\source{
+Hofmann, D.A., Griffin, M.A., & Gavin, M.B. (2000). The application of hierarchical linear modeling to management research. In K.J. Klein, & S.W.J. Kozlowski (Eds.), Multilevel theory, research, and methods in organizations: Foundations, extensions, and new directions (pp. 467-511).  Hoboken, NJ: Jossey-Bass.
+}
+\references{
+Aguinis, H., & Culpepper, S.A. (in press). An expanded decision making procedure for examining cross-level interaction effects with multilevel modeling. \emph{Organizational Research Methods}. Available at: \url{http://mypage.iu.edu/~haguinis/pubs.html}
+}
+\seealso{
+\code{\link[lme4]{lmer}}, \code{\link{model.matrix}}, \code{\link[lme4]{VarCorr}}, \code{\link[RLRsim]{LRTSim}}, \code{\link{simICCdata}}
+}
+\examples{
+\dontrun{
+data(Hofmann)
+  require(lme4)
+
+  #Random-Intercepts Model
+  lmmHofmann0 = lmer(helping ~ (1|id),data=Hofmann)
+  vy_Hofmann = var(Hofmann[,'helping'])
+  #computing icca
+  VarCorr(lmmHofmann0)$id[1,1]/vy_Hofmann
+
+  #Estimating Group-Mean Centered Random Slopes Model, no level 2 variables
+  lmmHofmann1  <- lmer(helping ~ mood_grp_cent + (mood_grp_cent |id),data=Hofmann,REML=F)
+  X_Hofmann = model.matrix(lmmHofmann1)
+  P = ncol(X_Hofmann)
+  T1_Hofmann  = VarCorr(lmmHofmann1)$id[1:P,1:P]
+  #computing iccb
+  icc_beta(X_Hofmann,Hofmann[,'id'],T1_Hofmann,vy_Hofmann)$rho_beta
+
+  #Performing LR test
+  #Need to install 'RLRsim' package
+  library('RLRsim')
+  lmmHofmann1a  <- lmer(helping ~ mood_grp_cent + (1 |id),data=Hofmann,REML=F)
+  obs.LRT <- 2*(logLik(lmmHofmann1)-logLik(lmmHofmann1a))[1]
+  X <- getME(lmmHofmann1,"X")
+  Z <- t(as.matrix(getME(lmmHofmann1,"Zt")))
+  sim.LRT <- LRTSim(X, Z, 0, diag(ncol(Z)))
+  (pval <- mean(sim.LRT > obs.LRT))
+}
+  }
+\keyword{datasets}

man/icc_beta.Rd

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+\name{icc_beta}
+\alias{icc_beta}
+\docType{package}
+\title{
+Intraclass correlation used to assess variability of lower-order relationships across higher-order processes/units.
+}
+\description{
+A function and vignettes for computing the intraclass correlation described in Aguinis & Culpepper (in press). iccbeta quantifies the share of variance in an outcome variable that is attributed to heterogeneity in slopes due to higher-order processes/units. 
+}
+\usage{
+icc_beta(X, l2id, T, vy)	
+}
+
+\arguments{
+\item{X}{The design \code{matrix} of fixed effects from a lmer model.}
+
+\item{l2id}{A \code{vector} that identifies group membership. The vector must be coded as a sequence of integers from 1 to J, the number of groups.}
+
+\item{T}{A \code{matrix} of the estimated variance-covariance matrix of a lmer model fit.}
+
+\item{vy}{The variance of the outcome variable.}
+}
+
+\author{
+Steven Andrew Culpepper,
+Herman Aguinis
+
+Maintainer: Steven Andrew Culpepper <sculpepp@illinois.edu>
+}
+
+\references{
+Aguinis, H., & Culpepper, S.A. (in press). An expanded decision making procedure for examining cross-level interaction effects with multilevel modeling. \emph{Organizational Research Methods}. Available at: \url{http://mypage.iu.edu/~haguinis/pubs.html}
+}
+\seealso{
+\code{\link[lme4]{lmer}}, \code{\link{model.matrix}}, \code{\link[lme4]{VarCorr}}, \code{\link[RLRsim]{LRTSim}}, \code{\link{Hofmann}}, \code{\link{simICCdata}}
+}
+\examples{
+\dontrun{
+#Simulated Data Example from Aguinis & Culpepper (in press)
+data(simICCdata)
+  require(lme4)
+
+  #computing icca
+  vy = var(simICCdata$Y)
+  lmm0 <- lmer(Y ~ (1|l2id),data=simICCdata,REML=F)
+  VarCorr(lmm0)$l2id[1,1]/vy
+
+  #Estimating random slopes model
+  lmm1  <- lmer(Y~I(X1-m_X1)+I(X2-m_X2) +(I(X1-m_X1)+I(X2-m_X2)|l2id),data=simICCdata2,REML=F)
+  X = model.matrix(lmm1)
+  p=ncol(X)
+  T1  = VarCorr(lmm1) $l2id[1:p,1:p]
+  #computing iccb
+  #Notice '+1' because icc_beta assumes l2ids are from 1 to 30.
+  icc_beta(X,simICCdata2$l2id+1,T1,vy)$rho_beta
+
+#Hofmann et al. (2000) Example
+data(Hofmann)
+  require(lme4)
+
+  #Random-Intercepts Model
+  lmmHofmann0 = lmer(helping ~ (1|id),data=Hofmann)
+  vy_Hofmann = var(Hofmann[,'helping'])
+  #computing icca
+  VarCorr(lmmHofmann0)$id[1,1]/vy_Hofmann
+
+  #Estimating Group-Mean Centered Random Slopes Model, no level 2 variables
+  lmmHofmann1  <- lmer(helping ~ mood_grp_cent + (mood_grp_cent |id),data=Hofmann,REML=F)
+  X_Hofmann = model.matrix(lmmHofmann1)
+  P = ncol(X_Hofmann)
+  T1_Hofmann  = VarCorr(lmmHofmann1)$id[1:P,1:P]
+  #computing iccb
+  icc_beta(X_Hofmann,Hofmann[,'id'],T1_Hofmann,vy_Hofmann)$rho_beta
+
+  #Performing LR test
+  library('RLRsim')
+  lmmHofmann1a  <- lmer(helping ~ mood_grp_cent + (1 |id),data=Hofmann,REML=F)
+  obs.LRT <- 2*(logLik(lmmHofmann1)-logLik(lmmHofmann1a))[1]
+  X <- getME(lmmHofmann1,"X")
+  Z <- t(as.matrix(getME(lmmHofmann1,"Zt")))
+  sim.LRT <- LRTSim(X, Z, 0, diag(ncol(Z)))
+  (pval <- mean(sim.LRT > obs.LRT))
+}  
+}

man/iccbeta-package.Rd

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+\name{iccbeta-package}
+\alias{iccbeta-package}
+\alias{iccbeta}
+\docType{package}
+\title{
+Intraclass correlation used to assess variability of lower-order relationships across higher-order processes/units.
+}
+\description{
+A function and vignettes for computing the intraclass correlation described in Aguinis & Culpepper (in press). iccbeta quantifies the share of variance in an outcome variable that is attributed to heterogeneity in slopes due to higher-order processes/units. 
+}
+\details{
+\tabular{ll}{
+Package: \tab iccbeta\cr
+Type: \tab Package\cr
+Version: \tab 1.0\cr
+Date: \tab 2014-11-21\cr
+License: \tab GPL (>= 2)\cr
+}
+}
+\author{
+Steven Andrew Culpepper,
+Herman Aguinis
+
+Maintainer: Steven Andrew Culpepper <sculpepp@illinois.edu>
+}
+\references{
+Aguinis, H., & Culpepper, S.A. (in press). An expanded decision making procedure for examining cross-level interaction effects with multilevel modeling. \emph{Organizational Research Methods}. Available at: \url{http://mypage.iu.edu/~haguinis/pubs.html}
+}
+
+\keyword{ package }
+
+\examples{
+\dontrun{
+#Simulated Data Example
+data(simICCdata)
+  require(lme4)
+
+  #computing icca
+  vy = var(simICCdata$Y)
+  lmm0 <- lmer(Y ~ (1|l2id),data=simICCdata,REML=F)
+  VarCorr(lmm0)$l2id[1,1]/vy
+
+  #Estimating random slopes model
+  lmm1  <- lmer(Y~I(X1-m_X1)+I(X2-m_X2) +(I(X1-m_X1)+I(X2-m_X2)|l2id),data=simICCdata2,REML=F)
+  X = model.matrix(lmm1)
+  p=ncol(X)
+  T1  = VarCorr(lmm1) $l2id[1:p,1:p]
+  #computing iccb
+  #Notice '+1' because icc_beta assumes l2ids are from 1 to 30.
+  icc_beta(X,simICCdata2$l2id+1,T1,vy)$rho_beta
+
+#Hofmann 2000 Example
+data(Hofmann)
+  require(lme4)
+
+  #Random-Intercepts Model
+  lmmHofmann0 = lmer(helping ~ (1|id),data=Hofmann)
+  vy_Hofmann = var(Hofmann[,'helping'])
+  #computing icca
+  VarCorr(lmmHofmann0)$id[1,1]/vy_Hofmann
+
+  #Estimating Group-Mean Centered Random Slopes Model, no level 2 variables
+  lmmHofmann1  <- lmer(helping ~ mood_grp_cent + (mood_grp_cent |id),data=Hofmann,REML=F)
+  X_Hofmann = model.matrix(lmmHofmann1)
+  P = ncol(X_Hofmann)
+  T1_Hofmann  = VarCorr(lmmHofmann1)$id[1:P,1:P]
+  #computing iccb
+  icc_beta(X_Hofmann,Hofmann[,'id'],T1_Hofmann,vy_Hofmann)$rho_beta
+
+  #Performing LR test
+  library('RLRsim')
+  lmmHofmann1a  <- lmer(helping ~ mood_grp_cent + (1 |id),data=Hofmann,REML=F)
+  obs.LRT <- 2*(logLik(lmmHofmann1)-logLik(lmmHofmann1a))[1]
+  X <- getME(lmmHofmann1,"X")
+  Z <- t(as.matrix(getME(lmmHofmann1,"Zt")))
+  sim.LRT <- LRTSim(X, Z, 0, diag(ncol(Z)))
+  (pval <- mean(sim.LRT > obs.LRT))
+}
+}