R/iccbeta-internal.R

# Package documentation

#' @useDynLib iccbeta, .registration=TRUE
#' @importFrom Rcpp evalCpp
#' @references
#' Aguinis, H., & Culpepper, S.A. (2015).
#' An expanded decision making procedure for examining cross-level interaction
#' effects with multilevel modeling. \emph{Organizational Research Methods}.
#' Available at: \url{http://www.hermanaguinis.com/pubs.html}
#' @examples 
#' \dontrun{
#' 
#' if(requireNamespace("lme4") && requireNamespace("RLRsim")){ 
#' # Simulated Data Example
#' data(simICCdata)
#' library('lme4')
#' 
#' # computing icca
#' vy <- var(simICCdata$Y)
#' lmm0 <- lmer(Y ~ (1|l2id), data = simICCdata, REML = FALSE)
#' VarCorr(lmm0)$l2id[1,1]/vy
#' 
#' # Create simICCdata2
#' grp_means = aggregate(simICCdata[c('X1','X2')], simICCdata['l2id'],mean)
#' colnames(grp_means)[2:3] = c('m_X1','m_X2')
#' simICCdata2 = merge(simICCdata,grp_means,by='l2id')
#' 
#' # 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 = FALSE)
#' 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)$rho_beta
#' 
#' # Hofmann 2000 Example
#' data(Hofmann)
#' library('lme4')
#' 
#' # Random-Intercepts Model
#' lmmHofmann0 <- lmer(helping ~ (1|id), data = Hofmann)
#' vy_Hofmann <- var(Hofmann[,'helping'])
#' # computing icca
#' VarCorr(lmmHofmann0)$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 = FALSE)
#' 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 = FALSE)
#' 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))
#' } else {
#'  stop("Please install packages `RLRsim` and `lme4` to run the above example.")
#' }
#' }
"_PACKAGE"

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iccbeta documentation built on May 2, 2019, 5:25 a.m.