R: iccbeta: Multilevel Model Intraclass Correlation for Slope...

iccbeta-package {iccbeta} | R Documentation

## iccbeta: Multilevel Model Intraclass Correlation for Slope Heterogeneity

### Description

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.

### Author(s)

Maintainer: Steven Andrew Culpepper sculpepp@illinois.edu (0000-0003-4226-6176) [copyright holder]

Authors:

- Herman Aguinis haguinis@gwu.edu (0000-0002-3485-9484) [copyright holder]

### References

Aguinis, H., & Culpepper, S.A. (2015). An expanded decision making procedure for examining cross-level interaction effects with multilevel modeling. Organizational Research Methods. Available at: http://www.hermanaguinis.com/pubs.html

### See Also

Useful links:

- https://github.com/tmsalab/iccbeta

- Report bugs at https://github.com/tmsalab/iccbeta/issues

### Examples

    ## Not run:

    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.")
    }

    ## End(Not run)

[Package iccbeta version 1.2.0 Index]