Type: Package
Title: Multilevel Model Intraclass Correlation for Slope Heterogeneity
Version: 1.2.0
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.
Depends: R (≥ 3.4.0)
Imports: Rcpp, lme4, stats, methods
LinkingTo: Rcpp (≥ 1.0.0), RcppArmadillo (≥ 0.9.200)
Suggests: RLRsim, testthat, covr
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/tmsalab/iccbeta
BugReports: https://github.com/tmsalab/iccbeta/issues
RoxygenNote: 6.1.1
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2019-01-28 20:34:11 UTC; ronin
Author: Steven Andrew Culpepper ORCID iD [aut, cph, cre], Herman Aguinis ORCID iD [aut, cph]
Maintainer: Steven Andrew Culpepper <sculpepp@illinois.edu>
Repository: CRAN
Date/Publication: 2019-01-28 21:50:02 UTC

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:

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:

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)

A multilevel dataset from Hofmann, Griffin, and Gavin (2000).

Description

A multilevel dataset from Hofmann, Griffin, and Gavin (2000).

Usage

Hofmann

Format

A data frame with 1,000 observations and 7 variables.

id

a numeric vector of group ids.

helping

a numeric vector of the helping outcome variable construct.

mood

a level 1 mood predictor.

mood_grp_mn

a level 2 variable of the group mean of mood.

cohesion

a level 2 covariate measuring cohesion.

mood_grp_cent

group-mean centered mood predictor.

mood_grd_cent

grand-mean centered mood predictor.

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. (2015). An expanded decision making procedure for examining cross-level interaction effects with multilevel modeling. Organizational Research Methods. Available at: http://hermanaguinis.com/pubs.html

See Also

lmer, model.matrix, VarCorr, LRTSim, simICCdata

Examples

## Not run: 

if(requireNamespace("lme4") && requireNamespace("RLRsim")){ 
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
# Need to install 'RLRsim' package
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)

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 (2015). 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, ...)

## S3 method for class 'lmerMod'
icc_beta(x, ...)

## Default S3 method:
icc_beta(x, l2id, T, vy, ...)

Arguments

x

A lmer model object or a design matrix with no missing values.

...

Additional parameters...

l2id

A vector that identifies group membership. The vector must be coded as a sequence of integers from 1 to J, the number of groups.

T

A matrix of the estimated variance-covariance matrix of a lmer model fit.

vy

The variance of the outcome variable.

Value

A list with:

Author(s)

Steven Andrew Culpepper

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://hermanaguinis.com/pubs.html

See Also

lme4::lmer(), model.matrix(), lme4::VarCorr(), RLRsim::LRTSim(), iccbeta::Hofmann, and iccbeta::simICCdata

Examples

## Not run: 

if(requireNamespace("lme4") && requireNamespace("RLRsim")){

## Example 1: Simulated Data Example from Aguinis & Culpepper (2015) ----
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)

## iccbeta calculation on `lmer` object
icc_beta(lmm1)

## Manual specification of iccbeta

# Extract components from model.
X <- model.matrix(lmm1)
p <- ncol(X)
T1  <- VarCorr(lmm1)$l2id[1:p,1:p]

# Note: vy was computed under "icca"

# Computing iccb
# Notice '+1' because icc_beta assumes l2ids are from 1 to 30.
icc_beta(X, simICCdata2$l2id + 1, T1, vy)$rho_beta

## Example 2: Hofmann et al. (2000)   ----

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)

## Automatic calculation of iccbeta using the lmer model
amod = icc_beta(lmmHofmann1)

## Manual calculation of iccbeta

X_Hofmann <- model.matrix(lmmHofmann1)
P <- ncol(X_Hofmann)
T1_Hofmann <- VarCorr(lmmHofmann1)$id[1:P,1:P]

# Computing iccb
bmod = 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)

Simulated data example from Aguinis and Culpepper (2015).

Description

A simulated data example from Aguinis and Culpepper (2015) to demonstrate the icc_beta function for computing the proportion of variance in the outcome variable that is attributed to heterogeneity in slopes due to higher-order processes/units.

Usage

simICCdata

Format

A data frame with 900 observations (i.e., 30 observations nested within 30 groups) on the following 6 variables.

l1id

A within group ID variable.

l2id

A group ID variable.

one

A column of 1's for the intercept.

X1

A simulated level 1 predictor.

X2

A simulated level 1 predictor.

Y

A simulated outcome variable.

Details

See Aguinis and Culpepper (2015) for the model used to simulate the dataset.

Source

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

lmer, model.matrix, VarCorr, LRTSim, Hofmann

Examples

## Not run: 
data(simICCdata)
if(requireNamespace("lme4")){ 
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
} else {
 stop("Please install `lme4` to run the above example.")
}

## End(Not run)