This function calculates the intraclass-correlation coefficient (ICC) - sometimes also called variance partition coefficient (VPC) - for mixed effects models. The ICC can be calculated for all models supported by insight::get_variance(). For models fitted with the brms-package, icc() might fail due to the large variety of models and families supported by the brms-package. In such cases, an alternative to the ICC is the variance_decomposition(), which is based on the posterior predictive distribution (see 'Details').

icc(model, by_group = FALSE)

variance_decomposition(model, re_formula = NULL, robust = TRUE, ci = 0.95, ...)

## Arguments

model A (Bayesian) mixed effects model. Logical, if TRUE, icc() returns the variance components for each random-effects level (if there are multiple levels). See 'Details'. Formula containing group-level effects to be considered in the prediction. If NULL (default), include all group-level effects. Else, for instance for nested models, name a specific group-level effect to calculate the variance decomposition for this group-level. See 'Details' and ?brms::posterior_predict. Logical, if TRUE, the median instead of mean is used to calculate the central tendency of the variances. Credible interval level. Arguments passed down to posterior_predict().

## Value

A list with two values, the adjusted and conditional ICC. For variance_decomposition(), a list with two values, the decomposed ICC as well as the credible intervals for this ICC.

## Details

### Interpretation

The ICC can be interpreted as “the proportion of the variance explained by the grouping structure in the population”. This index goes from 0, if the grouping conveys no information, to 1, if all observations in a group are identical (Gelman \& Hill, 2007, p. 258). In other word, the ICC “can also be interpreted as the expected correlation between two randomly drawn units that are in the same group” (Hox 2010: 15), although this definition might not apply to mixed models with more complex random effects structures.

### Calculation

The ICC is calculated by dividing the random effect variance, σ2i, by the total variance, i.e. the sum of the random effect variance and the residual variance, σ2ε.

icc() calculates an adjusted and conditional ICC, which both take all sources of uncertainty (i.e. of all random effects) into account. While the adjusted ICC only relates to the random effects, the conditional ICC also takes the fixed effects variances into account (see Nakagawa et al. 2017). Typically, the adjusted ICC is of interest when the analysis of random effects is of interest. icc() returns a meaningful ICC also for more complex random effects structures, like models with random slopes or nested design (more than two levels) and is applicable for models with other distributions than Gaussian. For more details on the computation of the variances, see ?insight::get_variance.

### ICC for unconditional and conditional models

Usually, the ICC is calculated for the null model ("unconditional model"). However, according to Raudenbush and Bryk (2002) or Rabe-Hesketh and Skrondal (2012) it is also feasible to compute the ICC for full models with covariates ("conditional models") and compare how much, e.g., a level-2 variable explains the portion of variation in the grouping structure (random intercept).

### ICC for specific group-levels

The proportion of variance for specific levels related to the overall model can be computed by setting by_group = TRUE. The reported ICC is the variance for each (random effect) group compared to the total variance of the model. For mixed models with a simple random intercept, this is identical to the classical (adjusted) ICC.

### Variance decomposition for brms-models

If model is of class brmsfit, icc() might fail due to the large variety of models and families supported by the brms package. In such cases, variance_decomposition() is an alternative ICC measure. The function calculates a variance decomposition based on the posterior predictive distribution. In this case, first, the draws from the posterior predictive distribution not conditioned on group-level terms (posterior_predict(..., re_formula = NA)) are calculated as well as draws from this distribution conditioned on all random effects (by default, unless specified else in re_formula) are taken. Then, second, the variances for each of these draws are calculated. The "ICC" is then the ratio between these two variances. This is the recommended way to analyse random-effect-variances for non-Gaussian models. It is then possible to compare variances across models, also by specifying different group-level terms via the re_formula-argument.

Sometimes, when the variance of the posterior predictive distribution is very large, the variance ratio in the output makes no sense, e.g. because it is negative. In such cases, it might help to use robust = TRUE.

## References

• Hox, J. J. (2010). Multilevel analysis: techniques and applications (2nd ed). New York: Routledge.

• Nakagawa, S., Johnson, P. C. D., & Schielzeth, H. (2017). The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. Journal of The Royal Society Interface, 14(134), 20170213. doi: 10.1098/rsif.2017.0213

• Rabe-Hesketh, S., & Skrondal, A. (2012). Multilevel and longitudinal modeling using Stata (3rd ed). College Station, Tex: Stata Press Publication.

• Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: applications and data analysis methods (2nd ed). Thousand Oaks: Sage Publications.

## Examples

if (require("lme4")) {
model <- lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
icc(model)
}
#> # Intraclass Correlation Coefficient
#>
#>   Conditional ICC: 0.311
# ICC for specific group-levels
if (require("lme4")) {
data(sleepstudy)
set.seed(12345)
sleepstudy$grp <- sample(1:5, size = 180, replace = TRUE) sleepstudy$subgrp <- NA
for (i in 1:5) {
filter_group <- sleepstudy$grp == i sleepstudy$subgrp[filter_group] <-
sample(1:30, size = sum(filter_group), replace = TRUE)
}
model <- lmer(
Reaction ~ Days + (1 | grp / subgrp) + (1 | Subject),
data = sleepstudy
)
icc(model, by_group = TRUE)
}
#> # ICC by Group
#>
#> Group      |   ICC
#> ------------------
#> subgrp:grp | 0.017
#> Subject    | 0.589
#> grp        | 0.001