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Return all ICCs for the original variables.

Source: R/04_post_02_accessor_functions.R
get_ICC.Rd

You can use get_ICC() or get_ICCs() interchangeably.

Usage

get_ICC(object)

get_ICCs(object)

get_icc(object)

Arguments

object

A wbCorr object, created by the wbCorr() function.

Value

A data frame with the one-way random-effects, single-measure ICC(1,1) for every variable. Each ICC is estimated separately from all finite observations with a non-missing cluster identifier. The ANOVA method-of-moments estimator uses an effective cluster size for unbalanced clusters. Negative sample estimates are retained and can be less than -1 in severely unbalanced samples. The population interpretation assumes independent clusters, a common within-cluster variance, and noninformative cluster size and missingness. NA is returned when an ICC cannot be estimated because there are fewer than two clusters, no within-cluster replication, or zero total variability.

References

Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86(2), 420-428. doi:10.1037/0033-2909.86.2.420

Ohyama, T. (2025). A comparison of confidence interval methods for the intraclass correlation coefficient based on the one-way random effects model. Japanese Journal of Statistics and Data Science, 8, 587-602. doi:10.1007/s42081-025-00292-3

Wang, C.-M., Yandell, B. S., & Rutledge, J. J. (1992). The dilemma of negative analysis of variance estimators of intraclass correlation. Theoretical and Applied Genetics, 85, 79-88. doi:10.1007/BF00223848

See also

wbCorr

Examples

# importing our simulated example dataset with pre-specified within- and between- correlations
data("simdat_intensive_longitudinal")

# create object:
correlations <- wbCorr(data = simdat_intensive_longitudinal,
                      cluster = 'participantID')
#> Warning: Analytic p-values and confidence intervals are working approximations for clustered data; use inference = 'cluster_bootstrap' for whole-cluster resampling intervals.

# returns the ICCs:
ICCs <- get_ICC(correlations)
print(ICCs)
#>   variable         ICC
#> 1      day -0.02040816
#> 2     var1  0.51348244
#> 3     var2  0.49354674
#> 4     var3  0.49258895