A function that computes the one-way intraclass correlations (ICC), with the corresponding standard error of measurement, sem, and the confidence intervals, using the variance estimates from a linear mixed model. See details for more information.

icc_oneway(data, cols = colnames(data), alpha = 0.05, twoway = FALSE)



data.frame with a column for each observer/rater and a row per rated subject.


character vector with the column names to be used as observers. Default is `cols = colnames(data)`.


confidence interval level, default `alpha = 0.05`.


logical indicator if the variance components are estimated from the two-way model default: `twoway = FALSE`.


a `list` with parameter estimates.


The ICC type oneway is the variance between the subjects divided by the sum of the subject variance and the residual variance (total variance in a oneway model. Each subject is rated by a different set of raters, that are randomly selected from a larger population of judges (Shrout & Fleiss, 1979). The `icc_oneway()` uses the `varcomp()` function to compute the variances. Theses variances are estimated from a `lmer` model with random slope for the subjects. When `twoway = TRUE` a level for the raters is estimated as well and the rater variance is not used for the ICC oneway and is subtracted from the sum of subject variance over the raters, which is then averaged. The error variance is computed as the sum of the residual variance and the rater variance. Accordingly, the rater variance is part of the error variance. The standard #' error of measurement is the square root of this error variance. The confidence intervals are computed with the exact F method. F = (k * subject variance + error variance)/ error variance, with df1 = n - 1 and df2 = n * (k - 1) (Shrout & Fleiss, 1979).


Shrout, P.E. & Fleiss, J.L. (1979) Intraclass Correlations: Uses in Assessing Rater Reliability. Psychological Bulletin, 87(2), 420-428.


Iris Eekhout