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)`

## Arguments

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

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

- alpha
confidence interval level, default `alpha = 0.05`.

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

## Value

a `list` with parameter estimates.

## Details

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).

## References

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