iccdeboot: Bootstrap Confidence Intervals for Double-Entry Intraclass...

In iccde: Computation of the Double-Entry Intraclass Correlation

Bootstrap Confidence Intervals for Double-Entry Intraclass Correlations)

Description

For different applications, confidence intervals for the double- entry intraclass correlation can be useful. Bootstrap confidence intervals are computed by means of repeated resampling from the original data at hand.

Usage

icc.de.boot(data,
            n.sim = 1000,
            alpha = .05,
            use = "pairwise",
            digit = 3)

Arguments

data	A data frame with participants in rows and variables in columns. Users should restrict the data set to the variables of concrete interest because the all available information in the data frame will be used to compute bootstrapped confidence intervals. Thus, it is advisable to create a new data frame that entails only the variables on which the matrix of ICCDEs should be based.
n.sim	The number of iterations to be carried out. Default is 1,000.
alpha	Type I error. Default is .05.
use	Optional character string specifying how to deal with missing values. The input will be forwarded to the base cor function and can be one of the following strings, including abbreviation: "everything", "all.obs", "complete.obs", "na.or.complete", or "pairwise.complete.obs". The default is use = "pairwise".
digit	Number of digits in the output. Default is 3.

Value

The output provides a list with the following elements.

M	A matrix of the bootstrapped point estimates of the bootstrapped double-entry intraclass correlations.
LL	A matrix providing the lower limits of the bootstrap confidence intervals, given the desired alpha level.
UL	A matrix providing the upper limits of the bootstrap confidence intervals, given the selected alpha level.
Mean	A vector of the mean correlation per row of the raw correlation matrix (i.e., not bootstrapped), excluding the diagonal element.
SD	A vector of the standard deviations of the correlations per row of the raw correlation matrix (i.e., not bootstrapped), excluding the diagonal element.

Author(s)

Christian Blötner, Michael Paul Grosz [e-mail]

References

Furr, R. M. (2010). The Double-Entry Intraclass Correlation as an index of profile similarity: Meaning, limitations, and alternatives. Journal of Personality Assessment, 92(1), 1–15. <https://doi.org/10.1080/00223890903379134>

McCrae, R. R. (2008). A note on some measures of profile agreement. Journal of Personality Assessment, 90(2), 105–109. <https://doi.org/10.1080/00223890701845104>

Examples

df <- data.frame(a = rnorm(100), b = rnorm(100), c = rnorm(100),
                 x = rnorm(100), y = rnorm(100), z = rnorm(100))
icc.de.boot(data = df,
            n.sim = 10, # just for illustration. Use higher n.sim in real data
            alpha = .01,
            digit = 2)

iccde documentation built on June 13, 2026, 1:06 a.m.