% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/compute_ICC.R
\name{compute_ICC}
\alias{compute_ICC}
\title{Compute Intraclass Correlation Coefficient (ICC) for Selected Columns}
\usage{
compute_ICC(data, items, check_numeric = TRUE, lmer = FALSE, ...)
}
\arguments{
\item{data}{A data frame containing the ratings or measurements.}

\item{items}{A character vector specifying column names for which
ICC should be calculated (e.g., raters or repeated measurements).}

\item{check_numeric}{Logical. If \code{TRUE} (default), ensures all
selected columns are numeric.}

\item{lmer}{Logical. Passed to \code{psych::ICC()}. Default is FALSE.}

\item{...}{Additional arguments passed to \code{psych::ICC()}.}
}
\value{
An object returned by \code{psych::ICC()}, typically a list
  containing a results table with ICC estimates, F statistics,
  confidence intervals, and model details.
}
\description{
Computes Intraclass Correlation Coefficients (ICC) for a specified set
of numeric variables within a data frame using \code{psych::ICC()}.

This function standardizes column selection and basic validation while
delegating all statistical estimation to \code{psych::ICC()}.
}
\details{
The function does not choose or filter specific ICC models.
All available ICC types (e.g., ICC1, ICC2, ICC3, single and average)
are returned exactly as produced by \code{psych::ICC()}.

Users are responsible for:
\itemize{
  \item Choosing the appropriate ICC model for their study design.
  \item Ensuring assumptions (e.g., continuous ratings, independence).
  \item Handling missing data appropriately.
}
}
\seealso{
\code{\link[psych]{ICC}}
}
