% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/readingskills.R
\docType{data}
\name{ReadingSkills}
\alias{ReadingSkills}
\title{Reading Accuracy in Dyslexic and Non-Dyslexic Children}
\format{
A data frame with 44 observations and 4 variables:
\describe{
  \item{accuracy}{Numeric. Reading score transformed to the open (0,1) interval.
        Values originally equal to 1 were replaced with 0.99. Suitable for
        standard beta regression.}
  \item{dyslexia}{Factor. Indicates whether the child has dyslexia
        (levels: "no", "yes"). Note that sum contrasts are typically used
        instead of treatment contrasts in beta regression analyses of this data.}
  \item{iq}{Numeric. Non-verbal intelligence quotient, transformed to
        z-scores (mean = 0, standard deviation = 1).}
  \item{accuracy1}{Numeric. Unrestricted reading score in the [0,1] interval.
        This version preserves the original maximum value of 1 and can be used
        with extended-support beta mixture regression models.}
}
}
\source{
Pammer, K. & Kevan, A. (2004). The Contribution of Visual Sensitivity,
Phonological Processing and Non-Verbal IQ to Children's Reading.
\emph{Unpublished manuscript}, The Australian National University, Canberra.

Smithson, M. & Verkuilen, J. (2006). A Better Lemon Squeezer?
Maximum-Likelihood Regression with Beta-Distributed Dependent Variables.
\emph{Psychological Methods}, 11(1), 54-71.
}
\usage{
data(ReadingSkills)
}
\description{
This dataset examines the relationship between non-verbal IQ and reading
accuracy in children diagnosed with dyslexia and in typical readers.
The reading scores are proportions bounded in the (0,1) interval, making
them suitable for beta regression and simplex regression analysis.
}
\details{
The data were originally collected by Pammer and Kevan (2004) and later
analyzed by Smithson and Verkuilen (2006) to demonstrate beta regression.

The transformation procedure for \code{accuracy} was as follows:
\enumerate{
  \item The original test scores were scaled to the [0,1] interval using
        the minimum and maximum possible scores in the reading test,
        resulting in \code{accuracy1}.
  \item To avoid boundary values (0 and 1) that are problematic for standard
        beta regression, all observations with value 1 were replaced with 0.99,
        creating the \code{accuracy} variable.
}

The unrestricted \code{accuracy1} variable can be analyzed using extended-support
beta regression methods (Kosmidis & Zeileis, 2025), which naturally accommodate
boundary observations.
}
\examples{
# Load the data
data(ReadingSkills)

# Quick overview
head(ReadingSkills)
str(ReadingSkills)

# Summary statistics by dyslexia status
aggregate(accuracy ~ dyslexia, data = ReadingSkills, summary)
aggregate(iq ~ dyslexia, data = ReadingSkills, summary)

# Visualize the relationship between IQ and reading accuracy
plot(accuracy ~ iq, data = ReadingSkills,
     col = c(4, 2)[dyslexia], pch = 19,
     main = "Reading Accuracy vs. IQ by Dyslexia Status",
     xlab = "IQ (z-scored)", ylab = "Reading Accuracy")
legend("topleft", legend = c("Non-dyslexic", "Dyslexic"),
       col = c(4, 2), pch = 19, bty = "n")

# Check for boundary values
table(ReadingSkills$accuracy == 0.99)  # Values replaced from 1
table(ReadingSkills$accuracy1 == 1)    # Original boundary values
}
\references{
Cribari-Neto, F. & Zeileis, A. (2010). Beta Regression in R.
\emph{Journal of Statistical Software}, 34(2), 1-24.
\doi{10.18637/jss.v034.i02}

Kosmidis, I. & Zeileis, A. (2025). Extended-Support Beta Regression for
[0, 1] Responses. \emph{Journal of the Royal Statistical Society C},
forthcoming. \doi{10.1093/jrsssc/qlaf039}
}
\keyword{datasets}
