ReadingSkills | R Documentation |
Data for assessing the contribution of non-verbal IQ to children's reading skills in dyslexic and non-dyslexic children.
data("ReadingSkills", package = "betareg")
A data frame containing 44 observations on 3 variables.
numeric. Reading score with maximum restricted to be 0.99 rather than 1 (see below).
factor. Is the child dyslexic? (A sum contrast rather than treatment contrast is employed.)
numeric. Non-verbal intelligence quotient transformed to z-scores.
numeric. Unrestricted reading score with a maximum of 1 (see below).
The data were collected by Pammer and Kevan (2004) and employed by
Smithson and Verkuilen (2006). The original reading accuracy score was transformed
by Smithson and Verkuilen (2006) so that accuracy
is in the open unit
interval (0, 1) and beta regression can be employed. First, the original accuracy
was scaled using the minimal and maximal score (a
and b
, respectively)
that can be obtained in the test: accuracy1 = (original_accuracy - a) / (b - a)
(a
and b
are not provided). Subsequently, accuracy
was obtained
from accuracy1
by replacing all observations with a value of 1 with 0.99.
Kosmidis and Zeileis (2024) propose to investigate the original unrestricted
accuracy1
variable using their extended-support beta mixture regression.
Example 3 from Smithson and Verkuilen (2006) supplements.
Cribari-Neto F, Zeileis A (2010). Beta Regression in R. Journal of Statistical Software, 34(2), 1–24. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v034.i02")}
Grün B, Kosmidis I, Zeileis A (2012). Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned. Journal of Statistical Software, 48(11), 1–25. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v048.i11")}
Kosmidis I, Zeileis A (2024). Extended-Support Beta Regression for [0, 1] Responses. 2409.07233, arXiv.org E-Print Archive. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.2409.07233")}
Pammer K, Kevan A (2004). The Contribution of Visual Sensitivity, Phonological Processing and Non-Verbal IQ to Children's Reading. Unpublished manuscript, The Australian National University, Canberra.
Smithson M, Verkuilen J (2006). A Better Lemon Squeezer? Maximum-Likelihood Regression with Beta-Distributed Dependent Variables. Psychological Methods, 11(7), 54–71.
betareg
, MockJurors
, StressAnxiety
options(digits = 4)
data("ReadingSkills", package = "betareg")
## Smithson & Verkuilen (2006, Table 5)
## OLS regression
## (Note: typo in iq coefficient: 0.3954 instead of 0.3594)
rs_ols <- lm(qlogis(accuracy) ~ dyslexia * iq, data = ReadingSkills)
summary(rs_ols)
## Beta regression (with numerical rather than analytic standard errors)
## (Note: Smithson & Verkuilen erroneously compute one-sided p-values)
rs_beta <- betareg(accuracy ~ dyslexia * iq | dyslexia + iq,
data = ReadingSkills, hessian = TRUE)
summary(rs_beta)
## Extended-support beta mixture regression (Kosmidis & Zeileis 2024)
rs_xbx <- betareg(accuracy1 ~ dyslexia * iq | dyslexia + iq, data = ReadingSkills)
summary(rs_xbx)
## Coefficients in XBX are typically somewhat shrunken compared to beta
cbind(XBX = coef(rs_xbx), Beta = c(coef(rs_beta), NA))
## Visualization
plot(accuracy1 ~ iq, data = ReadingSkills, col = c(4, 2)[dyslexia], pch = 19)
nd <- data.frame(dyslexia = "no", iq = -30:30/10)
lines(nd$iq, predict(rs_xbx, nd), col = 4)
lines(nd$iq, predict(rs_beta, nd), col = 4, lty = 5)
lines(nd$iq, plogis(predict(rs_ols, nd)), col = 4, lty = 3)
nd <- data.frame(dyslexia = "yes", iq = -30:30/10)
lines(nd$iq, predict(rs_xbx, nd), col = 2)
lines(nd$iq, predict(rs_beta, nd), col = 2, lty = 5)
lines(nd$iq, plogis(predict(rs_ols, nd)), col = 2, lty = 3)
legend("topleft", c("Dyslexia: no", "Dyslexia: yes", "OLS", "XBX", "Beta"),
lty = c(0, 0, 3, 1, 5), pch = c(19, 19, NA, NA, NA), col = c(4, 2, 1, 1, 1), bty = "n")
## see demo("SmithsonVerkuilen2006", package = "betareg") for further details
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