A toy data set illustrating the spurious correlation
between reading skills and shoe size in school-children.
Usage
data("readingSkills")
Format
A data frame with 200 observations on the following 4 variables.
nativeSpeaker
a factor with levels no and yes,
where yes indicates that the child
is a native speaker of the language of the reading test.
age
age of the child in years.
shoeSize
shoe size of the child in cm.
score
raw score on the reading test.
Details
In this artificial data set, that was generated by means of a linear model,
age and nativeSpeaker are actual predictors of the
score, while the spurious correlation between score and
shoeSize is merely caused by the fact that both depend on age.
The true predictors can be identified, e.g., by means of partial correlations,
standardized beta coefficients in linear models or the conditional random
forest variable importance, but not by means of the standard random
forest variable importance (see example).
Examples
set.seed(290875)
readingSkills.cf <- cforest(score ~ ., data = readingSkills,
control = cforest_unbiased(mtry = 2, ntree = 50))
# standard importance
varimp(readingSkills.cf)
# the same modulo random variation
varimp(readingSkills.cf, pre1.0_0 = TRUE)
# conditional importance, may take a while...
varimp(readingSkills.cf, conditional = TRUE)