Peter Huber referred to “the rawness of raw data”, a kind of
data we would not expect to find in a textbook. The book of
Fahrmeir and Tutz on multivariate modelling refers to the visual
impairment data from Liang et al., 1992 in table 3.12: Nothing
wrong here at first sight; but how would you tell? There are
some people who are actually able to look at non-trivial table
data and spot “the round peg in the square hole”, but that just
won’t work for the rest of us. As you might guess, I am going to
make a case for graphics here. Let’s start with what the
mainstream would do: plot the data in a dotplot like thing using
the trellis paradigm of conditioning. I used ggplot2 to make
sure to trellis state-of-the-art. A simple qplot(count, side,
data=visual2, colour=impaired) + facet_grid(age ~ race) (I still
have a hard time to find that syntax intuitive …) Surprisingly
this plot already is sufficient to spot the “problem” in the
data, although some important properties of the data can’t be
seen here. A mosaic plot m