Plot Univariate Effects of a Design or Model
plot.design(x, y = NULL, fun = mean, data = NULL, …, ylim = NULL, xlab = "Factors", ylab = NULL, main = NULL, ask = NULL, xaxt = par("xaxt"), axes = TRUE, xtick = FALSE)
the response, if not given in x.
a function (or name of one) to be applied to each subset. It must return one number for a numeric (vector) input.
data frame containing the variables referenced by
xwhen that is formula-like.
range of y values, as in
x axis label, see
y axis label with a ‘smart’ default.
main title, see
logical indicating if the user should be asked before a new page is started -- in the case of multiple y's.
character giving the type of x axis.
logical indicating if axes should be drawn.
logical indicating if ticks (one per factor) should be drawn on the x axis.
The supplied function will be called once for each level of each
factor in the design and the plot will show these summary values. The
levels of a particular factor are shown along a vertical line, and the
overall value of
fun() for the response is drawn as a
A big effort was taken to make this closely compatible to the S
fg) specifications have
In S this was a method of the
plot generic function for
Chambers, J. M. and Hastie, T. J. eds (1992) Statistical Models in S. Chapman & Hall, London, the white book, pp.546--7 (and 163--4).
Freeny, A. E. and Landwehr, J. M. (1990) Displays for data from large designed experiments; Computer Science and Statistics: Proc.\ 22nd Symp\. Interface, 117--126, Springer Verlag.
interaction.plot for a ‘standard graphic’
of designed experiments.
require(stats) plot.design(warpbreaks) # automatic for data frame with one numeric var. Form <- breaks ~ wool + tension summary(fm1 <- aov(Form, data = warpbreaks)) plot.design( Form, data = warpbreaks, col = 2) # same as above ## More than one y : utils::str(esoph) plot.design(esoph) ## two plots; if interactive you are "ask"ed ## or rather, compare mean and median: op <- par(mfcol = 1:2) plot.design(ncases/ncontrols ~ ., data = esoph, ylim = c(0, 0.8)) plot.design(ncases/ncontrols ~ ., data = esoph, ylim = c(0, 0.8), fun = median) par(op)