| plot.design {graphics} | R Documentation |
Plot Univariate Effects of a Design or Model
Description
Plot univariate effects of one or more factors,
typically for a designed experiment as analyzed by aov().
Usage
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)
Arguments
x |
either a data frame containing the design factors and
optionally the response, or a |
y |
the response, if not given in x. |
fun |
a function (or name of one) to be applied to each subset. It must return one number for a numeric (vector) input. |
data |
data frame containing the variables referenced by |
... |
graphical parameters such as |
ylim |
range of y values, as in |
xlab |
x axis label, see |
ylab |
y axis label with a ‘smart’ default. |
main |
main title, see |
ask |
logical indicating if the user should be asked before a new page is started – in the case of multiple y values. |
xaxt |
character giving the type of x axis. |
axes |
logical indicating if axes should be drawn. |
xtick |
logical indicating if ticks (one per factor) should be drawn on the x axis. |
Details
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
horizontal line.
Note
A big effort was taken to make this closely compatible to the S
version. However, col (and fg) specifications have
different effects.
In S this was a method of the plot generic function for
design objects.
Author(s)
Roberto Frisullo and Martin Maechler
References
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.
See Also
interaction.plot for a ‘standard graphic’
of designed experiments.
Examples
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)