| 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., Hastie T. J. (1992). Statistical Models in S. Chapman & Hall, London. ISBN 9780412830402. Pages 546–7 (and 163–4).
Freeny A. E., Landwehr J. M. (1992). “Displays for Data from Large Designed Experiments.” In Page C., LePage R. (eds.), Computing Science and Statistics, 117–126. ISBN 978-1-4612-2856-1. doi:10.1007/978-1-4612-2856-1_15.
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)