# plot.design

0th

Percentile

##### Plot Univariate Effects of a Design or Model

Plot univariate effects of one or more factors, typically for a designed experiment as analyzed by aov().

Keywords
hplot
##### 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 formula or terms object.

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 x when that is formula-like.

graphical parameters such as col, see par.

ylim

range of y values, as in plot.default.

xlab

x axis label, see title.

ylab

y axis label with a ‘smart’ default.

main

main title, see title.

logical indicating if the user should be asked before a new page is started -- in the case of multiple y's.

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.

##### 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.

interaction.plot for a ‘standard graphic’ of designed experiments.
library(graphics) # NOT RUN { 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) # }