# plot.design

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

Plot univariate effects of one or more `factor`

s,
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`

.- ask
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.

##### See Also

`interaction.plot`

for a ‘standard graphic’
of designed experiments.

##### Examples

`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)
# }
```

*Documentation reproduced from package graphics, version 3.5.0, License: Part of R 3.5.0*