plot.design {graphics} R Documentation

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

### Description

Plot univariate effects of one ore more `factor`s, typically for a designed experiment as analyzed by `aov()`. Further, in S this a method of the `plot` generic function for `design` objects.

### 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 arguments 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.

This is a new R implementation which will not be completely compatible to the earlier S implementations. This is not a bug but might still change.

### Note

A big effort was taken to make this closely compatible to the S version. However, `col` (and `fg`) specification has different effects.

### 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.

`interaction.plot` for a “standard graphic” of designed experiments.

### Examples

```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)
```

[Package graphics version 2.5.0 Index]