R: Plot Univariate Effects of a Design or Model

plot.design {graphics} | R Documentation

## Plot Univariate Effects of a Design or Model

### Description

Plot univariate effects of one or 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 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.

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.

### 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)

[Package graphics version 2.15.1 Index]