plot.design             package: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.

See Also:

     'interaction.plot' for a "standard graphic" of designed
     experiments.

Examples:

     data(warpbreaks)
     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 :
     data(esoph)
     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)

