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Last data update: 2014.03.03

R: Plot Univariate Effects of a Design or Model

plot.design

R Documentation

Plot Univariate Effects of a Design or Model

Description

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

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.

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.

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)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'license()' or 'licence()' for distribution details.

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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(graphics)
> png(filename="/home/ddbj/snapshot/RGM3/R_rel/result/graphics/plot.design.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plot.design
> ### Title: Plot Univariate Effects of a Design or Model
> ### Aliases: plot.design
> ### Keywords: hplot
> 
> ### ** Examples
> 
> require(stats)
> plot.design(warpbreaks)  # automatic for data frame with one numeric var.
> 
> Form <- breaks ~ wool + tension
> summary(fm1 <- aov(Form, data = warpbreaks))
            Df Sum Sq Mean Sq F value  Pr(>F)   
wool         1    451   450.7   3.339 0.07361 . 
tension      2   2034  1017.1   7.537 0.00138 **
Residuals   50   6748   135.0                   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> plot.design(       Form, data = warpbreaks, col = 2)  # same as above
> 
> ## More than one y :
> utils::str(esoph)
'data.frame':	88 obs. of  5 variables:
 $ agegp    : Ord.factor w/ 6 levels "25-34"<"35-44"<..: 1 1 1 1 1 1 1 1 1 1 ...
 $ alcgp    : Ord.factor w/ 4 levels "0-39g/day"<"40-79"<..: 1 1 1 1 2 2 2 2 3 3 ...
 $ tobgp    : Ord.factor w/ 4 levels "0-9g/day"<"10-19"<..: 1 2 3 4 1 2 3 4 1 2 ...
 $ ncases   : num  0 0 0 0 0 0 0 0 0 0 ...
 $ ncontrols: num  40 10 6 5 27 7 4 7 2 1 ...
> 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)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>