interaction2wt: Plot all main effects and twoway interactions in a multifactor design

Description

The main diagonal displays boxplots for the main effects of each factor. The off-diagonals show the interaction plots for each pair of factors. The i,j panel shows the same factors as the j,i but with the trace- and x-factor roles interchanged.

Usage

interaction2wt(x, ...)

## S3 method for class 'formula':
interaction2wt(x, data = sys.parent(), responselab, ...)

## S3 method for class 'default':
interaction2wt(x,
               response.var,
               responselab = deparse(substitute(y)),
               relation = list(x = "same", y = "same"),
               x.relation = relation$x,
               y.relation = relation$y,
               digits = 3,
               x.between=if (label.as.interaction.formula) 0 else 1,
               y.between=if (label.as.interaction.formula) 0 else 1,
               between,
               cex = 0.75,
               rot=c(0,0),
               panel.input = panel.interaction2wt,
               strip.input =
                if (label.as.interaction.formula) strip.default
                else strip.interaction2wt,
               par.strip.text.input = list(cex=.7, responselab=responselab),
               scales.additional,
               main.in =
                 paste(responselab,
                       ": main effects and 2-way interactions",
                       sep = ""),
               xlab = list(labels = ""),
               ylab = list(labels = ""),
               simple=FALSE,
               box.ratio=if (simple) .32 else 1,
               label.as.interaction.formula=TRUE,
               ...,
               main.cex)

Arguments

The object on which method dispatch is carried out. For the "formula" method, a formula describing the response variable and factors. The formula is generally of the form y ~ g1 + g2 + .... There may be one

data

For the formula method, a data frame containing values for any variables in the formula. In the R version, if not found in data, or if data is unspecified, the variables are looked for in the env

responselab

Character name of response variable, defaults to the name of the response variable in the formula.

...

additional arguments, primarily trellis arguments.

response.var

For the "default" method, the response variable. This is usually constructed by formula method from the input data and the input formula.

simple

logical. TRUE if simple effects are to be displayed. Arguments simple.offset, simple.scale, and col.by.row may also be needed. See pan

box.ratio

bwplot in R or bwplot in S-Plus.

relation

trellis argument.

x.relation

x value of relation argument.

y.relation

y value of relation argument.

digits

doesn't do anything at the moment

x.between

x value of between argument.

y.between

y value of between argument.

between

trellis/lattice between argument. If used, between has precedence over both the x.between and y.between arguments.

cex

S-Plus: changes the size of the median dot in the boxplots. R: doesn't do anything.

panel.input

panel function. Default is panel.interaction2wt.

label.as.interaction.formula

logical. If TRUE, each panel has a single strip label of the form y ~ a | b. If FALSE, each panel has a pair of strip labels, one for the trace factor and one for the x factor.

strip.input

strip function. Default depends on the value of label.as.interaction.formula.

par.strip.text.input

par.strip.text argument.

scales.additional

additional arguments to scales argument.

main.in

Text of main title.

xlab

No effect.

ylab

No effect.

main.cex

cex for main title.

rot

Rotation of x tick labels and y tick labels. Only 0 and 90 will look good.

Value

"trellis" object containing the plot.

References

Heiberger, Richard M. and Holland, Burt (2004b). Statistical Analysis and Data Display: An Intermediate Course with Examples in S-Plus, R, and SAS. Springer Texts in Statistics. Springer. ISBN 0-387-40270-5.

Examples

Run this code

vulcan <- read.table(hh("datasets/vulcan.dat"), header=TRUE)
vulcan$filler <- factor(vulcan$filler)
position(vulcan$raw) <- (1:4)+.5
position(vulcan$pretreat) <- 2:4
interaction2wt(wear ~ filler + pretreat + raw, data=vulcan)
interaction2wt(wear ~ filler + raw, data=vulcan,
               simple=TRUE)
interaction2wt(wear ~ filler + raw, data=vulcan,
               simple=TRUE, simple.scale=c(filler=.15, raw=.2))

if.R(r={
  ToothGrowth <- ToothGrowth  ## local copy
  ToothGrowth$dose <- positioned(ToothGrowth$dose) ## modify local copy
print(
  anova(aov(len ~ supp*dose, data=ToothGrowth))
)
print(
  interaction2wt(len ~ supp + dose, data=ToothGrowth)
)
print(
  bwplot(len ~ supp | unpositioned(dose), data=ToothGrowth, layout=c(3,1))
)
print(
  bwplot(len ~ dose | supp, data=ToothGrowth, layout=c(2,1),
         box.ratio=.5,
         panel=panel.bwplot.intermediate.hh,
         scales=list(x=list(at=position(ToothGrowth$dose))))
)
print(
  bwplot(len ~ dose | supp, data=ToothGrowth, layout=c(2,1),
         box.ratio=.5,
         scales=list(x=list(at=position(ToothGrowth$dose))))
)
}, s={})