xysplom: scatterplot matrix with potentially different sets of variables on the rows and columns.

Description

scatterplot matrix with potentially different sets of variables on the rows and columns. The slope or regression coefficient for simple least squares regression can be displayed in the strip label for each panel.

Usage

xysplom(x, ...)

## S3 method for class 'formula':
xysplom(x, data = sys.parent(), na.action = na.pass, ...)

## S3 method for class 'default':
xysplom(x, y=x, group, relation="free",
        x.relation=relation, y.relation=relation,
        xlim.in=NULL, ylim.in=NULL,
        corr=FALSE, beta=FALSE, abline=corr||beta, digits=3,
        x.between=NULL, y.between=NULL,
        between.in=list(x=x.between, y=y.between),
        scales.in=list(
          x=list(relation=x.relation, alternating=FALSE),
          y=list(relation=y.relation, alternating=FALSE)),
        strip.in=strip.xysplom,
        pch=16, cex=.75,
        panel.input=panel.xysplom, ...,
        cartesian=TRUE,
        plot=TRUE)

Arguments

Value

When plot=TRUE (the normal setting), the "trellis" object containing the graph. When plot=FALSE, the restructured data that must be sent to the xyplot function.

Details

The argument plot=TRUE is the normal setting and then the function returns a "trellis" object. When the argument plot=FALSE, the function returns the argument list that would otherwise be sent to xyplot. This list is interesting when the function xysplom was designed because the function works by restructuring the input data and running xyplot on the restructured data.

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

## xysplom syntax options

tmp <- data.frame(y=rnorm(12), x=rnorm(12), z=rnorm(12), w=rnorm(12),
                  g=factor(rep(1:2,c(6,6))))
tmp2 <- tmp[,1:4]

xysplom(y ~ x , data=tmp)

xysplom(y ~ x + w , data=tmp)

xysplom(y + w ~ x , data=tmp, corr=TRUE)
xysplom(y + w ~ x , data=tmp, beta=TRUE)
xysplom(y + w ~ x , data=tmp, corr=TRUE, beta=TRUE)
xysplom(y + w ~ x , data=tmp, abline=TRUE)
xysplom(y + w ~ x , data=tmp, corr=TRUE, abline=FALSE)

xysplom(y + x ~ z | g, data=tmp)
xysplom(y + x ~ z | g, data=tmp, layout=c(2,2))

xysplom(w + y ~ x + z, data=tmp)
xysplom(w + y ~ x + z | g, data=tmp, layout=c(2,4))
xysplom(w + y ~ x + z | g, data=tmp, cartesian=FALSE)

xysplom(w + y ~ x + z, data=tmp, scales=list(relation="same"))
xysplom(w + y ~ x + z, data=tmp, x.relation="same")

xysplom(~ y + x + z , data=tmp)
xysplom(~ y + x + z | g, data=tmp)
xysplom(~ y + x + z | g, data=tmp, corr=TRUE)
xysplom(~ y + x + z | g, data=tmp, corr=TRUE, digits=2)
xysplom(~ y + x + z | g, data=tmp, corr=TRUE, layout=c(3,6), par.strip.text=list(cex=.5))

## These three examples run from R and the command line in S-Plus.
## They don't run from inside the Splus CMD check.
xysplom(~ tmp)
xysplom(~ tmp | tmp$g)
xysplom(tmp$y ~ tmp2 | tmp$g)

xysplom(g ~ x , data=tmp)
xysplom(x ~ g , data=tmp)

## Subscripting requires the x=, y= notation.
## Subscripting doesn't work with the y ~ x notation.
xysplom( ~ tmp[, c("x","y")])                   ## doesn't work
  xysplom(tmp2[, c("w","z")] ~ tmp[, c("x","y")]) ## doesn't work

## use this instead
  xysplom(x = tmp[, c("x","y")])
  xysplom(y   = tmp2[, c("w","z")],  x   = tmp[, c("x","y")])

## or, even better, use the y ~ x notation
  xysplom(~ x + y, data=tmp)
  xysplom(w + z ~ x + y, data=cbind(tmp, tmp2))

## xyplot in R has many similar capabilities with xysplom
if.R(r=
       xyplot(w + z ~ x + y, data=tmp, outer=TRUE)
     ,s=
       {}
    )

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