# xysplom

From HH v1.8
0th

Percentile

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

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.

Keywords
hplot
##### 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
x
In the "formula" method, a formula. In the "default" method, a data.frame
...
other arguments to xyplot.
data
data.frame
na.action
See na.action in R, na.exclude in S-Plus. Defaults to na.pass because xyplot does sensible things with mi
y
In the "default" method, a data.frame with the same number of rows as the data.frame in x.
group
In the "default" method, a data.frame with the same number of rows as the data.frame in x.
relation, x.relation, y.relation,scales.in
Alternate ways to get to the scales(relation=) arguments to xyplot.
xlim.in, ylim.in
Alternate ways to get to the scales(limits=) arguments to xyplot.
corr, beta
Display the correlation and/or the regression coefficient for lm(y ~ x) for each panel in an additional strip label.
abline
logical. If TRUE, draw the least squares regression line within each panel. By default the abline is FALSE unless at least one of corr or beta is TRUE.
digits
number of significant digits for the correlation coefficient.
x.between, y.between, between.in
Alternate ways to get to the between= argument to xyplot.
strip.in
strip function that knows how to handle the corr and beta displays.
pch, cex
arguments to xyplot
panel.input
panel function used by xyplot within each panel. When abline==FALSE, the default panel function calls panel.xyplot. When abline==TRUE, the default panel function calls panel.xyplot
cartesian
When cartesian==TRUE, the cartesian product of the left-hand side number of variables and the right-hand side number of variables defines the number of panels in the display. When cartesian==FALSE, each variable in the l
plot
Defaults to TRUE. See details.
##### 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.

##### 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.

##### 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.

xyplot in R.

##### Aliases
• xysplom
• xysplom.formula
• xysplom.default
##### Examples
## 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)

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 | g, data=tmp)
xysplom(y ~ x | g, data=tmp, layout=c(2,1))

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

xysplom(w + y ~ x + z, data=tmp, cartesian=FALSE)
xysplom(w + y + x ~ z, 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 , data=tmp, corr=TRUE)
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))

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

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=
{}
)
Documentation reproduced from package HH, version 1.8, License: GPL version 2 or newer

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