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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.
xysplom(x, ...)# S3 method for formula
xysplom(x, data=NULL, na.action = na.pass, ...)
# S3 method for 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)
In the "formula"
method, a formula. In the
"default"
method, a data.frame.
Any variables that are used in a formula with +
should be
numeric. Factors are not rejected, but their levels will be
combined strangely.
other arguments to xyplot
.
data.frame
See
In the "default"
method, a data.frame with the
same number of rows as the data.frame in x
.
In the "default"
method, a data.frame with the
same number of rows as the data.frame in x
.
Alternate ways to get to the
scales(relation=)
arguments to xyplot
.
Alternate ways to get to the
scales(limits=)
arguments to xyplot
.
Display the correlation and/or the regression
coefficient for lm(y ~ x)
for each panel in an additional
strip label.
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
.
number of significant digits for the correlation coefficient.
Alternate ways to get to the
between=
argument to xyplot
.
strip function that knows how to handle the corr
and
beta
displays.
arguments to xyplot
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
and
panel.abline(lm(y~x, na.action=na.exclude))
.
Note that we use
na.action=na.exclude
inside lm
.
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 left-hand side is
paired with the variable in the corresponding position in the right-hand side
and only those pairs are plotted. Both sides must have the same number of
variables.
Defaults to TRUE
. See details.
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.
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.
Heiberger, Richard M. and Holland, Burt (2015). Statistical Analysis and Data Display: An Intermediate Course with Examples in R. Second Edition. Springer-Verlag, New York. https://link.springer.com/us/book/9781493921218
# NOT RUN {
## 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 + w ~ x , data=tmp, corr=TRUE, beta=TRUE, cartesian=FALSE, layout=c(1,2))
xysplom(y + x ~ z | g, data=tmp, layout=c(2,2))
xysplom(y + x ~ z | g, data=tmp, cartesian=FALSE)
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)
# }
# NOT RUN {
## xyplot in R has many similar capabilities with xysplom
if.R(r=
xyplot(w + z ~ x + y, data=tmp, outer=TRUE)
,s=
{}
)
# }
# NOT RUN {
# }
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