In the HistData package, I have a data frame, PearsonLee, containing
observations on heights of parent and child, in weighted form:
library(HistData)
> str(PearsonLee)
'data.frame': 746 obs. of 6 variables:
$ child : num 59.5 59.5 59.5 60.5 60.5 61.5 61.5 61.5 61.5 61.5 ...
$ parent : num 62.5 63.5 64.5 62.5 66.5 59.5 60.5 62.5 63.5 64.5 ...
$ frequency: num 0.5 0.5 1 0.5 1 0.25 0.25 0.5 1 0.25 ...
$ gp : Factor w/ 4 levels "fd","fs","md",..: 2 2 2 2 2 2 2 2 2 2 ...
$ par : Factor w/ 2 levels "Father","Mother": 1 1 1 1 1 1 1 1 1 1 ...
$ chl : Factor w/ 2 levels "Daughter","Son": 2 2 2 2 2 2 2 2 2 2 ...
I want to make a 2x2 set of plots of child ~ parent | par+chl, with
regression lines and loess smooths, that
incorporate weights=frequency. The "frequencies" are not integers, so I
can't simply expand the
data frame.
I'd also like to use different colors for the regression and smoothed lines.
Here's what I've tried using xyplot, all unsuccessful. I suppose I
could also use ggplot2, if I could do what
I want.
xyplot(child ~ parent|par+chl, data=PearsonLee, weights=frequency,
type=c("p", "r", "smooth"))
xyplot(child ~ parent|par+chl, data=PearsonLee, type=c("p", "r", "smooth"))
panel.lmline and panel.smooth don't have a weights= argument, though
lm() and loess() do.
# Try to control line colors: unsuccessfully -- only one value of
col.lin is used
xyplot(child ~ parent|par+chl, data=PearsonLee, type=c("p", "r",
"smooth"), col.line=c("red", "blue"))
## try to use panel functions ... unsucessfully
xyplot(child ~ parent|par+chl, data=PearsonLee, type="p",
panel = function(x, y, ...) {
panel.xyplot(x, y, ...)
panel.lmline(x, y, col="blue", ...)
panel.smooth(x, y, col="red", ...)
}
)
The following, using base graphics, illustrates the difference between
the weighted and unweighted lines,
for the total data frame:
with(PearsonLee,
{
lim <- c(55,80)
xv <- seq(55,80, .5)
sunflowerplot(parent,child, number=frequency, xlim=lim, ylim=lim,
seg.col="gray", size=.1)
# unweighted
abline(lm(child ~ parent), col="green", lwd=2)
lines(xv, predict(loess(child ~ parent), data.frame(parent=xv)),
col="green", lwd=2)
# weighted
abline(lm(child ~ parent, weights=frequency), col="blue", lwd=2)
lines(xv, predict(loess(child ~ parent, weights=frequency),
data.frame(parent=xv)), col="blue", lwd=2)
})
thanks,
-Michael
--
Michael Friendly Email: friendly AT yorku DOT ca
Professor, Psychology Dept.
York University Voice: 416 736-5115 x66249 Fax: 416 736-5814
4700 Keele Street Web: http://www.datavis.ca
Toronto, ONT M3J 1P3 CANADA
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