group variable can be
specified to compute and plot the smooth curves by levels of
group
. When
group is present, the
datadensity
option will draw tick marks showing the location of the raw
x
-values, separately for each curve.
plsmo has an
option to plot connected points for raw data, with no smoothing.
panel.plsmo is a
panel function for
trellis for the
xyplot
function that uses
plsmo and its options to draw
one or more nonparametric function estimates on each panel. This has
advantages over using
xyplot with
panel.xyplot and
panel.loess
: (1) by default it will invoke
labcurve to
label the curves where they are most separated, (2) the
datadensity
option will put rug plots on each curve (instead of a
single rug plot at the bottom of the graph), and (3) when
panel.plsmo
invokes
plsmo it can use the "super smoother"
(
supsmu function) instead of
lowess.
panel.plsmo
senses when a
group variable is specified to
xyplot so
that it can invoke
instead of
panel.xyplot
. Using
panel.plsmo through
trellis
has some advantages over calling
plsmo directly in that
conditioning variables are allowed and
trellis uses nicer fonts
etc.
When a
group variable was used,
panel.plsmo creates a function
Key
in the session frame that the user can invoke to draw a key for
individual data point symbols used for the
groups.
By default, the key is positioned at the upper right
corner of the graph. If
Key(locator(1)) is specified, the key will
appear so that its upper left corner is at the coordinates of the
mouse click.
plsmo(x, y, method=c("lowess","supsmu","raw"), xlab, ylab,
add=FALSE, lty=1:nlev, col=par("col"), lwd=par("lwd"),
iter=if(length(unique(y))>2) 3 else 0, bass=0, trim,
fun, group, prefix, xlim, ylim,
label.curves=TRUE, datadensity=FALSE, lines.=TRUE, subset=TRUE,
grid=FALSE, ...)
#To use panel function:
#xyplot(formula=y ~ x | conditioningvars, groups,
# panel=panel.plsmo, type='b',
# label.curves=TRUE,
# lwd = superpose.line$lwd,
# lty = superpose.line$lty,
# pch = superpose.symbol$pch,
# cex = superpose.symbol$cex,
# font = superpose.symbol$font,
# col = NULL, ...)
group
group. Default is
current
par("col").
group.
Default is current
par("lwd").
lwd can also be specified as an element of
label.curves if
label.curves is a list.
y is binary, and 3 otherwise.
fun is given the y-values
are transformed by
fun()
factor vector or one that will be converted to
factor by
plsmo, that is used to stratify the data so that separate
smooths may be computed
prefix ensures that
labcurve will be called even when
add=TRUE.
FALSE to prevent
labcurve from being called to label multiple
curves corresponding to
groups. Set to a list to pass options to
labcurve.
lty and
col are passed to
labcurve automatically.
TRUE to draw tick marks on each curve, using x-coordinates
of the raw data
x values. This is done using
scat1d.
FALSE to suppress smoothed curves from being drawn. This can
make sense if
datadensity=TRUE.
TRUE if the R
grid package drew the current plot
scat1d,
or optional parameters to pass to
plsmo from
panel.plsmo. See optional arguments for
plsmo above.
p to have
panel.plsmo plot points (and not call
plsmo),
l to call
plsmo and not plot points, or use the default
b to plot both.
groups (scalars
if
group is absent). By default, the parameters set up by
trellis will be used.
plsmo returns a list of curves (x and y coordinates) that was passed to
labcurve
set.seed(1)
x <- 1:100
y <- x + runif(100, -10, 10)
plsmo(x,y,"supsmu",xlab="Time of Entry")
#Use label(y) or "y" for ylab
plsmo(x,y,add=TRUE,lty=2)
#Add lowess smooth to existing plot, with different line type
age <- rnorm(500, 50, 15)
survival.time <- rexp(500)
sex <- sample(c('female','male'), 500, TRUE)
race <- sample(c('black','non-black'), 500, TRUE)
plsmo(age, survival.time < 1, fun=qlogis, group=sex) # plot logit by sex
#Plot points and smooth trend line using trellis
# (add type='l' to suppress points or type='p' to suppress trend lines)
if(.R.) library(lattice)
xyplot(survival.time ~ age, panel=panel.plsmo)
#Do this for multiple panels
xyplot(survival.time ~ age | sex, panel=panel.plsmo)
#Do this for subgroups of points on each panel, show the data
#density on each curve, and draw a key at the default location
xyplot(survival.time ~ age | sex, groups=race, panel=panel.plsmo,
datadensity=TRUE)
Key()
#Use wloess.noiter to do a fast weighted smooth
plot(x, y)
lines(wtd.loess.noiter(x, y))
lines(wtd.loess.noiter(x, y, weights=c(rep(1,50), 100, rep(1,49))), col=2)
points(51, y[51], pch=18) # show overly weighted point
#Try to duplicate this smooth by replicating 51st observation 100 times
lines(wtd.loess.noiter(c(x,rep(x[51],99)),c(y,rep(y[51],99)),
type='ordered all'), col=3)
#Note: These two don't agree exactly