plsmo  R Documentation 
Plot smoothed estimates of x vs. y, handling missing data for lowess
or supsmu, and adding axis labels. Optionally suppresses plotting
extrapolated estimates. An optional 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. The
nonpanel version of plsmo
allows y
to be a matrix, for
which smoothing is done separately over its columns. If both
group
and multicolumn y
are used, the number of curves
plotted is the product of the number of groups and the number of
y
columns.
method='intervals'
is often used when y is binary, as it may be
tricky to specify a reasonable smoothing parameter to lowess
or
supsmu
in this case. The 'intervals'
method uses the
cut2
function to form intervals of x containing a target of
mobs
observations. For each interval the ifun
function
summarizes y, with the default being the mean (proportions for binary
y). The results are plotted as step functions, with vertical
discontinuities drawn with a saturation of 0.15 of the original color.
A plus sign is drawn at the mean x within each interval.
For this approach, the default xrange is the entire raw data range,
and trim
and evaluate
are ignored. For
panel.plsmo
it is best to specify type='l'
when using
'intervals'
.
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
, or pass
method='intervals'
. panel.plsmo
senses when a group
variable is specified to xyplot
so
that it can invoke panel.superpose
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 group
s.
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.
For ggplot2
graphics the counterparts are
stat_plsmo
and histSpikeg
.
plsmo(x, y, method=c("lowess","supsmu","raw","intervals"), xlab, ylab,
add=FALSE, lty=1 : lc, col=par("col"), lwd=par("lwd"),
iter=if(length(unique(y))>2) 3 else 0, bass=0, f=2/3, mobs=30, trim,
fun, ifun=mean, group, prefix, xlim, ylim,
label.curves=TRUE, datadensity=FALSE, scat1d.opts=NULL,
lines.=TRUE, subset=TRUE,
grid=FALSE, evaluate=NULL, ...)
#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, scat1d.opts=NULL, \dots)
x 
vector of xvalues, NAs allowed 
y 
vector or matrix of yvalues, NAs allowed 
method 

xlab 
xaxis label iff add=F. Defaults of label(x) or argument name. 
ylab 
yaxis label, like xlab. 
add 
Set to T to call lines instead of plot. Assumes axes already labeled. 
lty 
line type, default=1,2,3,..., corresponding to columns of 
col 
color for each curve, corresponding to 
lwd 
vector of line widths for the curves, corresponding to 
iter 
iter parameter if 
bass 
bass parameter if 
f 
passed to the 
mobs 
for 
trim 
only plots smoothed estimates between trim and 1trim quantiles of x. Default is to use 10th smallest to 10th largest x in the group if the number of observations in the group exceeds 200 (0 otherwise). Specify trim=0 to plot over entire range. 
fun 
after computing the smoothed estimates, if 
ifun 
a summary statistic function to apply to the

group 
a variable, either a 
prefix 
a character string to appear in group of group labels. The presence of

xlim 
a vector of 2 xaxis limits. Default is observed range. 
ylim 
a vector of 2 yaxis limits. Default is observed range. 
label.curves 
set to 
datadensity 
set to 
scat1d.opts 
a list of options to hand to 
lines. 
set to 
subset 
a logical or integer vector specifying a subset to use for processing, with respect too all variables being analyzed 
grid 
set to 
evaluate 
number of points to keep from smoother. If specified, an
equallyspaced grid of 
... 
optional arguments that are passed to 
type 
set to 
pch , cex , font 
vectors of graphical parameters corresponding to the 
plsmo
returns a list of curves (x and y coordinates) that was passed to labcurve
plots, and panel.plsmo
creates the Key
function in the session frame.
lowess
, supsmu
, label
,
quantile
, labcurve
, scat1d
,
xyplot
, panel.superpose
,
panel.xyplot
, stat_plsmo
,
histSpikeg
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','nonblack'), 500, TRUE)
plsmo(age, survival.time < 1, fun=qlogis, group=sex) # plot logit by sex
#Bivariate Y
sbp < 120 + (age  50)/10 + rnorm(500, 0, 8) + 5 * (sex == 'male')
dbp < 80 + (age  50)/10 + rnorm(500, 0, 8)  5 * (sex == 'male')
Y < cbind(sbp, dbp)
plsmo(age, Y)
plsmo(age, Y, group=sex)
#Plot points and smooth trend line using trellis
# (add type='l' to suppress points or type='p' to suppress trend lines)
require(lattice)
xyplot(survival.time ~ age, panel=panel.plsmo)
#Do this for multiple panels
xyplot(survival.time ~ age  sex, panel=panel.plsmo)
#Repeat this using equal sample size intervals (n=25 each) summarized by
#the median, then a proportion (mean of binary y)
xyplot(survival.time ~ age  sex, panel=panel.plsmo, type='l',
method='intervals', mobs=25, ifun=median)
ybinary < ifelse(runif(length(sex)) < 0.5, 1, 0)
xyplot(ybinary ~ age, groups=sex, panel=panel.plsmo, type='l',
method='intervals', mobs=75, ifun=mean, xlim=c(0, 120))
#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
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