# plsmo

##### Plot smoothed estimates

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
non-panel version of `plsmo`

allows `y`

to be a matrix, for
which smoothing is done separately over its columns. If both
`group`

and multi-column `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 x-range 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`

.

- Keywords
- hplot, smooth, nonparametric

##### Usage

```
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)

##### Arguments

- x
vector of x-values, NAs allowed

- y
vector or matrix of y-values, NAs allowed

- method
`"lowess"`

(the default),`"supsmu"`

,`"raw"`

to not smooth at all, or`"intervals"`

to use intervals (see above)- xlab
x-axis label iff add=F. Defaults of label(x) or argument name.

- ylab
y-axis 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

`y`

and`group`

combinations- col
color for each curve, corresponding to

`group`

. Default is current`par("col")`

.- lwd
vector of line widths for the curves, corresponding to

`group`

. Default is current`par("lwd")`

.`lwd`

can also be specified as an element of`label.curves`

if`label.curves`

is a list.- iter
iter parameter if

`method="lowess"`

, default=0 if`y`

is binary, and 3 otherwise.- bass
bass parameter if

`method="supsmu"`

, default=0.- f
passed to the

`lowess`

function, for`method="lowess"`

- mobs
for

`method='intervals'`

, the target number of observations per interval- trim
only plots smoothed estimates between trim and 1-trim 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

`fun`

is given the y-values are transformed by`fun()`

- ifun
a summary statistic function to apply to the

`y`

-variable for`method='intervals'`

. Default is`mean`

.- group
a variable, either a

`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
a character string to appear in group of group labels. The presence of

`prefix`

ensures that`labcurve`

will be called even when`add=TRUE`

.- xlim
a vector of 2 x-axis limits. Default is observed range.

- ylim
a vector of 2 y-axis limits. Default is observed range.

- label.curves
set to

`FALSE`

to prevent`labcurve`

from being called to label multiple curves corresponding to`group`

s. Set to a list to pass options to`labcurve`

.`lty`

and`col`

are passed to`labcurve`

automatically.- datadensity
set to

`TRUE`

to draw tick marks on each curve, using x-coordinates of the raw data`x`

values. This is done using`scat1d`

.- scat1d.opts
a list of options to hand to

`scat1d`

- lines.
set to

`FALSE`

to suppress smoothed curves from being drawn. This can make sense if`datadensity=TRUE`

.- subset
a logical or integer vector specifying a subset to use for processing, with respect too all variables being analyzed

- grid
set to

`TRUE`

if the R`grid`

package drew the current plot- evaluate
number of points to keep from smoother. If specified, an equally-spaced grid of

`evaluate`

`x`

values will be obtained from the smoother using linear interpolation. This will keep from plotting an enormous number of points if the dataset contains a very large number of unique`x`

values.- …
optional arguments that are passed to

`scat1d`

, or optional parameters to pass to`plsmo`

from`panel.plsmo`

. See optional arguments for`plsmo`

above.- type
set to

`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.- pch,cex,font
vectors of graphical parameters corresponding to the

`group`

s (scalars if`group`

is absent). By default, the parameters set up by`trellis`

will be used.

##### Value

`plsmo`

returns a list of curves (x and y coordinates) that was passed to `labcurve`

##### Side Effects

plots, and `panel.plsmo`

creates the `Key`

function in the session frame.

##### See Also

`lowess`

, `supsmu`

, `label`

,
`quantile`

, `labcurve`

, `scat1d`

,
`xyplot`

, `panel.superpose`

,
`panel.xyplot`

, `stat_plsmo`

,
`histSpikeg`

##### Examples

```
# NOT RUN {
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
#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
# }
```

*Documentation reproduced from package Hmisc, version 4.3-0, License: GPL (>= 2)*