plsmo: Plot smoothed estimates
In Hmisc: Harrell Miscellaneous
plsmo R Documentation
Plot smoothed estimates
Description
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 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.
For ggplot2 graphics the counterparts are
stat_plsmo and histSpikeg.
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 groups. 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 groups (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
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
Hmisc documentation built on June 22, 2024, 12:19 p.m.
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| plsmo | R Documentation |
Plot smoothed estimates
Description
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 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.
For ggplot2 graphics the counterparts are
stat_plsmo and histSpikeg.
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 |
|
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 |
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 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 |
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 x-axis limits. Default is observed range. |
ylim |
a vector of 2 y-axis 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
equally-spaced grid of |
... |
optional arguments that are passed to |
type |
set to |
pch, cex, font |
vectors of graphical parameters corresponding to the |
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
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
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