scat1d {Hmisc} | R Documentation |
scat1d
adds tick marks (bar codes. rug plot) on any of the four
sides of an existing plot, corresponding with non-missing values of a
vector x
. This is used to show the data density. Can also place
the tick marks along a curve by specifying y-coordinates to go along
with the x
values.
If any two values of x
are within eps*w
of each other, where eps
defaults to .001 and w
is the span of the intended axis, values of
x
are jittered by adding a value uniformly distributed in
[-jitfrac*w, jitfrac*w]
, where jitfrac
defaults to .008.
Specifying preserve=TRUE
invokes jitter2
with a different logic of
jittering. Allows plotting random sub-segments to handle very large
x
vectors (see tfrac
).
jitter2
is a generic method for jittering, which does not add
random noise. It retains unique values and ranks, and randomly
spreads duplicate values at equidistant positions within limits of
enclosing values. jitter2
is especially useful for numeric
variables with discrete values, like rating scales. Missing values
are allowed and are returned. Currently implemented methods are
jitter2.default
for vectors and jitter2.data.frame
which returns
a data.frame with each numeric column jittered.
datadensity
is a generic method used to show data densities in more
complex situations. In the Design library there is a datadensity
method for use with plot.Design
. Here, another datadensity
method
is defined for data frames. Depending on the which
argument, some
or all of the variables in a data frame will be displayed, with
scat1d
used to display continuous variables and, by default, bars
used to display frequencies of categorical, character, or discrete
numeric variables. For such variables, when the total length of value
labels exceeds 200, only the first few characters from each level are used.
By default, datadensity.data.frame
will construct
one axis (i.e., one strip) per variable in the data frame. Variable
names appear to the left of the axes, and the number of missing values
(if greater than zero) appear to the right of the axes. An optional
group
variable can be used for stratification, where the different
strata are depicted using different colors. If the q
vector is
specified, the desired quantiles (over all group
s) are displayed
with solid triangles below each axis.
When the sample size exceeds 2000 (this value may be modified using
the nhistSpike
argument, datadensity
calls histSpike
instead of
scat1d
to show the data density for numeric variables. This results
in a histogram-like display that makes the resulting graphics file
much smaller. In this case, datadensity
uses the minf
argument
(see below) so that very infrequent data values will not be lost on
the variable's axis, although this will slightly distort the histogram.
histSpike
is another method for showing a high-resolution data
distribution that is particularly good for very large datasets (say
n
> 1000). By
default, histSpike
bins the continuous x
variable into 100
equal-width bins and then computes the frequency counts within bins
(if n
does not exceed 10, no binning is done).
If add=FALSE
(the default), the function displays either proportions or
frequencies as in a vertical histogram. Instead of bars, spikes are
used to depict the frequencies. If add=FALSE
, the function assumes you
are adding small density displays that are intended to take up a small
amount of space in the margins of the overall plot. The frac
argument is used as with scat1d
to determine the relative length of
the whole plot that is used to represent the maximum frequency. No
jittering is done by histSpike
.
histSpike
can also graph a kernel density estimate for x
, or add a
small density curve to any of 4 sides of an existing plot. When y
or curve
is specified, the density or spikes are drawn with respect
to the curve rather than the x-axis.
scat1d(x, side=3, frac=0.02, jitfrac=0.008, tfrac, eps=ifelse(preserve,0,.001), lwd=0.1, col=par("col"), y=NULL, curve=NULL, bottom.align=FALSE, preserve=FALSE, fill=1/3, limit=TRUE, nhistSpike=2000, nint=100, type=c('proportion','count','density'), grid=FALSE, ...) jitter2(x, ...) ## Default S3 method: jitter2(x, fill=1/3, limit=TRUE, eps=0, presorted=FALSE, ...) ## S3 method for class 'data.frame' jitter2(x, ...) datadensity(object, ...) ## S3 method for class 'data.frame' datadensity(object, group, which=c("all","continuous","categorical"), method.cat=c("bar","freq"), col.group=1:10, n.unique=10, show.na=TRUE, nint=1, naxes, q, bottom.align=nint>1, cex.axis=sc(.5,.3), cex.var=sc(.8,.3), lmgp=NULL, tck=sc(-.009,-.002), ranges=NULL, labels=NULL, ...) # sc(a,b) means default to a if number of axes <= 3, b if >=50, use # linear interpolation within 3-50 histSpike(x, side=1, nint=100, frac=.05, minf=NULL, mult.width=1, type=c('proportion','count','density'), xlim=range(x), ylim=c(0,max(f)), xlab=deparse(substitute(x)), ylab=switch(type,proportion='Proportion', count ='Frequency', density ='Density'), y=NULL, curve=NULL, add=FALSE, bottom.align=type=='density', col=par('col'), lwd=par('lwd'), grid=FALSE, ...)
x |
a vector of numeric data, or a data frame (for |
object |
a data frame or list (even with unequal number of observations per
variable, as long as |
side |
axis side to use (1=bottom (default for |
frac |
fraction of smaller of vertical and horizontal axes for tick mark lengths.
Can be negative to move tick marks outside of plot. For |
jitfrac |
fraction of axis for jittering. If <=0, no jittering is done. If
|
tfrac |
fraction of tick mark to actually draw. If |
eps |
fraction of axis for determining overlapping points in |
lwd |
line width for tick marks, passed to |
col |
color for tick marks, passed to |
y |
specify a vector the same length as |
curve |
a list containing elements |
bottom.align |
set to |
preserve |
set to |
fill |
maximum fraction of the axis filled by jittered values. If |
limit |
specifies a limit for maximum shift in jittered values. Duplicate
values will be spread within |
nhistSpike |
If the number of observations exceeds or equals |
type |
used by or passed to |
grid |
set to |
nint |
number of intervals to divide each continuous variable's axis for
|
... |
optional arguments passed to |
presorted |
set to |
group |
an optional stratification variable, which is converted to a |
which |
set |
method.cat |
set |
col.group |
colors representing the |
n.unique |
number of unique values a numeric variable must have before it is considered to be a continuous variable |
show.na |
set to |
naxes |
number of axes to draw on each page before starting a new plot. You
can set |
q |
a vector of quantiles to display. By default, quantiles are not shown. |
cex.axis |
character size for draw labels for axis tick marks |
cex.var |
character size for variable names and frequence of |
lmgp |
spacing between numeric axis labels and axis (see |
tck |
see |
ranges |
a list containing ranges for some or all of the numeric variables. If
|
labels |
a vector of labels to use in labeling the axes for
|
minf |
For |
mult.width |
multiplier for the smoothing window width computed by |
xlim |
a 2-vector specifying the outer limits of |
ylim |
|
xlab |
|
ylab |
|
add |
set to |
For scat1d
the length of line segments used is frac*min(par()$pin)
/ par()$uin[opp]
data units, where opp
is the index of the opposite
axis and frac
defaults to .02. Assumes that plot
has already been
called. Current par("usr")
is used to determine the range of data
for the axis of the current plot. This range is used in jittering and
in constructing line segments.
histSpike
returns the actual range of x
used in its binning
scat1d
adds line segments to plot. datadensity.data.frame
draws a
complete plot. histSpike
draws a complete plot or adds to an
existing plot.
Frank Harrell
Department of Biostatistics
Vanderbilt University
Charlottesville VA, USA
f.harrell@vanderbilt.edu
Martin Maechler (improved scat1d
)
Seminar fuer Statistik
ETH Zurich SWITZERLAND
maechler@stat.math.ethz.ch
Jens Oehlschlaegel-Akiyoshi (wrote jitter2
)
Center for Psychotherapy Research
Christian-Belser-Strasse 79a
D-70597 Stuttgart Germany
oehl@psyres-stuttgart.de
segments
, jitter
, rug
, plsmo
, stripplot
,
hist.data.frame
,Ecdf
,
hist
, histogram
, table
, density
plot(x <- rnorm(50), y <- 3*x + rnorm(50)/2 ) scat1d(x) # density bars on top of graph scat1d(y, 4) # density bars at right histSpike(x, add=TRUE) # histogram instead, 100 bins histSpike(y, 4, add=TRUE) histSpike(x, type='density', add=TRUE) # smooth density at bottom histSpike(y, 4, type='density', add=TRUE) smooth <- lowess(x, y) # add nonparametric regression curve lines(smooth) # Note: plsmo() does this scat1d(x, y=approx(smooth, xout=x)$y) # data density on curve scat1d(x, curve=smooth) # same effect as previous command histSpike(x, curve=smooth, add=TRUE) # same as previous but with histogram histSpike(x, curve=smooth, type='density', add=TRUE) # same but smooth density over curve plot(x <- rnorm(250), y <- 3*x + rnorm(250)/2) scat1d(x, tfrac=0) # dots randomly spaced from axis scat1d(y, 4, frac=-.03) # bars outside axis scat1d(y, 2, tfrac=.2) # same bars with smaller random fraction x <- c(0:3,rep(4,3),5,rep(7,10),9) plot(x, jitter2(x)) # original versus jittered values abline(0,1) # unique values unjittered on abline points(x+0.1, jitter2(x, limit=FALSE), col=2) # allow locally maximum jittering points(x+0.2, jitter2(x, fill=1), col=3); abline(h=seq(0.5,9,1), lty=2) # fill 3/3 instead of 1/3 x <- rnorm(200,0,2)+1; y <- x^2 x2 <- round((x+rnorm(200))/2)*2 x3 <- round((x+rnorm(200))/4)*4 dfram <- data.frame(y,x,x2,x3) plot(dfram$x2, dfram$y) # jitter2 via scat1d scat1d(dfram$x2, y=dfram$y, preserve=TRUE, col=2) scat1d(dfram$x2, preserve=TRUE, frac=-0.02, col=2) scat1d(dfram$y, 4, preserve=TRUE, frac=-0.02, col=2) pairs(jitter2(dfram)) # pairs for jittered data.frame # This gets reasonable pairwise scatter plots for all combinations of # variables where # # - continuous variables (with unique values) are not jittered at all, thus # all relations between continuous variables are shown as they are, # extreme values have exact positions. # # - discrete variables get a reasonable amount of jittering, whether they # have 2, 3, 5, 10, 20 \dots levels # # - different from adding noise, jitter2() will use the available space # optimally and no value will randomly mask another # # If you want a scatterplot with lowess smooths on the *exact* values and # the point clouds shown jittered, you just need # pairs( dfram ,panel=function(x,y) { points(jitter2(x),jitter2(y)) lines(lowess(x,y)) } ) datadensity(dfram) # graphical snapshot of entire data frame datadensity(dfram, group=cut2(dfram$x2,g=3)) # stratify points and frequencies by # x2 tertiles and use 3 colors # datadensity.data.frame(split(x, grouping.variable)) # need to explicitly invoke datadensity.data.frame when the # first argument is a list