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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 $\code{eps}*\var{w}$ 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 $[-\code{jitfrac}*\var{w},
\code{jitfrac}*\var{w}]$, 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 (seetfrac
).
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. 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 distortthe histogram.
histSpike
is another method for showing a high-resolution data
distribution that is particularly good for very large datasets (say
$\code{n} > 1000$). 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. histSpikeg
is similar to histSpike
but is for adding layers
to a ggplot2
graphics object or traces to a plotly
object.
histSpikeg
can also add lowess
curves to the plot.
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, ...)
"jitter2"(x, fill=1/3, limit=TRUE, eps=0, presorted=FALSE, ...)
"jitter2"(x, ...)
datadensity(object, ...)
"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, ...)
histSpikeg(formula=NULL, predictions=NULL, data, plotly=NULL, lowess=FALSE, xlim=NULL, ylim=NULL, side=1, nint=100, frac=function(f) 0.01 + 0.02*sqrt(f-1)/sqrt(max(f,2)-1), span=3/4, histcol='black', showlegend=TRUE)
jitter2
)
group
is notspecified)
histSpike
), 2=left,
3=top (default for scat1d
), 4=right)
histSpike
, this is the relative y-direction length to be used for the
largest frequency. When scat1d
calls histSpike
, it
multiplies its frac
argument by 2.5. For histSpikeg
,
frac
is a function of f
, the vector of all frequencies. The
default function scales tick marks so that they are between 0.01 and
0.03 of the y range, linearly scaled in the square root of the
frequency less one.
preserve=TRUE, the amount of
jittering is independent of jitfrac.
tfrac of the line segment at
each point. This is useful for very large samples or ones with some
very dense points. The default value is 1 if the number of
non-missing observations n
is less than 125, and
$\max{(.1, 125/\var{n})}$ otherwise.
x
. For
preserve=TRUE
the default is 0 and original unique values are
retained, bigger values of eps tends to bias observations from dense
to sparse regions, but ranks are still preserved.
segments
segments
x
to draw tick marks
along a curve instead of by one of the axes. The y
values
are often predicted values from a model. The side
argument
is ignored when y
is given. If the curve is already
represented as a table look-up, you may specify it using the
curve
argument instead. y
may be a scalar to use a
constant verticalplacement.
x
and y
for which linear
interpolation is used to derive y
values corresponding to
values of x
. This results in tick marks being drawn along
the curve. For histSpike
, interpolated y
values are
derived for binmidpoints.
TRUE
to have the bottoms of tick marks (for
side=1
or side=3
) aligned at the y-coordinate. The
default behavior is to center the tick marks. For
datadensity.data.frame
, bottom.align
defaults to
TRUE
if nint>1
. In other words, if you are only
labeling the first and last axis tick mark, the scat1d
tick
marks are centered on the variable's axis.
TRUE
to invoke jitter2
d
are duplicated values between a lower value l and upper value
u, then d will be spread within
$
+/- \code{fill}*min(\var{u}-\var{d},\var{d}-\var{l})/2$.
TRUE
restricts jittering to the smallest
$\min{(\var{u}-\var{d},\var{d}-\var{l})}/2$ observed and results
in equal amount of jittering for all d. Setting to
FALSE
allows for locally different amount of jittering, using
maximum space available.
nhistSpike
,
scat1d
will automatically call histSpike
to draw the
data density, to prevent the graphics file from being too large.
histSpike
. Set to "count"
to
display frequency counts rather than relative frequencies, or
"density"
to display a kernel density estimate computed using
the density
function.
TRUE
if the R grid
package is in effect for
the current plot
datadensity
. For histSpike
, is the number of
equal-width intervals for which to bin x
, and if instead
nint
is a character string (e.g.,nint="all"
), the
frequency tabulation is done with no binning. In other words,
frequencies for all unique values of x
are derived and
plotted. For histSpikeg
, if x
has no more than
nint
unique values, all observed values are used, otherwise
the data are rounded before tabulation so that there are no more
than nint
intervals.
scat1d
from datadensity
or to histSpike
from scat1d
. For histSpikep
are passed to the lines
list to add_trace
.
TRUE
to prevent from sorting for determining the order
$\var{l}factor
vector if it is not one already
which="continuous"
to only plot continuous variables, or
which="categorical"
to only plot categorical, character, or
discrete numeric ones. By default, all types of variables are
depicted.
method.cat="freq"
to depict frequencies of categorical
variables with digits representing the cell frequencies, with size
proportional to the square root of the frequency. By default,
vertical bars are used.
group
strata. The vector of colors
is recycled to be the same length as the levels of group
.
FALSE
to suppress drawing the number of NA
s to
the right of each axis
naxes
larger than the number of variables in the data
frame if you want to compress the plot vertically.
NA
s
par
for
mgp
)
tck
under par
ranges
is not given or if a certain variable is not found
in the list, the empirical range, modified by pretty
, is
used. Example:
ranges=list(age=c(10,100), pressure=c(50,150))
.
datadensity.data.frame
. Default is to use the names of the
variable in the input data frame. Note: margin widths computed for
setting aside names of variables use the names, and not these
labels.
histSpike
, if minf
is specified low bin
frequencies are set to a minimum value of minf
times the
maximum bin frequency, so that rare data points will remain visible.
A good choice of minf
is 0.075.
datadensity.data.frame
passes minf=0.075
to
scat1d
to pass to histSpike
. Note that specifying
minf
will cause the shape of the histogram to be distorted
somewhat.
histSpike
when type="density"
x
for binning (and
plotting, if add=FALSE
and nint
is a number). For
histSpikeg
, observations outside the xlim
range are ignored.
add=FALSE
). Often needed for
histSpikeg
to help scale the tick mark line segments.
add=FALSE
); default is name of input argument
x
add=FALSE
)
TRUE
to add the spike-histogram to an existing plot,
to show marginal data densities
y ~ x1
or y ~ x1 + ...
where
y
is the name of the y
-axis variable being plotted
with ggplot
, x1
is the name of the x
-axis
variable, and optional ... are variables used by
ggplot
to produce multiple curves on a panel and/or facets.
ggplot
, containing x
and y
coordinates of curves. If omitted, spike histograms
are drawn at the bottom (default) or top of the plot according to
side
.
histSpikeg
is a mandatory data frame containing raw data whose
frequency distribution is to be summarized, using variables in
formula
.
plotly
object. If not NULL
,
histSpikeg
uses plotly
instead of ggplot
.TRUE
to have histSpikeg
add a geom_line
layer to the ggplot2
graphic, containing
lowess()
nonparametric smoothers. This causes the
returned value of histSpikeg
to be a list with two
components: "hist"
and "lowess"
each containing
a layer. Fortunately, ggplot2
plots both layers
automatically. If the dependent variable is binary,
iter=0
is passed to lowess
so that outlier
detection is turned off; otherwise iter=3
is passed.
lowess
as the f
argumenthistSpikeg
. Default is black. Set to any color or to
"default"
to use the prevailing colors for the
graphic.
FALSE
too have the added plotly
traces not have entries in the plot legendhistSpike
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.scat1d
the length of line segments used is
frac*min(par()$pin)/par()$uin[opp]
data units, where
opp is the index of the opposite axis and frac
defaults
to .02. Assumes that plot
has already been called. Current
par("usr")
is used to determine the range of data for the axis
of the current plot. This range is used in jittering and in
constructing line segments.
segments
, jitter
, rug
,
plsmo
, lowess
, stripplot
,
hist.data.frame
,Ecdf
, hist
,
histogram
, table
,
density
, stat_plsmo
, histboxp
plot(x <- rnorm(50), y <- 3*x + rnorm(50)/2 )
scat1d(x) # density bars on top of graph
scat1d(y, 4) # density bars at right
histSpike(x, add=TRUE) # histogram instead, 100 bins
histSpike(y, 4, add=TRUE)
histSpike(x, type='density', add=TRUE) # smooth density at bottom
histSpike(y, 4, type='density', add=TRUE)
smooth <- lowess(x, y) # add nonparametric regression curve
lines(smooth) # Note: plsmo() does this
scat1d(x, y=approx(smooth, xout=x)$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
## Not run:
# require(rms)
# f <- lrm(y ~ blood.pressure + sex * (age + rcs(cholesterol,4)),
# data=d)
# p <- Predict(f, cholesterol, sex)
# g <- ggplot(p, aes(x=cholesterol, y=yhat, color=sex)) + geom_line() +
# xlab(xl2) + ylim(-1, 1)
# g <- g + geom_ribbon(data=p, aes(ymin=lower, ymax=upper), alpha=0.2,
# linetype=0, show_guide=FALSE)
# g + histSpikeg(yhat ~ cholesterol + sex, p, d)
#
# # colors <- c('red', 'blue')
# # p <- plot_ly(x=x, y=y, color=g, colors=colors, mode='markers')
# # histSpikep(p, x, y, z, color=g, colors=colors)
# ## End(Not run)
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