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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
groups) 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.
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 NAs 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.
NAs
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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