Produce tailored histogram plots and kernel density/log-density estimate plots.
histPlot(x, labels = TRUE, col = "steelblue", fit = TRUE,
title = TRUE, grid = TRUE, rug = TRUE, skip = FALSE, ...)
densityPlot(x, labels = TRUE, col = "steelblue", fit = TRUE, hist = TRUE,
title = TRUE, grid = TRUE, rug = TRUE, skip = FALSE, ...)
logDensityPlot(x, labels = TRUE, col = "steelblue", robust = TRUE,
title = TRUE, grid = TRUE, rug = TRUE, skip = FALSE, ...)
NULL
, invisibly. The functions are used for the side effect of
producing a plot.
an object of class "timeSeries"
.
a logical flag, should the plot be returned with default labels
and decorated in an automated way? By default TRUE
.
the color for the series. In the univariate case use just a color
name like the default, col="steelblue"
, in the multivariate
case we recommend to select the colors from a color palette,
e.g. col=heat.colors(ncol(x))
.
a logical flag, should a fit be added to the plot?
a logical flag, by default TRUE
. Should a histogram be laid
under the plot?
a logical flag, by default TRUE
. Should a default title be
added to the plot?
a logical flag, should a grid be added to the plot? By default
TRUE
. To plot a horizontal lines only use grid="h"
and
for vertical lines use grid="h"
, respectively.
a logical flag, by default TRUE. Should a rug representation of the data be added to the plot?
a logical flag, should zeros be skipped in the return Series?
a logical flag, by default TRUE
. Should a robust fit be added
to the plot?
optional arguments to be passed on.
histPlot
produces a tailored histogram plot.
densityPlot
produces a tailored kernel density estimate plot.
logDensityPlot
produces a tailored log kernel density estimate plot.
## data
data(LPP2005REC, package = "timeSeries")
SPI <- LPP2005REC[, "SPI"]
plot(SPI, type = "l", col = "steelblue", main = "SP500")
abline(h = 0, col = "grey")
histPlot(SPI)
densityPlot(SPI)
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