Description Usage Arguments Value Details Methods Author(s) References See Also Examples
Methods for classes SECdistrUv
and SECdistrMv
1 2 3 4 5 6 
x 
an object of class 
range 
in the univariate case, a vector of length 2 which defines the plotting range; in the multivariate case, a matrix with two rows where each column defines the plotting range of the corresponding component variable. If missing, a sensible choice is made. 
probs 
a vector of probability values. In the univariate case, the
corresponding quantiles are plotted on the horizontal axis; it can be
skipped by setting 
npt 
a numeric value or vector (in the univariate and in the
multivariate case, respectively) to assign the number of evaluation points
of the distribution, on an equallyspaced grid over the 
landmarks 
a character string which affects the placement of some
landmark values in the multivariate case, that is, the origin, the mode
and the mean (or its substitute pseudomean), which are all aligned.
Possible values: 
main 
a character string for main title; if missing, one is built from the available ingredients. 
comp 
a subset of the vector 
compLabs 
a vector of character strings or expressions used to denote
the variables in the plot;
if missing, 
data 
an optional set of data of matching dimensionity of

data.par 
an optional list of graphical parameters used for plotting

gap 
a numeric value which regulates the gap between panels of a
multivariate plot when 
... 
additional graphical parameters 
an invisible list. In the univariate case the list has three components:
the input object representing the distribution and two numeric vectors with
the coordinates of the plotted density values.
In the multivariate case, the first element of the list is the input object
representing the distribution and all subsequent list elements are lists with
components of the panels comprising the matrix plot;
the elements of these sublists are:
the vectors of x
and y
coordinates, the names of the variables,
the density values at the (x,y)
points, a vector of the density levels
of the curves appearing in each panel plot, with the corresponding approximate
probability content as a vector attribute.
For univariate density plots, probs
are used to compute quantiles
from the appropriate distribution, and these are superimposed to the plot of
the density function, unless probs=NULL
. In the multivariate case,
each bivariate plot is constructed as a collection of contour curves,
one curve for each probability level; consequently, probs
cannot be
missing or NULL
. The level of the density contour lines are chosen
so that each curve circumscribes a region with the quoted probability,
to a good degree of approssimation; for additional information, see
Azzalini and Capitanio (2014), specifically Complement 5.2 and p.179,
and references therein.
signature(x = "SECdistrUv")
Plot an object x
of class SECdistrUv
.
signature(x = "SECdistrMv")
Plot an object x
of class SECdistrMv
.
Adelchi Azzalini
Azzalini, A. with the collaboration of Capitanio, A. (2014). The SkewNormal and Related Families. Cambridge University Press, IMS Monographs series.
makeSECdistr
, summary.SECdistr
,
dp2cp
1 2 3 4 5 6 7 8 9 10 11 12 13 14  # d=1
f1 < makeSECdistr(dp=c(3,2,5), family="SC", name="Univariate SkewCauchy")
plot(f1)
plot(f1, range=c(3,40), probs=NULL, col=4)
#
# d=2
Omega2 < matrix(c(3, 3, 3, 5), 2, 2)
f2 < makeSECdistr(dp=list(c(10,30), Omega=Omega2, alpha=c(3, 5)),
family="sn", name="SN2d", compNames=c("x1","x2"))
plot(f2)
x2 < rmsn(100, dp=slot(f2,"dp"))
plot(f2, main="Distribution 'f2'", probs=c(0.5,0.9), cex.main=1.5, col=2,
cex=0.8, compLabs=c(expression(x[1]), expression(log(z[2]beta^{1/3}))),
data=x2, data.par=list(col=4, cex=0.6, pch=5))

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