plot.SECdistr {sn} | R Documentation |
SECdistrUv
and SECdistrMv
Methods for classes SECdistrUv
and SECdistrMv
## S4 method for signature 'SECdistrUv' plot(x, range, probs, main, npt = 251, ...) ## S4 method for signature 'SECdistrMv' plot(x, range, probs, npt, landmarks = "auto", main, comp, compLabs, data = NULL, data.par = NULL, gap = 0.5, ...)
x |
an object of the pertaining 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 equally-spaced 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 pseudo-mean), 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 |
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 Skew-Normal and Related Families. Cambridge University Press, IMS Monographs series.
makeSECdistr
, summary.SECdistr
,
dp2cp
# d=1 f1 <- makeSECdistr(dp=c(3,2,5), family="SC", name="Univariate Skew-Cauchy") 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="SN-2d", 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))