| SNorm-class | R Documentation |
The skew normal distribution.
Objects can be created by calls of the form new("SNorm", mean, sd,xi).
More frequently they are created via the generating function
SNorm.
imgObject of class "Reals".
paramObject of class "SNormParameter".
rrgpd
ddgpd
ppgpd, but vectorized and with special treatment of
arguments lower.tail and log.p
qqgpd, but vectorized and with special treatment of
arguments lower.tail and log.p
gaps(numeric) matrix or NULL
.withArithlogical: used internally to issue warnings as to interpretation of arithmetics
.withSimlogical: used internally to issue warnings as to accuracy
.logExactlogical: used internally to flag the case where there are explicit formulae for the log version of density, cdf, and quantile function
.lowerExactlogical: used internally to flag the case where there are explicit formulae for the lower tail version of cdf and quantile function
Class "AbscontDistribution", directly.
Class "UnivariateDistribution", by class "AbscontDistribution".
Class "Distribution", by class "AbscontDistribution".
signature(object = "SNorm"): wrapped access method for
slot xi of slot param.
signature(object = "SNorm"): wrapped access method for
slot mean of slot param.
signature(object = "SNorm"): wrapped access method for
slot nu of slot param.
signature(x = "SNorm"): wrapped access method for
slot sd of slot param.
signature(object = "SNorm"): wrapped replace method for
slot xi of slot param.
signature(object = "SNorm"): wrapped replace method for
slot mean of slot param.
signature(object = "SNorm"): wrapped replace method for
slot nu of slot param.
signature(x = "SNorm"): wrapped replace method for
slot sd of slot param.
This class is based on the code provided by the package fGarch by Diethelm Wuertz
Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.de
dsnorm, AbscontDistribution-class
(SN <- SNorm(xi=2)) # SN is a skewed normal distribution with nu = 3.
set.seed(1)
r(SN)(1) # one random number generated from this distribution, e.g. -0.4037723
d(SN)(1) # Density of this distribution is 0.1914826 for x = 1.
p(SN)(1) # Probability that x < 1 is 0.8374454.
q(SN)(.1) # Probability that x < -1.137878 is 0.1.
## in RStudio or Jupyter IRKernel, use q.l(.)(.) instead of q(.)(.)
xi(SN) # shape of this distribution is 2.
xi(SN) <- 2.5 # shape of this distribution is now 2.5.
plot(SN)
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