dmsn(x, xi=rep(0,length(alpha)), Omega, alpha, log=FALSE)
dmsn(x, dp=, log=FALSE)
pmsn(x, xi=rep(0,length(alpha)), Omega, alpha, ...)
pmsn(x, dp=)
rmsn(n=1, xi=rep(0,length(alpha)), Omega, alpha)
rmsn(n=1, dp=)
dmsn
, this is either a vector of length d
,
where d=length(alpha)
, or a matrix with d
columns,
giving the coordinates of the point(s) where the density must
be evaluated;
for pmsn
, onld
, or a matrix with d
columns,
representing the location parameter of the distribution.
If xi
is a matrix, its dimensions must agree with those of x
.(d,d)
.xi
, Omega
and alpha
containing quantities as described above. If dp
is specified, this
overrides the individual parameter specification.pmnorm
dmsn
), or a single probability
(pmsn
) or a matrix of random points (rmsn
).(Omega,alpha)
parametrization adopted here is the one of Azzalini and Capitanio (1999).Omega
is not tested for
efficiency reasons. Function pmsn
requires pmnorm
from package mnormt
;
the accuracy of its computation can be controlled via use of ...
Azzalini, A. and Capitanio, A. (1999). Statistical applications of the multivariate skew-normal distribution. J.Roy.Statist.Soc. B 61, 579--602.
dsn
, dmst
, dmnorm
x <- seq(-3,3,length=15)
xi <- c(0.5, -1)
Omega <- diag(2)
Omega[2,1] <- Omega[1,2] <- 0.5
alpha <- c(2,-6)
pdf <- dmsn(cbind(x,2*x-1), xi, Omega, alpha)
rnd <- rmsn(10, xi, Omega, alpha)
p1 <- pmsn(c(2,1), xi, Omega, alpha)
p2 <- pmsn(c(2,1), xi, Omega, alpha, abseps=1e-12, maxpts=10000)
Run the code above in your browser using DataLab