| qmvnorm {mvtnorm} | R Documentation |
Computes the equicoordinate quantile function of the multivariate normal distribution for arbitrary correlation matrices based on an inversion of the algorithms by Genz and Bretz.
qmvnorm(p, interval = c(-10, 10), tail = c("lower.tail", "upper.tail", "both.tails"),
mean = 0, corr = NULL, sigma = NULL, maxpts = 25000, abseps = 0.001,
releps = 0, ...)
p |
probability. |
interval |
a vector containing the end-points of the interval to be
searched by uniroot. |
tail |
specifies which quantiles should be computed.
lower.tail gives the quantile x for which
P[X <= x] = p, upper.tail gives x with
P[X > x] = p and
both.tails leads to x
with P[-x <= X <= x] = p. |
mean |
the mean vector of length n. |
corr |
the correlation matrix of dimension n. |
sigma |
the covariance matrix of dimension n. Either corr or
sigma can be specified. If sigma is given, the
problem is standardized. If neither corr nor
sigma is given, the identity matrix is used
for sigma. |
maxpts |
maximum number of function values as integer. |
abseps |
absolute integration error tolerance as double. |
releps |
relative integration error tolerance as double. |
... |
additional paramters to be passed to
uniroot. |
Only equicoordinate quantiles are computed, i.e., the quantiles in each
dimension coincide. Currently, the distribution function is inverted by
using the
uniroot function which may result in limited accuracy of the
quantiles.
A list with four components: quantile and f.quantile
give the location of the quantile and the value of the function
evaluated at that point. iter and estim.prec give the number
of iterations used and an approximate estimated precision from
uniroot.
qmvnorm(0.95, sigma = diag(2), tail = "both")