pcmvnorm {condMVNorm} | R Documentation |
Computes the distribution function of the conditional multivariate normal, [Y given X], where Z = (X,Y) is the fully-joint multivariate normal distribution with mean equal to mean
and covariance matrix sigma
.
pcmvnorm(lower=-Inf, upper=Inf, mean, sigma,
dependent.ind, given.ind, X.given,
check.sigma=TRUE, algorithm = GenzBretz(), ...)
lower |
the vector of lower limits of length n. |
upper |
the vector of upper limits of length n. |
mean |
the mean vector of length n. |
sigma |
a symmetric, positive-definte matrix, of dimension n x n, which must be specified. |
dependent.ind |
a vector of integers denoting the indices of the dependent variable Y. |
given.ind |
a vector of integers denoting the indices of the conditioning variable X. If specified as integer vector of length zero or left unspecified, the unconditional distribution is used. |
X.given |
a vector of reals denoting the conditioning value of X. This should be of the same length as |
check.sigma |
logical; if |
algorithm |
an object of class |
... |
additional parameters (currently given to |
This program involves the computation of multivariate normal probabilities with arbitrary correlation matrices.
The evaluated distribution function is returned with attributes
error |
estimated absolute error and |
msg |
status messages. |
n <- 10
A <- matrix(rnorm(n^2), n, n)
A <- A %*% t(A)
pcmvnorm(lower=-Inf, upper=1, mean=rep(1,n), sigma=A, dependent.ind=3,
given.ind=c(1,4,7,9,10), X.given=c(1,1,0,0,-1))
pcmvnorm(lower=-Inf, upper=c(1,2), mean=rep(1,n), sigma=A,
dep=c(2,5), given=c(1,4,7,9,10), X=c(1,1,0,0,-1))
pcmvnorm(lower=-Inf, upper=c(1,2), mean=rep(1,n), sigma=A,
dep=c(2,5))