pmnorm {mprobit}R Documentation

MVN Rectangle Probabilities

Description

Multivariate normal rectangle probabilities using Schervish's method

Usage

pmnorm(lb, ub, mu, sigma, eps = 1.e-05)

Arguments

lb vector of lower limits of integral/probability
ub vector of upper limits of integral/probability
mu mean vector of the multivariate normal density
sigma covariance matrix, it is assumed to be positive-definite
eps tolerance for integration

Value

pr probability of the multivariate normal rectangle region
perr estimated accuracy
ifault return codes from the referenced paper
= 0 if no problems
= 1 or 2 if eps too small
= 3 if dimension is not between 1 and 6 inclusive
= 4 if covariance matrix is not positive-definite

Author(s)

H. Joe, Statistics Department, UBC

References

Schervish, M.J. (1984). Multivariate normal probabilities with error bound. Appl. Statist., 33, 81-94.

See Also

mvnapp.

Examples


rh<-0.3
m<-2
a<-c(-1,-1)
b<-c(1,1)
mu<-rep(0,m)
s<-matrix(c(1,rh,rh,1),2,2)
print(pmnorm(a,b,mu,s))

m<-3
a<-c(-1,-1,-2)
b<-c(1,1,.5)
mu<-rep(0,m)
s<-matrix(c(1,rh,rh,rh,1,rh,rh,rh,1),3,3)
print(pmnorm(a,b,mu,s))

m<-4
a<-c(-1,-2.5,-2,-1.5)
b<-c(1.68,1.11,.5,.25)
mu<-rep(0,m)
s<-matrix(c(1,rh,rh,rh,rh,1,rh,rh,rh,rh,1,rh,rh,rh,rh,1),4,4)
print(pmnorm(a,b,mu,s))

[Package mprobit version 0.9-3 Index]