Original Algorithm AS 251: Student T Distribution

Calculates the Multivariate Normal Distribution with Product Correlation Structure published by Charles Dunnett, Algorithm AS 251.1 Appl.Statist. (1989), Vol.38, No.3, doi:10.2307/2347754 .

Usage

mvstud(..., NDF, A, B, BPD, D, EPS = 1e-06, INF, IERC = 1, HINC = 0)

Arguments

...

Ensures that all arguments (starting from the "...") are to be named and that a warning will be displayed if unknown arguments are passed.

NDF

Degrees of Freedom. Use 0 for infinite D.F.

A

Upper limits of integration. Array of N dimensions

B

Lower limits of integration. Array of N dimensions

BPD

Values defining correlation structure. Array of N dimensions

D

Non-Centrality Vector

EPS

desired accuracy. Defaults to 1e-06

INF

Determines where integration is done to infinity. Array of N dimensions. Valid values for INF(I): 0 = c(B(I), Inf), 1 = c(-Inf, A(I)), 2 = c(B(I), A(I))

IERC

error control. If set to 1, strict error control based on fourth derivative is used. If set to zero, error control based on halving intervals is used

HINC

Interval width for Simpson's rule. Value of zero caused a default .24 to be used

Details

This is a wrapper function for the original Fortran 77 code. For a multivariate normal vector with correlation structure defined by RHO(I,J) = BPD(I) * BPD(J), computes the probability that the vector falls in a rectangle in n-space with error less than eps.

Examples

if (FALSE) { # \dontrun{
N <- 3
RHO <- 0.5
B <- rep(-5.0, length = N)
A <- rep(5.0, length = N)
INF <- rep(2, length = N)
BPD <- rep(sqrt(RHO), length = N)
D <- rep(0.0, length = N)
result <- mvstud(NDF = 0, A = A, B = B, BPD = BPD, INF = INF, D = D)
result
} # }