| mvstud {rpact} | R Documentation |
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.
mvstud(..., NDF, A, B, BPD, D, EPS = 1e-06, INF, IERC = 1, HINC = 0)
... |
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 |
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.
## Not run:
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
## End(Not run)