Description Usage Arguments Details Value Note Author(s) References See Also Examples

The probability density function, the distribution function and random number generation for the multivariate normal (Gaussian) distribution

1 2 3 4 |

`x` |
either a vector of length |

`mean` |
either a vector of length |

`varcov` |
a symmetric positive-definite matrix representing the
variance-covariance matrix of the distribution;
a vector of length 1 is also allowed (in this case, |

`sqrt` |
if not |

`log` |
a logical value (default value is |

`...` |
parameters passed to |

`n` |
the number of random vectors to be generated. |

`lower` |
a numeric vector of lower integration limits of
the density function; must be of maximal length |

`upper` |
a numeric vector of upper integration limits
of the density function; must be of maximal length |

`maxpts` |
the maximum number of function evaluations
(default value: |

`abseps` |
absolute error tolerance (default value: |

`releps` |
relative error tolerance (default value: |

The function `pmnorm`

works by making a suitable call to
`sadmvn`

if `d>2`

, or to `biv.nt.prob`

if `d=2`

,
or to `pnorm`

if `d=1`

.
Function `sadmvn`

is an interface to a Fortran-77 routine with
the same name written by Alan Genz, available from his web page,
which works using an adaptive integration method.
This Fortran-77 routine makes uses of some auxiliary functions whose authors
are documented in the code.

If `sqrt=NULL`

(default value), the working of `rmnorm`

involves
computation of a square root of `varcov`

via the Cholesky decomposition.
If a non-`NULL`

value of `sqrt`

is supplied, it is assumed
that it represents a matrix, *R* say, such that *R' R*
represents the required variance-covariance matrix of the distribution;
in this case, the argument `varcov`

is ignored.
This mechanism is intended primarily for use in a sequence of calls to
`rmnorm`

, all sampling from a distribution with fixed variance matrix;
a suitable matrix `sqrt`

can then be computed only once beforehand,
avoiding that the same operation is repeated multiple times along the
sequence of calls; see the examples below.
Another use of `sqrt`

is to supply a different form of square root
of the variance-covariance matrix, in place of the Cholesky factor.

For efficiency reasons, `rmnorm`

does not perform checks on the supplied
arguments.

If, after setting the same seed value to `set.seed`

,
two calls to `rmnorm`

are made with the same arguments except that one
generates `n1`

vectors and the other `n2`

vectors, with
`n1<n2`

, then the `n1`

vectors of the first call coincide with the
initial `n2`

vectors of the second call.

`dmnorm`

returns a vector of density values (possibly log-transformed);
`pmnorm`

returns a vector of probabilities, possibly with attributes
on the accuracy in case `x`

is a vector;
`sadmvn`

return a single probability with
attributes giving details on the achieved accuracy;
`rmnorm`

returns a matrix of `n`

rows of random vectors
or a vector in case `n=1`

.

The attributes `error`

and `status`

of the probability
returned by `pmnorm`

and `sadmvn`

indicate whether the function
had a normal termination, achieving the required accuracy.
If this is not the case, re-run the function with a higher value of
`maxpts`

Fortran code of `SADMVN`

and most auxiliary functions by Alan Genz,
some additional auxiliary functions by people referred to within his
program. Interface to **R** and additional **R** code by Adelchi Azzalini

Genz, A. (1992).
Numerical Computation of multivariate normal probabilities.
*J. Computational and Graphical Statist.*, **1**, 141-149.

Genz, A. (1993). Comparison of methods for the computation of
multivariate normal probabilities.
*Computing Science and Statistics*, **25**, 400-405.

Genz, A.: Fortran code available at http://www.math.wsu.edu/math/faculty/genz/software/fort77/mvn.f

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | ```
x <- seq(-2, 4, length=21)
y <- cos(2*x) + 10
z <- x + sin(3*y)
mu <- c(1,12,2)
Sigma <- matrix(c(1,2,0,2,5,0.5,0,0.5,3), 3, 3)
f <- dmnorm(cbind(x,y,z), mu, Sigma)
f0 <- dmnorm(mu, mu, Sigma)
p1 <- pmnorm(c(2,11,3), mu, Sigma)
p2 <- pmnorm(c(2,11,3), mu, Sigma, maxpts=10000, abseps=1e-10)
p <- pmnorm(cbind(x,y,z), mu, Sigma)
#
set.seed(123)
x1 <- rmnorm(5, mu, Sigma)
set.seed(123)
x2 <- rmnorm(5, mu, sqrt=chol(Sigma)) # x1=x2
eig <- eigen(Sigma, symmetric = TRUE)
R <- t(eig$vectors %*% diag(sqrt(eig$values)))
for(i in 1:50) x <- rmnorm(5, mu, sqrt=R)
#
p <- sadmvn(lower=c(2,11,3), upper=rep(Inf,3), mu, Sigma) # upper tail
#
p0 <- pmnorm(c(2,11), mu[1:2], Sigma[1:2,1:2])
p1 <- biv.nt.prob(0, lower=rep(-Inf,2), upper=c(2, 11), mu[1:2], Sigma[1:2,1:2])
p2 <- sadmvn(lower=rep(-Inf,2), upper=c(2, 11), mu[1:2], Sigma[1:2,1:2])
c(p0, p1, p2, p0-p1, p0-p2)
#
p1 <- pnorm(0, 1, 3)
p2 <- pmnorm(0, 1, 3^2)
``` |

```
[1] 3.273202e-01 3.273202e-01 3.273202e-01 0.000000e+00 1.444171e-09
```

mnormt documentation built on May 30, 2017, 8:26 a.m.

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