ptmvnorm {tmvtnorm}

Truncated Multivariate Normal Distribution

Description

Computes the distribution function of the truncated multivariate normal distribution for arbitrary limits and correlation matrices based on the pmvnorm() implementation of the algorithms by Genz and Bretz.

Usage

ptmvnorm(lowerx, upperx, mean=rep(0, length(lowerx)), sigma, 
  lower = rep(-Inf, length = length(mean)), 
  upper = rep( Inf, length = length(mean)), 
  maxpts = 25000, abseps = 0.001, releps = 0)

Arguments

lowerx

the vector of lower limits of length n.

upperx

the vector of upper limits of length n.

mean

the mean vector of length n.

sigma

the covariance matrix of dimension n. Either corr or sigma can be specified. If sigma is given, the problem is standardized. If neither corr nor sigma is given, the identity matrix is used for sigma.

lower

Vector of lower truncation points,\ default is rep(-Inf, length = length(mean)).

upper

Vector of upper truncation points,\ default is rep( Inf, length = length(mean)).

maxpts

maximum number of function values as integer.

abseps

absolute error tolerance as double.

releps

relative error tolerance as double.

Details

The computation of truncated multivariate normal probabilities and densities is done using conditional probabilities from the standard/untruncated multivariate normal distribution. So we refer to the documentation of the mvtnorm package and the methodology is described in Genz (1992, 1993) and Genz/Bretz (2009).

For properties of the truncated multivariate normal distribution see for example Johnson/Kotz (1970) and Horrace (2005).

Value

The evaluated distribution function is returned with attributes

error

estimated absolute error and

msg

status messages.

References

Genz, A. (1992). Numerical computation of multivariate normal probabilities. Journal of Computational and Graphical Statistics, 1, 141–150

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

Genz, A. and Bretz, F. (2009). Computation of Multivariate Normal and t Probabilities. Lecture Notes in Statistics, Vol. 195, Springer-Verlag, Heidelberg.

Johnson, N./Kotz, S. (1970). Distributions in Statistics: Continuous Multivariate Distributions Wiley & Sons, pp. 70–73

Horrace, W. (2005). Some Results on the Multivariate Truncated Normal Distribution. Journal of Multivariate Analysis, 94, 209–221

Examples

 sigma <- matrix(c(5, 0.8, 0.8, 1), 2, 2)
 Fx <- ptmvnorm(lowerx=c(-1,-1), upperx=c(0.5,0), mean=c(0,0), 
   sigma=sigma, lower=c(-1,-1), upper=c(1,1))