ptmvt: Truncated Multivariate Student t Distribution

In tmvtnorm: Truncated Multivariate Normal and Student t Distribution

Truncated Multivariate Student t Distribution

Description

Computes the distribution function of the truncated multivariate t distribution

Usage

ptmvt(lowerx, upperx, mean = rep(0, length(lowerx)), sigma, df = 1, 
  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.
df	Degrees of freedom parameter
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.

Value

The evaluated distribution function is returned with attributes

error	estimated absolute error and
msg	status messages.

Author(s)

Stefan Wilhelm <[e-mail]>

References

Geweke, J. F. (1991) Efficient simulation from the multivariate normal and Student-t distributions subject to linear constraints and the evaluation of constraint probabilities. https://www.researchgate.net/publication/2335219_Efficient_Simulation_from_the_Multivariate_Normal_and_Student-t_Distributions_Subject_to_Linear_Constraints_and_the_Evaluation_of_Constraint_Probabilities

Samuel Kotz, Saralees Nadarajah (2004). Multivariate t Distributions and Their Applications. Cambridge University Press

Examples

sigma <- matrix(c(5, 0.8, 0.8, 1), 2, 2)
Fx <- ptmvt(lowerx=c(-1,-1), upperx=c(0.5,0), mean=c(0,0), sigma=sigma, df=3, 
  lower=c(-1,-1), upper=c(1,1))

tmvtnorm documentation built on Sept. 2, 2025, 1:09 a.m.