Computes the distribution function of the truncated multivariate t distribution
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)
The evaluated distribution function is returned with attributes
estimated absolute error and
status messages.
the vector of lower limits of length n.
the vector of upper limits of length n.
the mean vector of length n.
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
.
Degrees of freedom parameter
Vector of lower truncation points,
default is rep(-Inf, length = length(mean))
.
Vector of upper truncation points,
default is rep( Inf, length = length(mean))
.
maximum number of function values as integer.
absolute error tolerance as double.
relative error tolerance as double.
Stefan Wilhelm <Stefan.Wilhelm@financial.com>
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
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))
Run the code above in your browser using DataLab