gMCP: rqmvnorm – R documentation

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rqmvnorm

Random sample from the multivariate normal distribution

Description

Draw a quasi or pseudo random sample from the MVN distribution. For details on the implemented lattice rule for quasi-random numbers see Cools et al. (2006).

Usage

rqmvnorm(n, mean = rep(0, nrow(sigma)), sigma = diag(length(mean)),
  type = c("quasirandom", "pseudorandom"))

Arguments

`n`	Number of samples, when type = "quasirandom" is used this number is rounded up to the next power of 2 (e.g. 1000 to 1024=2^10) and at least 1024.
`mean`	Mean vector
`sigma`	Covariance matrix
`type`	What type of random numbers to use. `quasirandom` uses a randomized Lattice rule, and should be more efficient than `pseudorandom` that uses ordinary (pseudo) random numbers.

Value

Matrix of simulated values

Author(s)

We thank Dr. Frances Kuo for the permission to use the generating vectors (order 2 lattice rule) obtained from her website http://web.maths.unsw.edu.au/~fkuo/lattice/.

References

Cools, R., Kuo, F. Y., and Nuyens, D. (2006) Constructing embedded lattice rules for multivariate integration. SIAM Journal of Scientific Computing, 28, 2162-2188.

Examples

sims <- rqmvnorm(100, mean = 1:2, sigma = diag(2))
plot(sims)

gMCP

Graph Based Multiple Comparison Procedures

v0.8-15

GPL (>= 2)

Authors

Kornelius Rohmeyer [aut, cre], Florian Klinglmueller [aut]

Initial release