mvtData: Multivariate t-distribution data

In GLSE: Graphical Least Square Estimation

Description

The function provides multivariate t-distribution data for regression purposes. It can generate predictors and a response at the same time or only predictors.

Usage

mvtData(sigma = sigma, n, mean, df = 1, delta = rep(0, nrow(sigma)),
type = c("shifted", "Kshirsagar"), sd, Beta = NULL)

Arguments

sigma	A scale matrix of v x v dimension.
n	The number of observations required to be generated.
mean	A vector of length v.
df	Degrees of freedom, where the default is 1.
delta	A vector of noncentral parameters of length n.
type	Possible types are "Kshirsagar" and "shifted". More information is provided in the R package 'mvtnorm' version 1.0-7 under the rmvt function.
sd	A vector of standard deviations, which is only needed when the response is generated.
Beta	A vector of length v, which is only needed when the response is generated.

Value

A data frame consisting of

X	The generated predictors.
Y	The generated response if required.

References

Alan, G. Frank, B. (2009), Computation of Multivariate Normal and t Probabilities. Lecture Notes in Statistics, Vol. 195., Springer-Verlag, Heidelberg. ISBN 978-3-642-01688-2.

Alan, G. Frank, B. Tetsuhisa, M. Xuefe, M. Friedrich, L. Fabian, S. and Torsten, H. (2019). mvtnorm: Multivariate Normal and t Distributions. R package version 1.0-7. URL http://CRAN.R-project.org/package=mvtnorm.

Aldahmani, S. and Dai, H. (2015). Unbiased Estimation for Linear Regression When n< v. International Journal of Statistics and Probability, 4(3), p61.

Csardi, G., and Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5), 1-9.

Dethlefsen, C., and H?jsgaard, S. (2005). A common platform for graphical models in R: The gRbase package. Journal of Statistical Software, 14(17), 1-12.

See Also

mvnData

Examples

library(mvtnorm)

  # Generate sigma

    sigma <- matrix(0, nrow =5,ncol=5)
    sigma[1:5,1:5]<-0
    diag(sigma)<-1


  # Generate  vectors of Beta, mean and standard deviation

    Beta1<-  round(runif(5,1.5,3.5),1)
    mu <-  runif(5, 0, 0)
    sd <-  runif(5, 1, 1)

  # Get the multivariate normal distribution data

    set.seed(123)
    result <-mvtData(sigma=sigma, n=10,mean=mu,df=1,sd=1,Beta=Beta1)

    result

GLSE documentation built on May 2, 2019, 6:34 a.m.