mvnData: Multivariate normal distribution data

Description

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

Usage

mvnData(sigma, n, mean, method = c("eigen", "svd", "chol"), sd = 1, Beta = NULL)

Arguments

sigma

A variance covariance matrix of dimension v x v.

The number of observations required to be generated.

mean

A vector of length v.

method

Possible methods are eigenvalue decomposition "eigen", which is the default, singular value decomposition "svd", and Cholesky decomposition "chol". The method determines the matrix root of sigma.

A vector of standard deviations of length v, 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

The generated predictors.

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.

Examples

Run this code

# NOT RUN {
library(mvtnorm)
# Generate sigma

set.seed(1234)
sigma <- matrix(0, nrow =5,ncol=5)
sigma[1:5,1:5]<-.5
diag(sigma)<-3


# 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 <-mvnData(sigma=sigma, n=10,mean=mu,sd=1,Beta=Beta1)

result

# }