metaSEM (version 1.5.0)

rCor: Generate (Nested) Sample/Population Correlation/Covariance Matrices

Description

It generates (nested) random sample or population correlation or covariance matrices. rCor() is a wrapper to call rCorPop() and then rCorSam().

Usage

rCor(Sigma, V, n, corr=TRUE, raw.data=FALSE,
     nonPD.pop=c("replace", "nearPD", "accept"),
     nonPD.sam=c("stop", "nearPD"))
rCorPop(Sigma, V, k, corr=TRUE, 
        nonPD.pop=c("replace", "nearPD", "accept"))
rCorSam(Sigma, n, corr=TRUE, raw.data=FALSE, 
        nonPD.sam=c("stop", "nearPD"))
rCor3L(Sigma, V.B, V.W, n, cluster, corr=TRUE, raw.data=FALSE,
       nonPD.pop=c("replace", "nearPD", "accept"),
       nonPD.sam=c("stop", "nearPD"))

Value

An object of the generated population/sample correlation/covariance matrices.

Arguments

Sigma

A list of population correlation/covariance matrices or a single matrix

V

A variance-covariance matrix of Sigma.

V.B

A variance-covariance matrix of between-study Sigma.

V.W

A variance-covariance matrix of within-study Sigma

n

A vector or a single sample sizes.

cluster

A vector of number of studies in clusters.

corr

Logical. Whether to generate correlation or covariance matrices.

raw.data

Logical. Whether correlation/covariance matrices are generated via raw.data or directly from a Wishart distribution.

nonPD.pop

If it is replace, generated non-positive definite matrices are replaced by generated new ones which are positive definite. If it is nearPD, they are replaced by nearly positive definite matrices by calling Matrix::nearPD(). If it is accept, they are accepted.

nonPD.sam

If it is stop, the program stops when the inputs in the rCorSam are non-positive definite. If it is nearPD, they are replaced by nearly positive definite matrices by calling Matrix::nearPD().

k

A vector or a single number of studies.

Author

Mike W.-L. Cheung <[e-mail]>

Examples

Sigma <- matrix(c(1, .2, .3,
                  .2, 1, .4,
                  .3, .4, 1), ncol=3, nrow=3)
V <- diag(c(.1, .1, .1))

## Generate two population correlation matrices
Pop.corr <- rCorPop(Sigma, V, k=2)
Pop.corr
                  
summary(Pop.corr)
                  
## Generate two sample correlation matrices
rCorSam(Sigma=Pop.corr, n=c(10, 10))
                 
## The above code is the same as the following one
rCor(Sigma, V, n=c(10, 10))