metaSEM: asyCov – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

asyCov

Compute Asymptotic Covariance Matrix of a Correlation/Covariance Matrix

Description

It computes the asymptotic sampling covariance matrix of a correlation/covariance matrix under the assumption of multivariate normality.

Usage

asyCov(x, n, cor.analysis = TRUE, dropNA = FALSE, as.matrix = TRUE,
       acov=c("individual", "unweighted", "weighted"),
       suppressWarnings = TRUE,  silent = TRUE, run = TRUE, ...)

Arguments

`x`	A correlation/covariance matrix or a list of correlation/covariance matrices. `NA` on the variables or other values defined in `na.strings` will be removed before the analysis. Note that it only checks the diagonal elements of the matrices. If there are missing values, make sure that the diagonals are coded with `NA` or values defined in `na.strings`.
`n`	Sample size or a vector of sample sizes
`cor.analysis`	Logical. The output is either a correlation or covariance matrix.
`dropNA`	Logical. If it is `TRUE`, the resultant dimensions will be reduced by dropping the missing variables. If it is `FALSE`, the resultant dimensions are the same as the input by keeping the missing variables.
`as.matrix`	Logical. If it is `TRUE` and `x` is a list of correlation/covariance matrices with the same dimensions, the asymptotic covariance matrices will be column vectorized and stacked together. If it is `FALSE`, the output will be a list of asymptotic covariance matrices. Note that if it is `TRUE`, `dropNA` will be `FALSE` automatically. This option is useful when passing the asymptotic covariance matrices to `meta`
`acov`	If it is `individual` (the default), the sampling variance-covariance matrices are calculated based on the individual correlation/covariance matrix. If it is either `unweighted` or `weighted`, the average correlation/covariance matrix is calculated based on the unweighted or weighted mean with the sample sizes. The average correlation/covariance matrix is used to calculate the sampling variance-covariance matrices.
`suppressWarnings`	Logical. If `TRUE`, warnings are suppressed. It is passed to `mxRun`.
`silent`	Logical. An argument to be passed to `mxRun`
`run`	Logical. If `FALSE`, only return the mx model without running the analysis.
`...`	Further arguments to be passed to `mxRun`

Value

An asymptotic covariance matrix of the vectorized correlation/covariance matrix or a list of these matrices. If as.matrix=TRUE and x is a list of matrices, the output is a stacked matrix.

Author(s)

Mike W.-L. Cheung <mikewlcheung@nus.edu.sg>

References

Cheung, M. W.-L., & Chan, W. (2004). Testing dependent correlation coefficients via structural equation modeling. Organizational Research Methods, 7, 206-223.

Examples

## Not run: 
C1 <- matrix(c(1,0.5,0.4,0.5,1,0.2,0.4,0.2,1), ncol=3)  
asyCov(C1, n=100)

## Data with missing values
C2 <- matrix(c(1,0.4,NA,0.4,1,NA,NA,NA,NA), ncol=3)  
C3 <- matrix(c(1,0.2,0.2,1), ncol=2)

## Output is a list of asymptotic covariance matrices
asyCov(list(C1,C2,C3), n=c(100,50,50), dropNA=TRUE, as.matrix=FALSE)

## Output is a stacked matrix of asymptotic covariance matrices
asyCov(list(C1,C2), n=c(100,50), as.matrix=TRUE)

## Output is a stacked matrix of asymptotic covariance matrices
asyCov(list(C3,C3), n=c(100,50), as.matrix=TRUE)

## End(Not run)

metaSEM

Meta-Analysis using Structural Equation Modeling

v1.2.5

GPL (>= 2)

Authors

Mike Cheung [aut, cre] (<https://orcid.org/0000-0003-0113-0758>)

Initial release

2020-11-29