# asyCov: Compute Asymptotic Covariance Matrix of a... In metaSEM: Meta-Analysis using Structural Equation Modeling

## Description

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

## Usage

 ```1 2 3``` ```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 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. 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 <[email protected]>

## References

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

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18``` ```## 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 documentation built on May 9, 2018, 5:04 p.m.