cor_covariance_meta {configural} | R Documentation |

## Estimate the asymptotic sampling covariance matrix for the unique elements of a meta-analytic correlation matrix

### Description

Estimate the asymptotic sampling covariance matrix for the unique elements of a meta-analytic correlation matrix

### Usage

```
cor_covariance_meta(
r,
n,
sevar,
source = NULL,
rho = NULL,
sevar_rho = NULL,
n_overlap = NULL
)
```

### Arguments

`r` |
A meta-analytic matrix of observed correlations (can be full or lower-triangular). |

`n` |
A matrix of total sample sizes for the meta-analytic correlations in |

`sevar` |
A matrix of estimated sampling error variances for the meta-analytic correlations in |

`source` |
A matrix indicating the sources of the meta-analytic correlations in |

`rho` |
A meta-analytic matrix of corrected correlations (can be full or lower-triangular). |

`sevar_rho` |
A matrix of estimated sampling error variances for the meta-analytic corrected correlations in |

`n_overlap` |
A matrix indicating the overlapping sample size for the unique (lower triangular) values in |

### Details

If both `source`

and `n_overlap`

are `NULL`

, it is assumed that all meta-analytic correlations come from the the same source.

### Value

The estimated asymptotic sampling covariance matrix

### References

Nel, D. G. (1985).
A matrix derivation of the asymptotic covariance matrix of sample correlation coefficients.
*Linear Algebra and Its Applications, 67*, 137–145. doi:10.1016/0024-3795(85)90191-0

Wiernik, B. M. (2018).
*Accounting for dependency in meta-analytic structural equations modeling: A flexible alternative to generalized least squares and two-stage structural equations modeling.*
Unpublished manuscript.

### Examples

```
cor_covariance_meta(r = mindfulness$r, n = mindfulness$n,
sevar = mindfulness$sevar_r, source = mindfulness$source)
```

*configural*version 0.1.5 Index]