cor_covariance_meta {configural}

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 r (can be full or lower-triangular).

sevar

A matrix of estimated sampling error variances for the meta-analytic correlations in r (can be full or lower-triangular).

source

A matrix indicating the sources of the meta-analytic correlations in r (can be full or lower-triangular). Used to estimate overlapping sample size for correlations when n_overlap == NULL.

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 rho (can be full or lower-triangular).

n_overlap

A matrix indicating the overlapping sample size for the unique (lower triangular) values in r (can be full or lower-triangular). Values must be arranged in the order returned by cor_labels(colnames(R)).

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 Unpublished manuscript.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)