cor_covariance_meta {configural} | R Documentation |
Estimate the asymptotic sampling covariance matrix for the unique elements of a meta-analytic correlation matrix
cor_covariance_meta(
r,
n,
sevar,
source = NULL,
rho = NULL,
sevar_rho = NULL,
n_overlap = NULL
)
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 |
If both source
and n_overlap
are NULL
, it is assumed that all meta-analytic correlations come from the the same source.
The estimated asymptotic sampling covariance matrix
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 Unpublished manuscript.
cor_covariance_meta(r = mindfulness$r, n = mindfulness$n,
sevar = mindfulness$sevar_r, source = mindfulness$source)