| cor_covariance_meta | 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. \Sexpr[results=rd]{tools:::Rd_expr_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 Unpublished manuscript.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)
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