A formula keyword for the two-stage meta-regression workflow's
stage-2 entry, where the responses are stage-1 estimates (or
pre-computed effect sizes such as Hedges' g) and their marginal
sampling variances are known. This keyword desugars to
equalto(0 + obs | grp_V, V) inside gllvmTMB().
Arguments
- value
The response column name (typically the column called
valuein a stage-1 summary, or an effect-size column in a meta-analytic dataset).- sampling_var
Either an unquoted column name holding the per-row sampling variance, or a length-1 numeric used for every row. Deprecated in favour of
meta_known_V(value, V = V).
Details
Deprecated alias. Use meta_known_V() in new code:
meta_known_V(value, V = V).
Diagonal vs block-diagonal V
meta(value, sampling_var = vi) describes the diagonal case –
rows independent. For multivariate meta-analyses with multiple
effect sizes per study (multiple outcomes, multiple traits per
individual, etc.), within-study sampling errors are correlated and
V is block-diagonal. The engine supports any positive-definite
\(n \times n\) V via solve(V) internally. Build a block-diagonal
V with block_V() and pass it to gllvmTMB() as known_V = V_block.
See also
meta_known_V() (canonical replacement); block_V() for
constructing a block-diagonal sampling-V from a study factor;
gllvmTMB() for the stage-2 fit.
Examples
if (FALSE) { # \dontrun{
# Preferred (canonical) form -- use meta_known_V():
V <- diag(stage1_summary$sampling_var)
fit2 <- gllvmTMB(value ~ 0 + trait +
latent(0 + trait | site, d = 2) +
meta_known_V(value, V = V),
data = stage1_summary, known_V = V)
# Deprecated form (still works, emits a warning):
fit2 <- gllvmTMB(value ~ 0 + trait +
latent(0 + trait | site, d = 2) +
meta(value, sampling_var = sampling_var),
data = stage1_summary, known_V = V)
} # }