phacking_meta | R Documentation |
Fits right-truncated meta-analysis (RTMA), a bias correction for the joint effects of p-hacking (i.e., manipulation of results within studies to obtain significant, positive estimates) and traditional publication bias (i.e., the selective publication of studies with significant, positive results) in meta-analyses. This method analyzes only nonaffirmative studies (i.e., those with significant, positive estimates). You can pass all studies in the meta-analysis or only the nonaffirmative ones; if the former, the function will still analyze only the nonaffirmative ones.
phacking_meta(
yi,
vi,
sei,
favor_positive = TRUE,
alpha_select = 0.05,
ci_level = 0.95,
stan_control = list(adapt_delta = 0.98, max_treedepth = 20),
parallelize = TRUE
)
yi |
A vector of point estimates to be meta-analyzed. |
vi |
A vector of estimated variances (i.e., squared standard errors) for the point estimates. |
sei |
A vector of estimated standard errors for the point estimates.
(Only one of |
favor_positive |
|
alpha_select |
Alpha level at which an estimate's probability of being favored by publication bias is assumed to change (i.e., the threshold at which study investigators, journal editors, etc., consider an estimate to be significant). |
ci_level |
Confidence interval level (as proportion) for the corrected
point estimate. (The alpha level for inference on the corrected point
estimate will be calculated from |
stan_control |
List passed to |
parallelize |
Logical indicating whether to parallelize sampling. |
An object of class metabias::metabias()
, a list containing:
A tibble with one row per study and the columns
yi
, vi
, sei
, affirm
.
A list with the elements favor_positive
, alpha_select
, ci_level
, tcrit
, k
, k_affirmative
, k_nonaffirmative
, optim_converged
.
optim_converged
indicates whether the optimization to find
the posterior mode converged.
A tibble with two rows and the columns
param
, mode
, median
, mean
, se
, ci_lower
, ci_upper
, n_eff
, r_hat
. We recommend reporting the mode
for the point estimate; median
and mean
represent
posterior medians and means respectively.
A stanfit
object (the result of fitting the RTMA model).
mathur2022phackingmetabias
# passing all studies, though only nonaffirmative ones will be analyzed
money_priming_rtma <- phacking_meta(money_priming_meta$yi, money_priming_meta$vi,
parallelize = FALSE)
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