Bayesian Multivariate and Multilevel PEESE Model

Fits Bayesian precision-effect estimate with standard errors (PEESE) models with known sampling covariance and BayesTools random-effect formulas.

Usage

bPEESE.mv(
  yi,
  V,
  ni,
  mods,
  scale,
  random,
  R = NULL,
  Rscale = "cor",
  data,
  slab,
  subset,
  measure,
  prior_effect,
  prior_heterogeneity,
  prior_mods,
  prior_scale,
  prior_bias,
  standardize_continuous_predictors = TRUE,
  set_contrast_factor_predictors = "treatment",
  prior_unit_information_sd,
  rescale_priors = 1,
  prior_informed_field,
  prior_informed_subfield,
  effect_direction = "detect",
  known_v_parameterization = "auto",
  sample = 5000,
  burnin = 2000,
  adapt = 500,
  chains = 3,
  thin = 1,
  parallel = FALSE,
  autofit = FALSE,
  autofit_control = set_autofit_control(),
  convergence_checks = set_convergence_checks(),
  seed = NULL,
  silent,
  ...,
  vi = NULL,
  sei = NULL
)

Arguments

yi: a vector of effect sizes, or a formula with the effect size on the left-hand side and location moderators on the right-hand side (for example yi ~ x1 + x2). If a formula is supplied, mods must not be specified.
V: a known working variance-covariance matrix, a list of block variance-covariance matrices, or an exact diagonal-plus-factor declaration created by known_v_factor(), used by brma.mv(). Ordinary covariance matrices from metafor::vcalc() can be supplied directly. Product selection requires no publication groups. For best-p-value selection, declare groups separately with selection_model(weight_rule = "best", group = ...). Matrix symmetry uses base R's numerical tolerance. Accepted matrices are stored symmetrically by averaging off-diagonal pairs; the supplied object and its diagonal are unchanged. Dependency blocks that are block constant on nested partitions are represented exactly as a diagonal-plus-factor structure without a known_v_factor() declaration. Dimensions and row alignment are checked on the complete input; when rows are dropped (subset or missing values), the numerical covariance checks apply to the retained rows only, so acceptance says nothing about the entries of the dropped rows.
ni: an optional vector of sample sizes. Used for measure = "GEN" or when estimating the unit information standard deviation.
mods: an optional matrix, data.frame, or formula specifying location moderators (meta-regressors). Formula input is evaluated in data and supports explicit columns and standard formula operators, including ^ as R's crossing operator with a literal whole exponent of at least 2: (x + z)^2 expands to x + z + x:z, and x^2 is not a quadratic term (x + x^2 is x). Precompute transformed predictors, such as a squared predictor, as columns in data; inline calls such as I(), poly(), spline constructors, and user functions are not supported.
scale: an optional matrix, data.frame, or formula specifying scale predictors for location-scale models. Formula input is evaluated in data and follows the same explicit-column formula grammar as mods. Random-formula models also accept a named list targeting top-level random components or concrete random-effect blocks; see brma.mv().
random: Optional BayesTools random-effect formula or list of formulas. Omission or NULL means a fixed-effect model with no implicit heterogeneity term. Known sampling covariance V remains unchanged. Declare random before supplying heterogeneity priors or scale formulas.
R: optional known random-effect group covariance/correlation matrix, or a named list of matrices. Matrix row and column names identify grouping levels. This is distinct from the known sampling covariance V.
Rscale: scaling applied to each supplied R matrix before fitting. Supported values are "cor", "none", "cor0", "cov0", with metafor-compatible aliases TRUE, FALSE, 0, 1, 2, and 3. An unnamed scalar is recycled across all supplied matrices. A vector or list can specify one scaling value per named R entry, with exactly matching names. An unnamed one-element list is supported only for a single R matrix.
data: an optional data frame containing the variables.
slab: an optional vector of study labels.
subset: an optional logical or numeric vector specifying a subset of data to be used. Subsetting precedes missing-row removal; retained-row outcome and predictor checks follow these steps. Type, shape, and existing extraction checks can still reject malformed inputs before subsetting. Row selection (by subset or missing-row removal) drops factor levels left without rows from mods, scale, and random-effect grouping factors. The index variable of a structured random-effect term (cs(), hcs(), ar1(), har() with one index variable) keeps its declared levels, which define the covariance structure; the variables of a multi-variable cs(a + b | group) index follow the ordinary rule, because that index is built from their observed combinations. If an index variable that keeps its levels is also used in mods or scale and loses one of its levels to the selection, the fit stops with an error of class RoBMA_structured_index_level_removed (also RoBMA_data_error); remove the level from the data with droplevels() or use a separate column for the moderator.
measure: a character string specifying the effect size measure. Normal/effect-size constructors require an explicit value and support "SMD", "ZCOR", "RR", "OR", "HR", "RD", "IRR", and "GEN". Use "GEN" only for general effect sizes without a known unit information standard deviation. GLMM raw-count constructors support only "OR" and "IRR" and default to "OR".
prior_effect: prior distribution for the effect size (\(\mu\)) parameter (the intercept). If omitted and consumed, a default prior is constructed. In single-model functions, explicit NULL or FALSE sets a spike at zero.
prior_heterogeneity: prior distribution for the heterogeneity (\(\tau\)) parameter. If omitted and consumed, a default prior is constructed. In single-model functions, explicit NULL or FALSE sets a spike at zero. For brma.mv random-effect formulas, this can also be a partial or complete prior_random object; see random_effect_prior_specification.
prior_mods: prior distribution for the moderators (\(\beta\)) parameters. A single prior applies to all terms; a named list can specify term-specific priors. If omitted or NULL, default priors are used. This argument can be specified only when mods is specified. A formula without an intercept (~ 0 + x) fixes the location intercept at zero, so an intercept entry of the list is accepted only as a point prior at zero; any other intercept prior stops with an error of class RoBMA_prior_intercept_conflict (also of class RoBMA_prior_error). A named intercept entry supplies the complete intercept prior. Explicit competing global effect declarations are refused when this would discard them. Named NULL or FALSE intercept entries are invalid; specify the complete prior in one place.
prior_scale: prior distribution for the scale (\(\delta\)) parameters. A single prior applies to all terms; a named list can specify term-specific priors. If omitted or NULL, default priors are used. This argument can be specified only when scale is specified. A named intercept entry supplies the complete positive SD prior used by log(intercept). Global heterogeneity declarations remain meaningful when they supply another random root or its model-inclusion weights; declarations fully replaced without a consumer raise RoBMA_prior_intercept_conflict.
prior_bias: PEESE regression-coefficient prior created by prior_PEESE(). If omitted or NULL, the default is a Cauchy prior centered at 0, truncated to positive values, with scale from RoBMA.get_option("default_bias_PEESE.scale") after UISD adjustment.
standardize_continuous_predictors: logical. Whether to standardize continuous predictors. Defaults to TRUE.
set_contrast_factor_predictors: character. How to set contrast for factor predictors. Defaults are constructor-specific and shown in each function usage. Single-model constructors use "treatment"; model-averaging constructors use "meandif". Accepted values are "treatment", "meandif", "orthonormal", and "independent". Independent coding creates one coefficient per factor level. Random-coefficient blocks reuse the resolved fixed-factor contrast unless random_block(contrasts = ...) overrides it. A warning is issued when default or inherited independent contrasts make a fixed or random design overspecified. With "meandif" or "orthonormal" contrasts, a formula term that interacts a factor with a predictor whose own term is missing, such as g:x in ~ g + g:x or ~ g / x, stops the fit: the term codes g by level indicators, with one coefficient per level, on which mean-difference and orthonormal priors are not defined. Specify an independent prior for that term, e.g., prior_mods = list("g:x" = prior_factor("normal", list(0, 1), contrast = "independent")) (or prior_scale for a scale formula), include the missing term (~ g * x), or use set_contrast_factor_predictors = "treatment".
prior_unit_information_sd: numeric. The unit information standard deviation (\(\sigma_{unit}\)). Cannot be used together with prior_informed_field.
rescale_priors: numeric. A scaling factor for supported prior distributions. Point and none priors are unchanged; supplied prior_random() scale architectures and publication-bias priors are not rescaled. The default PEESE prior separately uses its documented UISD adjustment, independently of rescale_priors. Defaults to 1.
prior_informed_field: character. The field of the informed prior distributions. Omit to use the standard default prior specification; explicit NULL is invalid.
prior_informed_subfield: character. The subfield of the informed prior distributions. Can only be specified together with prior_informed_field. Omit to use the field-specific default, such as "Cochrane" for prior_informed_field = "medicine"; explicit NULL is invalid.
effect_direction: direction used by publication-bias adjustments. "positive" assumes statistically significant positive estimates are more likely to be selected; "negative" mirrors the selection direction; "detect" infers the direction from the fitted data.
known_v_parameterization: known-V backend used by brma.mv(). "auto" chooses an exact backend when feasible; "latent" uses a latent D + BB' decomposition; "whitened" uses an eigen-rotated normal likelihood; "block_mvn" uses an exact native block multivariate-normal likelihood.
sample: numeric. Number of MCMC samples to save. Defaults to 5000.
burnin: numeric. Number of burn-in iterations. Defaults to 2000.
adapt: numeric. Number of adaptation iterations. Defaults to 500.
chains: numeric. Number of MCMC chains. Defaults to 3.
thin: numeric. Thinning interval. Defaults to 1.
parallel: logical. Whether to run MCMC chains in parallel. Defaults to FALSE.
autofit: logical. Whether to automatically extend the MCMC chains if convergence is not met. Defaults to FALSE.
autofit_control: list of autofit control settings. See set_autofit_control() for details.
convergence_checks: list of convergence check settings. See set_convergence_checks() for details.
seed: numeric. Random seed for reproducibility. Defaults to NULL.
silent: logical. Whether to suppress output. Constructors with no explicit default use RoBMA.get_option("silent") when silent is omitted. Model-averaging wrappers default to TRUE unless explicitly changed.
...: additional advanced arguments. Fitting functions reject unused arguments; currently recognized internal arguments include only_data, only_priors, is_JASP, and is_JASP_prefix.
vi: a vector of sampling variances. Either vi or sei must be supplied for normal models.
sei: a vector of standard errors. Either vi or sei must be supplied for normal models.

Value

A fitted object of class c("bPEESE.mv", "bPEESE", "brma.mv", "brma.norm", "brma").

Details

Omitted or NULL random specifies a fixed-effect model with no implicit heterogeneity. Heterogeneity priors and scale formulas require an explicit random structure; the known sampling covariance remains unchanged.

bPEESE.mv() combines the PEESE publication-bias regression of bPEESE() with the known-covariance and random-formula model of brma.mv(). The PEESE predictor is the marginal sampling variance diag(V); known off-diagonal sampling covariance remains part of the joint likelihood.

The supported known-V backends, random-effect marginalization rules, predictive targets, and diagnostic semantics are the same as for brma.mv(). In particular, random replaces the specialized cluster argument used by bPEESE().

Examples

if (FALSE) { # \dontrun{
V <- matrix(c(0.04, 0.01, 0.01, 0.09), 2, 2)
fit <- bPEESE.mv(
  yi      = c(0.10, 0.20),
  V       = V,
  measure = "GEN",
  seed    = 1,
  silent  = TRUE
)
summary(fit)
} # }