data_input: Input Data Specification

In RoBMA: Robust Bayesian Meta-Analyses

Input Data Specification

Description

Shared data-input arguments used by the RoBMA fitting functions.

Normal models use approximate effect-size estimates supplied through yi with either vi or sei. GLMM models use the raw two-arm count arguments for binomial (measure = "OR") or Poisson (measure = "IRR") outcomes.

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.
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.
weights	an optional vector of positive likelihood weights. For normal/effect-size models, each weight powers the estimate likelihood. For constructors with GLMM raw-count input, each weight powers the paired two-arm likelihood for one study.
ni	an optional vector of sample sizes. Used for measure = "GEN" or when estimating "UISD").
mods	an optional matrix, data.frame, or formula specifying location moderators (meta-regressors). Formula input is evaluated in data.
scale	an optional matrix, data.frame, or formula specifying scale predictors for location-scale models. Formula input is evaluated in data.
cluster	an optional vector of cluster identifiers for multilevel meta-analysis.
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.
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".
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.
ai	a vector of the number of events in the treatment or experimental group for binomial GLMM models.
bi	a vector of the number of non-events in the treatment or experimental group for binomial GLMM models.
ci	a vector of the number of events in the control group for binomial GLMM models.
di	a vector of the number of non-events in the control group for binomial GLMM models.
n1i	a vector of the sample size in the treatment or experimental group. If omitted for binomial GLMMs, it is computed as ai + bi.
n2i	a vector of the sample size in the control group. If omitted for binomial GLMMs, it is computed as ci + di.
x1i	a vector of the number of events in the treatment/experimental group (for Poisson data).
x2i	a vector of the number of events in the control group (for Poisson data).
t1i	a vector of the person-time in the treatment/experimental group.
t2i	a vector of the person-time in the control group.

RoBMA documentation built on May 7, 2026, 5:08 p.m.