Estimates Hedges' g for a two-group contrast in each feature/pathway and omic. Inputs should already be quality-controlled and normalized appropriately for their assay technology. Hedges' g is scale-free but does not repair poor raw preprocessing, severe censoring, or inappropriate transformations.
Usage
estimate_effects(
data,
group,
reference,
comparison,
entities = NULL,
min_n = 3L,
conf_level = 0.95,
p_adjust = "BH"
)Arguments
- data
An `omics_braid_data` object.
- group
Metadata column containing the two groups.
- reference
Reference group label.
- comparison
Comparison group label. Positive effects mean comparison > reference.
- entities
Optional character vector limiting features/pathways.
- min_n
Minimum non-missing observations per group and omic.
- conf_level
Confidence level.
- p_adjust
Multiple-testing method applied separately within each omic.