Compares the complete-case analytic sample of a reference model against each
other model in a named list, flagging covariates whose sample composition
shifts (|(S)MD| > threshold). Engine-agnostic: it reads variable names from
each model and recomputes samples on data, so it works for lm, glm,
feols, coxph, weighted fits, etc. Models are never refit.
Usage
balance_models(
models,
data,
reference = 1,
covariates = NULL,
threshold = 0.1,
extra_vars = NULL,
include_outcome = FALSE
)Arguments
- models
Named list of fitted models. The names label the comparisons.
- data
The shared source data frame (a superset of every model's rows).
- reference
Name or index of the model that defines the baseline sample.
- covariates
Optional character vector of covariates to assess. Default: the union of all models' predictors present in
data.- threshold
Flag covariates with
|(S)MD|above this (default 0.1).- extra_vars
Additional variables that drive listwise deletion (e.g. a weight or cluster column not detected from the call).
- include_outcome
Assess the outcome variable too (default FALSE).
Value
An object of class DAGassist_balance: a list with $reference,
$comparisons (per-comparison (S)MD tables) and $summary (one row per
comparison: n_ref, n_cmp, n_covariates, n_flagged, pct_flagged, any_flagged).
Examples
# Rows with missing C drop out of the Original model only
d <- toy_data
d$C[d$Z > 1] <- NA
balance_models(
list(Original = lm(Y ~ X + M + C + Z, data = d),
Minimal = lm(Y ~ X + Z, data = d)),
data = d
)
#> Balance diagnostics (reference: Original)
#> |(S)MD| > 0.10 flags a covariate whose sample shifts vs the reference (binary vars use a raw difference).
#> Original vs Minimal: n = 1698 vs 2000 [!] 3 covariate(s) imbalanced
#> Z (S)MD = -0.348
#> X (S)MD = -0.187
#> M (S)MD = -0.143