Elamur Groupantsmlab-builtins-stats-contingency
The groupant beside mlab/src/builtins/stats.rs in the Mova repository: what the code computes, what it never does, how to go back from its outputs, its invariants, cost and checks.
Facts
Implementation material. A Mova code model describes this code at one source revision: partial models, finite checks and their stated scope. It is not a reviewed general exposition, and its labels are shown as written, never raised.
Components
| component | label | where |
|---|---|---|
| approximant | partial | approximant |
| ast | partial | ../syntax.json |
| cost | symbolic source derived | invariant |
| descriptant | present | descriptant |
| invariant | partial | invariant |
| tests | partial bounded parent evidence | tests |
Obernants
The typed relations around the forward model: the behaviour complement (what the code never does), the reverse reconstruction (every input compatible with an output) and specialised views.
| id | type | label | where |
|---|---|---|---|
behavior_complement | behavior_complement | partial source derived model | behavior_complement |
reverse_reconstruction | reverse_reconstruction | partial source derived model | reverse_reconstruction |
marginal_transport | marginal_transport | partial source derived model | marginal_transport |
category_missing | category_missing | partial source derived model | category_missing |
branch_relation | branch_relation | partial source derived model | branch_relation |
resource_cost | resource_cost | partial source derived model | resource_cost |
Model text
descriptant
mlab-builtins-stats-contingency
chi2_ind takes rectangular nonempty counts, computes marginals and rejects <=0 totals; Eij=ri*cj/N, statistic sum(O-E)^2/E, df=(r-1)(c-1)+fault. crosstab categorizes using table groups, counting missing category as group.
Epoch 2026-10-31: isolated source-bound fragment over bytes [25404,29854); includes no claim beyond this relation. Source hash bdc3772b09d9113e108afa29e391fee97a5372caf9052bb1689996c41c94116f.
approximant
contingency: forward relation
chi2_ind takes rectangular nonempty counts, computes marginals and rejects <=0 totals; Eij=ri*cj/N, statistic sum(O-E)^2/E, df=(r-1)(c-1)+fault. crosstab categorizes using table groups, counting missing category as group.
Domain: Finite, well-shaped bounded observations unless branch explicitly describes NaN/Inf. Direct public chi2/OLS helpers assume dimensions not checked at every index; wrappers add validation.
Executable surrogate gap: no measured cheaper implementation with a useful scoped metric is supplied. Categorical/error/effect relations are exact obligations, not percent losses. Future numeric caps only1/2/5/10%.
invariant
contingency: invariant and cost
Expected row/column sums reproduce observed marginals under finite arithmetic up to rounding.
NaN categories group, unlike missing join keys; wrapper chi2test requires>=2x2 before options.
Scope-specific cost: O(r*c) time and expected matrix storage, plus category sorting O(n log n); invalid marginals fail before matrix fill.
models
behavior_complement
Positive marginals do not ensure nonnegative cells; direct core does not validate that. crosstab degenerate category dimensions returns chi2=0,p=1.
reverse_reconstruction
Expected table admits many observed tables with same marginals; chi2 scalar further collapses dimensional arrangement. Category indices admit all value relabelings preserving sort order.
marginal_transport
Expected row/column sums reproduce observed marginals under finite arithmetic up to rounding.
category_missing
NaN categories group, unlike missing join keys; wrapper chi2test requires>=2x2 before options.
branch_relation
chi2_ind takes rectangular nonempty counts, computes marginals and rejects <=0 totals; Eij=ri*cj/N, statistic sum(O-E)^2/E, df=(r-1)(c-1)+fault. crosstab categorizes using table groups, counting missing category as group.
resource_cost
O(r*c) time and expected matrix storage, plus category sorting O(n log n); invalid marginals fail before matrix fill.
tests
Evidence boundary
See ../tests.md for the actual selected source test and mutant. These tests sample the file, not every fragment branch. Positive marginals do not ensure nonnegative cells; direct core does not validate that. crosstab degenerate category dimensions returns chi2=0,p=1. Other branches here are source-derived and unexecuted unless the parent test explicitly calls them.
This page in Markdown → https://md.groupants.elamur.ai/free/en/mova/mlab/src/builtins/stats.rs.groupant/contingency.groupant/