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https://github.com/MBombeck/HealthLog.git
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Marc-André Bombeck
cb8ed92d78
A four-parameter interrupted time series fit (pre-trend, level change, slope change) by OLS, with a Bartlett-kernel HAC covariance, the n/(n-k) small-sample factor and Student t intervals. The tests pin the coefficients, standard errors and intervals to independently computed reference values on contiguous and gapped series.
96 lines
3.9 KiB
TypeScript
96 lines
3.9 KiB
TypeScript
/**
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* Structural guard: a standard deviation is computed in `lib/stats/spread`,
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* with its kind named at the call site.
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*
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* About a dozen hand-rolled spreads each picked population (÷ n) or sample
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* (÷ n − 1) without saying so, and the two surfaces a person compares picked
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* differently: the series API reported the population SD of a blood-pressure
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* series, the MCP and AI payload the sample SD of the same readings, about
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* 22 % apart at three readings. The reading spread both report is now
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* `READING_SPREAD`.
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*
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* The matcher flags a squared deviation from a mean in a file that also takes
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* a square root. The files below compute one for a test statistic or a fit,
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* never as a reported spread, and say so. The doctor report's glucose panel
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* composes its SD from per-bucket sums of squares (sample, the same rule as
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* `glucoseSD`) and is out of the matcher's reach by shape. Comments are
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* stripped first; the planted case proves the matcher fires.
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*/
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import { readFileSync } from "node:fs";
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import { join } from "node:path";
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import { describe, expect, it } from "vitest";
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import { stripComments, walkSourceFiles } from "./helpers/source-files";
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const SRC = join(process.cwd(), "src");
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const ALLOWED: Record<string, string> = {
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"lib/stats/spread.ts": "The implementation.",
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"lib/insights/correlations.ts":
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"The correlation engine's centred sums and the Welch t-test variances.",
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"lib/insights/mood-tag-influence.ts": "Welch t-test variances.",
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"lib/insights/mood-pattern-cards.ts": "A pooled variance for a t statistic.",
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"lib/stats/segmented-regression.ts":
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"The total sum of squares behind R² and the HAC standard errors of a segmented fit; neither is reported as a spread.",
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"lib/insights/derived/trajectory.ts":
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"The residual standard error of a regression fit (÷ n − 2).",
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"lib/insights/derived/sleep-score.ts":
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"A circular SD of the sleep midpoint around its circular mean, the input of the consistency score; `spread` has no circular distance, and the score is never shown as a spread.",
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};
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/**
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* A squared deviation: `(x - m) ** 2`, `(x - m) * (x - m)`, or a two-argument
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* distance squared, `dist(x, m) ** 2` (a circular or otherwise custom
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* deviation is still a hand-rolled spread).
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*/
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const SQUARED_DEVIATION =
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/\(\s*[\w.[\]]+\s*-\s*[\w.[\]]+\s*\)\s*\*\*\s*2|\(\s*([\w.[\]]+)\s*-\s*([\w.[\]]+)\s*\)\s*\*\s*\(\s*\1\s*-\s*\2\s*\)|\b[A-Za-z_]\w*\s*\(\s*[\w.[\]]+\s*,\s*[\w.[\]]+\s*\)\s*\*\*\s*2/;
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function computesSpread(code: string): boolean {
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return SQUARED_DEVIATION.test(code) && code.includes("Math.sqrt(");
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}
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function read(file: string): string {
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return stripComments(readFileSync(join(SRC, file), "utf8"));
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}
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describe("one standard deviation", () => {
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it("the matcher fires on a hand-rolled spread", () => {
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expect(
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computesSpread(
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"const v = xs.reduce((s, x) => s + (x - m) ** 2, 0) / n; Math.sqrt(v);",
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),
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).toBe(true);
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expect(
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computesSpread("acc += (v - mean) * (v - mean); Math.sqrt(acc / n);"),
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).toBe(true);
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expect(
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computesSpread(
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"xs.reduce((s, v) => s + circularMinuteDistance(v, mean) ** 2, 0); Math.sqrt(x);",
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),
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).toBe(true);
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});
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it("no file outside the list computes its own", () => {
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const offenders = walkSourceFiles(SRC, { floor: 3000 })
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.filter(
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(file) =>
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!file.startsWith("generated/") &&
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!file.includes("__tests__/") &&
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!/\.test\.tsx?$/.test(file),
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)
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.filter((file) => computesSpread(read(file)) && !(file in ALLOWED));
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expect(offenders).toEqual([]);
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});
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it("the series API and the AI and MCP payload report the same spread", () => {
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expect(read("app/api/measurements/series/route.ts")).toMatch(
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/standardDeviation\(\s*values,\s*READING_SPREAD\s*\)/,
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);
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const features = read("lib/insights/features.ts");
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expect(
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features.match(/roundedSpread\(\s*vals,\s*READING_SPREAD\s*\)/g),
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).toHaveLength(2);
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});
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});
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