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pj
019d608f65
Steps to first violation with clean runs right-censored at the budget, since per-run yield is a binary at 11 to 45 percent and separating two arms on it would need roughly 80 runs per arm. Kaplan-Meier, log-rank, Wilcoxon rank-sum with Vargha-Delaney A12, Holm within each family. A hand-rolled log-rank that is subtly wrong is a silent-wrong-number generator and would be believed, so every statistic is validated against a published worked example with the source named in the test: R survdiff on aml, Freireich 6-MP, Hollander and Wolfe 1973 for the rank sum, printed p.adjust output for Holm. Two could not be: the k>2 log-rank, guarded by calibration instead, and the tie-corrected variance, checked against an exact permutation variance. Failed and timed-out runs are excluded as missing data and counted by reason, never treated as censored observations, which would bias the result. Claude-Session: https://claude.ai/code/session_01A5KmftdEJ49A9z5mF5ESrX
75 lines
2.2 KiB
Go
75 lines
2.2 KiB
Go
package main
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import (
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"math"
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"testing"
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)
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// Chi-square critical values are the standard published table entries: the
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// upper-tail probability of each of these statistics is the stated alpha in any
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// chi-square table, for example Pearson and Hartley, Biometrika Tables for
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// Statisticians, Table 8.
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func TestChiSquareUpperTail_MatchesPublishedCriticalValues(t *testing.T) {
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cases := []struct {
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statistic float64
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degreesOfFreedom int
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expected float64
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}{
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{3.841459, 1, 0.05},
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{6.634897, 1, 0.01},
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{10.827566, 1, 0.001},
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{5.991465, 2, 0.05},
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{9.210340, 2, 0.01},
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{7.814728, 3, 0.05},
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{11.344867, 3, 0.01},
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{9.487729, 4, 0.05},
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{18.307038, 10, 0.05},
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}
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for _, test := range cases {
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got := chiSquareUpperTail(test.statistic, test.degreesOfFreedom)
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if math.Abs(got-test.expected) > 1e-6 {
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t.Errorf("chiSquareUpperTail(%v, %d) = %v, want %v", test.statistic, test.degreesOfFreedom, got, test.expected)
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}
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}
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}
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// For one degree of freedom the upper tail has the closed form erfc(sqrt(x/2)),
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// which is an independent check on the incomplete gamma routine.
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func TestChiSquareUpperTail_AgreesWithClosedFormAtOneDegreeOfFreedom(t *testing.T) {
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for _, statistic := range []float64{0.1, 1, 3.4, 16.79, 40, 120} {
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expected := math.Erfc(math.Sqrt(statistic / 2))
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got := chiSquareUpperTail(statistic, 1)
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if math.Abs(got-expected) > 1e-12*math.Max(1, expected) {
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t.Errorf("chiSquareUpperTail(%v, 1) = %v, want %v", statistic, got, expected)
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}
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}
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}
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func TestChiSquareUpperTail_ZeroStatisticIsCertain(t *testing.T) {
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if got := chiSquareUpperTail(0, 1); got != 1 {
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t.Errorf("chiSquareUpperTail(0, 1) = %v, want 1", got)
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}
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}
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// Standard normal quantiles from any published normal table.
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func TestStandardNormalUpperTail_MatchesPublishedQuantiles(t *testing.T) {
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cases := []struct {
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z float64
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expected float64
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}{
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{1.281552, 0.10},
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{1.644854, 0.05},
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{1.959964, 0.025},
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{2.326348, 0.01},
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{2.575829, 0.005},
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{3.090232, 0.001},
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{0, 0.5},
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}
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for _, test := range cases {
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got := standardNormalUpperTail(test.z)
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if math.Abs(got-test.expected) > 1e-6 {
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t.Errorf("standardNormalUpperTail(%v) = %v, want %v", test.z, got, test.expected)
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}
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}
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}
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