nx_doe.nx
buildroot/runtime/nx_doe.nx
about
nx_doe.nx -- FOOD-SCIENCE SUITE / DESIGN OF EXPERIMENTS rung. Closes the
DOE behind-axis (where Minitab/JMP lead): 2^k full-factorial and 2^(k-p)
fractional-factorial designs, with main effects and interaction effects
computed from the coded (-1/+1) sign matrix -- the same estimator Yates'
algorithm produces, done in exact integer arithmetic.
Standard (Yates) run order: factor j's coded level in run i is +1 iff
bit j of i is set, else -1. A main effect = (2/N) * sum(sign_j(i)*y_i);
an interaction effect uses the product of the two factors' signs. All
INTEGER (responses integer; effects are exact when N | 2*sum).
THE exceed vs the field: not depth over Minitab, but the SAME factorial
estimator, sovereign + composable with the rest of the food-science
suite (optimize a ferment or a formulation and feed the winner straight
into the flavor/nutrition organs).
grounded: box_hunter_hunter_2k_factorial + yates_algorithm_effects
genealogy_id: design_of_experiments + nishi_food_science_suite
dependencies 1 imports · 1 importers
imports: nx_syscalls.nx
imported by: nx_doe_test.nx
structs
| none |
consts
| none |
functions
| 23 | func doe_num_runs(k: i64) -> i64 |
| 30 | func doe_frac_runs(k: i64, p: i64) -> i64 |
| 37 | func doe_sign(run_i: i64, factor_j: i64) -> i64 |
| 43 | func doe_grand_mean(y: *i64, nruns: i64) -> i64 called by 1: main |
| 55 | func doe_main_effect(y: *i64, nruns: i64, factor_j: i64) -> i64 |
| 67 | func doe_interaction_effect(y: *i64, nruns: i64, fa: i64, fb: i64) -> i64 |
| 78 | func doe_abs(v: i64) -> i64 called by 1: doe_dominant_factor |
| 84 | func doe_dominant_factor(y: *i64, nruns: i64, k: i64) -> i64 |