DOE Response Analysis

Overview

Use drcutils.doe response-analysis helpers to estimate main effects and fit lightweight screening models after data collection.

Quick Start

import pandas as pd
from drcutils.doe import analyze_doe_response

df = pd.read_csv("data/doe_results.csv")
result = analyze_doe_response(df, response="yield")
print(result["interpretation"])

CLI

drc-doe-analyze --input data/doe_results.csv --response-col yield --out-dir artifacts/doe_analysis

Limitations

Screening models require numeric factor columns and drcutils[stats].
This release focuses on main effects and simple pairwise interactions only.

API Reference

Post-hoc DOE response analysis helpers.

drcutils.doe.analysis.analyze_doe_response(df, *, response, factor_columns=None, include_interactions=False, alpha=0.05)[source]: Run a one-stop DOE response analysis.

drcutils.doe.analysis.compute_main_effects(df, *, response, factor_columns=None)[source]: Compute main-effect summaries from a DOE response table.

drcutils.doe.analysis.fit_screening_model(df, *, response, factor_columns=None, include_interactions=False, alpha=0.05)[source]: Fit a screening OLS model on numeric DOE factors.