From 4af6867ede1d70d82b71a78ed6ffd14dfeaaea95 Mon Sep 17 00:00:00 2001 From: igerber Date: Sat, 18 Jul 2026 17:00:47 -0400 Subject: [PATCH 1/2] feat(rdd): RDPlot - optimal data-driven RD plots (CCT 2015, rdplot parity) PR-B of the rdplot arc (paper review merged in #694). New RDPlot class + RDPlotResult in diff_diff/rdplot.py, parity-targeting rdplot() in CRAN rdrobust 4.0.0: all 8 binselect bin-count selectors (ES/QS x IMSE-optimal/mimicking-variance x spacings/polynomial-regression), manual nbins/scale/h/support/ci, masspoints detection with the spacings-to-pr adjust remap, per-bin means/SEs/CIs (vars_bins, R column names), 500-point-per-side global-fit curves (vars_poly), implied scale + Supplement S.1 WIMSE weights in summary(), covariate-adjusted plots reusing the #691 partialled-gamma machinery, and an optional lazy matplotlib RDPlotResult.plot() (matplotlib not a dependency). Golden parity on 24 configs (benchmarks/R/generate_rdplot_golden.R -> rdplot_golden.json) incl. the vendored Senate data; supplement Figures SA-1/SA-2 selector outputs asserted as JSON-independent paper anchors. R quirks replicated and REGISTRY-documented (left-edge slot reflection under empty bins, qs+nbins label, Inf J_IMSE on zero-variance sides, negative-sigma2 floor, k=4->3->2 raises-only fallback ladder, QS type-7 cutpoints). Documented deviations/defensive guards: fractional/pair scale takes CCT 2015 Eq 2's ceiling (R crashes there), missing-row and discrete-outcome warnings, zero-effective-h / single-support-point / tiny-side clear errors, covariate-name validation shared with the estimator. Docs sweep: REGISTRY RDPlot section, api rst, references, llms.txt/llms-full.txt, README one-liner, doc-deps entry, CHANGELOG. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01VwhFMnFQGBumYfwbeQaUjm --- CHANGELOG.md | 20 + README.md | 1 + benchmarks/R/generate_rdplot_golden.R | 203 +++++ benchmarks/data/rdplot_golden.json | 1129 +++++++++++++++++++++++++ diff_diff/__init__.py | 7 + diff_diff/guides/llms-full.txt | 64 ++ diff_diff/guides/llms.txt | 1 + diff_diff/rdplot.py | 1127 ++++++++++++++++++++++++ docs/api/regression_discontinuity.rst | 36 +- docs/doc-deps.yaml | 20 + docs/methodology/REGISTRY.md | 156 +++- docs/references.rst | 4 + tests/test_rdplot.py | 612 ++++++++++++++ 13 files changed, 3372 insertions(+), 8 deletions(-) create mode 100644 benchmarks/R/generate_rdplot_golden.R create mode 100644 benchmarks/data/rdplot_golden.json create mode 100644 diff_diff/rdplot.py create mode 100644 tests/test_rdplot.py diff --git a/CHANGELOG.md b/CHANGELOG.md index 52858cb97..86de19c7c 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -31,6 +31,26 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 bootstrap re-derivation (T24 precedent). Registered in the RTD toctree (Business Applications), the tutorials catalog (also backfilling the missing Tutorial 25 entry), and `docs/doc-deps.yaml`. +- **`RDPlot` - optimal data-driven regression discontinuity plots (Calonico, + Cattaneo & Titiunik 2015), parity-targeting R `rdrobust` 4.0.0's `rdplot()`.** + Per-side global polynomial fits (default p=4, uniform kernel) plus binned + local means with data-driven bin counts: all 8 `binselect` selectors + (evenly/quantile-spaced x IMSE-optimal/mimicking-variance x + spacings/polynomial-regression variance estimators), manual + `nbins`/`scale`/`h`/`support` knobs, masspoints detection with the + spacings-to-`pr` adjust remap, per-bin means/SEs/CIs (`vars_bins` with R's + column names), the 500-point-per-side global-fit curve (`vars_poly`), and + implied-scale/WIMSE-weight reporting per the paper's Supplement S.1. + Covariate-adjusted plots (`fit(..., covariates=...)`, R's `covs=` with + `covs_eval="mean"`) reuse the RD estimator's partialled-gamma machinery + including its collinearity pipeline and degenerate-adjustment guards. + Rendering is an optional `RDPlotResult.plot(ax=...)` (matplotlib is NOT a + dependency). Golden-tested against R on 24 configs incl. the vendored + Senate data, with the paper's own figure numbers as JSON-independent + anchors. Documented deviations: fractional/pair `scale` products take CCT + 2015 Eq 2's ceiling where R 4.0.0 crashes by accident; R's left-side + bin-edge slot-reflection quirk under empty bins is replicated for parity + and documented. ## [3.8.0] - 2026-07-18 diff --git a/README.md b/README.md index d4ca17737..54b4ab68e 100644 --- a/README.md +++ b/README.md @@ -124,6 +124,7 @@ Full guide: `diff_diff.get_llm_guide("practitioner")`. ## Diagnostics & Sensitivity +- [RD Plots](https://diff-diff.readthedocs.io/en/stable/api/regression_discontinuity.html) - Calonico, Cattaneo & Titiunik (2015) optimal data-driven RD plots (`RDPlot`): all 8 rdrobust `binselect` bin selectors, implied-scale/WIMSE-weight reporting, optional matplotlib rendering - [Parallel Trends Testing](https://diff-diff.readthedocs.io/en/stable/api/diagnostics.html) - simple and Wasserstein-robust parallel trends tests, equivalence testing (TOST) - [Placebo Tests](https://diff-diff.readthedocs.io/en/stable/api/diagnostics.html) - placebo timing, group, permutation, leave-one-out - [Honest DiD](https://diff-diff.readthedocs.io/en/stable/api/honest_did.html) - Rambachan & Roth (2023) sensitivity analysis: robust CI under PT violations, breakdown values diff --git a/benchmarks/R/generate_rdplot_golden.R b/benchmarks/R/generate_rdplot_golden.R new file mode 100644 index 000000000..94fbc0ffa --- /dev/null +++ b/benchmarks/R/generate_rdplot_golden.R @@ -0,0 +1,203 @@ +# Golden-value generator for the diff-diff RD PLOT port +# (diff_diff/rdplot.py::RDPlot). Parity target: CRAN rdrobust 4.0.0 +# (tarball sha256 +# 78f0d6b4bdec4091cc8f42f6f1598704747f95926446d3aaee381ea1d613a36f); +# the GitHub 4.1.0-dev tree must NOT be installed when regenerating. +# +# Outputs benchmarks/data/rdplot_golden.json: per config the input +# arguments, the selector outputs (J / J_IMSE / J_MV / rscale / bin_avg / +# bin_med), counts, descriptive strings, per-side global-fit coefficients, +# the FULL vars_bins table, and vars_poly at a fixed downsample of the +# 1000 curve points. DGP inputs are embedded at 17 significant digits. +# +# Configs stay inside the surface where R 4.0.0's rdplot() actually runs: +# fractional scale*J products and length-2 scale CRASH R (vector-indexing +# accident / vectorized-if error on R >= 4.2) and are covered by +# Deviation-locked Python unit tests instead of goldens. +# +# Run from the repo root: Rscript benchmarks/R/generate_rdplot_golden.R + +library(rdrobust) +library(jsonlite) + +stopifnot(packageVersion("rdrobust") == "4.0.0") + +TARBALL_SHA256 <- "78f0d6b4bdec4091cc8f42f6f1598704747f95926446d3aaee381ea1d613a36f" + +POLY_IDX <- sort(unique(c(seq(1, 1000, by = 25), 500L, 501L, 1000L))) + +run_rdplot <- function(name, y, x, c = 0, p = 4, nbins = NULL, + binselect = "esmv", scale = NULL, kernel = "uni", + h = NULL, support = NULL, masspoints = "adjust", + ci = NULL, covs = NULL, covs_drop = TRUE) { + args <- list(y = y, x = x, c = c, p = p, binselect = binselect, + kernel = kernel, masspoints = masspoints, hide = TRUE, + covs_drop = covs_drop) + if (!is.null(nbins)) args$nbins <- nbins + if (!is.null(scale)) args$scale <- scale + if (!is.null(h)) args$h <- h + if (!is.null(support)) args$support <- support + if (!is.null(ci)) args$ci <- ci + if (!is.null(covs)) args$covs <- covs + r <- tryCatch( + suppressWarnings(suppressMessages(do.call(rdplot, args))), + error = function(e) stop(sprintf("config '%s' failed in R: %s", + name, conditionMessage(e))) + ) + vb <- r$vars_bins + vp <- r$vars_poly + out <- list( + # input echoes (the Python test rebuilds RDPlot(...) from these) + args = list( + c = c, p = p, + nbins = if (is.null(nbins)) NA else nbins, + binselect = binselect, + scale = if (is.null(scale)) NA else scale, + kernel = kernel, + h = if (is.null(h)) NA else h, + support = if (is.null(support)) NA else support, + masspoints = masspoints, + ci = if (is.null(ci)) NA else ci, + covs_in = !is.null(covs), + covs_names = if (is.null(covs)) NA else colnames(covs), + covs_drop = covs_drop + ), + J = unname(r$J), J_IMSE = unname(r$J_IMSE), J_MV = unname(r$J_MV), + scale_out = unname(r$scale), rscale = unname(r$rscale), + bin_avg = unname(r$bin_avg), bin_med = unname(r$bin_med), + h_out = unname(r$h), N = unname(r$N), N_h = unname(r$N_h), + binselect_type = r$binselect, kernel_type = r$kernel, + coef_left = unname(r$coef[, 1]), coef_right = unname(r$coef[, 2]), + vars_bins = list( + mean_bin = vb$rdplot_mean_bin, mean_x = vb$rdplot_mean_x, + mean_y = vb$rdplot_mean_y, min_bin = vb$rdplot_min_bin, + max_bin = vb$rdplot_max_bin, se_y = vb$rdplot_se_y, + N = vb$rdplot_N, ci_l = vb$rdplot_ci_l, ci_r = vb$rdplot_ci_r + ), + vars_poly_idx = POLY_IDX, + vars_poly_x = vp$rdplot_x[POLY_IDX], + vars_poly_y = vp$rdplot_y[POLY_IDX] + ) + if (!is.null(covs)) out$coef_covs <- unname(as.vector(r$coef_covs)) + out +} + +golden <- list() + +golden$metadata <- list( + rdrobust_version = as.character(packageVersion("rdrobust")), + rdrobust_tarball_sha256 = TARBALL_SHA256, + seeds = list(dgp_ties = 123L, dgp_covs = 2718L, dgp_small = 555L, + dgp_ladder = 999L), + generator = "benchmarks/R/generate_rdplot_golden.R", + algorithm = paste( + "rdplot() bin selectors (all 8 binselect values), manual", + "nbins/scale/h/support/ci knobs, masspoints adjust remap, covariate", + "adjustment, and the k=4->3->2 selector-fit fallback ladder; per", + "config the full vars_bins table and downsampled vars_poly curve." + ), + r_version = R.version.string +) + +## --- Senate data (vendored CSV; same complete-case filter as rdplot) ---- +senate_path <- "benchmarks/data/rdrobust_senate.csv" +stopifnot(file.exists(senate_path)) +senate <- read.csv(senate_path) +ok <- complete.cases(senate$vote, senate$margin) +sv <- senate$vote[ok] +sm <- senate$margin[ok] + +golden$senate <- list( + csv = "benchmarks/data/rdrobust_senate.csv", + configs = list( + default = run_rdplot("default", sv, sm), + es = run_rdplot("es", sv, sm, binselect = "es"), + espr = run_rdplot("espr", sv, sm, binselect = "espr"), + esmvpr = run_rdplot("esmvpr", sv, sm, binselect = "esmvpr"), + qs = run_rdplot("qs", sv, sm, binselect = "qs"), + qspr = run_rdplot("qspr", sv, sm, binselect = "qspr"), + qsmv = run_rdplot("qsmv", sv, sm, binselect = "qsmv"), + qsmvpr = run_rdplot("qsmvpr", sv, sm, binselect = "qsmvpr"), + p2 = run_rdplot("p2", sv, sm, p = 2), + p0 = run_rdplot("p0", sv, sm, p = 0), + p1_tri_h20 = run_rdplot("p1_tri_h20", sv, sm, p = 1, kernel = "tri", + h = 20), + epa_h_asym = run_rdplot("epa_h_asym", sv, sm, kernel = "epa", + h = c(15, 25)), + nbins_asym = run_rdplot("nbins_asym", sv, sm, nbins = c(10, 14)), + scale2 = run_rdplot("scale2", sv, sm, scale = 2), + support_w = run_rdplot("support_w", sv, sm, support = c(-110, 110)), + ci90 = run_rdplot("ci90", sv, sm, ci = 90), + qs_nbins = run_rdplot("qs_nbins", sv, sm, binselect = "qs", nbins = 8) + ) +) + +## --- Tied running variable (masspoints machinery) ----------------------- +# Same seeded recipe as generate_rdrobust_estimates_golden.R dgp_ties. +set.seed(123) +n2 <- 800 +x2 <- round(2 * rbeta(n2, 2, 4) - 1, 2) +y2 <- 0.5 + 0.8 * x2 + (x2 >= 0) * 1.0 + rnorm(n2, sd = 0.3) + +golden$dgp_ties <- list( + x = x2, y = y2, + configs = list( + # default esmv under adjust remaps to esmvpr (rdplot.R:130-135) + adjust = run_rdplot("adjust", y2, x2, masspoints = "adjust"), + off = run_rdplot("off", y2, x2, masspoints = "off"), + # explicit esmvpr with detection off must EQUAL the adjust config + # (the Python suite asserts the cross-config identity) + esmvpr_off = run_rdplot("esmvpr_off", y2, x2, binselect = "esmvpr", + masspoints = "off") + ) +) + +## --- Covariates (same seeded recipe as the estimates golden) ------------ +set.seed(2718) +n5 <- 1200 +x5 <- 2 * rbeta(n5, 2, 4) - 1 +zlong <- 0.5 * x5 + rnorm(n5, sd = 0.8) +zb <- rbinom(n5, 1, 0.4) +y5 <- 0.4 * x5 + 0.9 * (x5 >= 0) + 0.7 * zlong + 0.3 * zb + rnorm(n5, sd = 0.3) +zdup <- 1.5 * zlong - 0.5 * zb +covs2 <- cbind(zlong = zlong, zb = zb) # UNSORTED names: pins order(nchar) +covs3 <- cbind(zlong = zlong, zb = zb, zdup = zdup) + +golden$dgp_covs <- list( + x = x5, y = y5, zlong = zlong, zb = zb, zdup = zdup, + configs = list( + covs_default = run_rdplot("covs_default", y5, x5, covs = covs2), + covs_collinear = run_rdplot("covs_collinear", y5, x5, covs = covs3) + ) +) + +## --- Small sample (n = 25; just above rdplot's n >= 20 hard floor) ------ +set.seed(555) +n6 <- 25 +x6 <- runif(n6, -1, 1) +y6 <- 0.3 + 0.5 * x6 + 0.8 * (x6 >= 0) + rnorm(n6, sd = 0.4) + +golden$dgp_small <- list( + x = x6, y = y6, + configs = list(default = run_rdplot("small_default", y6, x6)) +) + +## --- Selector-fit fallback ladder (k = 4 -> 3) -------------------------- +# |x| ~ 1e40 keeps every x^4 design entry finite but overflows the k=4 +# Gram (entries ~x^8 -> Inf), so R's try(qrXXinv) errors and the ladder +# drops to k=3 (rdplot.R:281-298). Extreme magnitudes make float surfaces +# BLAS-sensitive, so the Python suite asserts only the integer/string +# outputs for this DGP. +set.seed(999) +n7 <- 120 +x7 <- c(-runif(50, 1e39, 2e39), runif(70, 1e39, 2e39)) +y7 <- rnorm(n7) + +golden$dgp_ladder <- list( + x = x7, y = y7, + configs = list(default = run_rdplot("ladder_default", y7, x7)) +) + +out_path <- "benchmarks/data/rdplot_golden.json" +write_json(golden, out_path, auto_unbox = TRUE, pretty = TRUE, digits = I(17)) +cat("Wrote", out_path, "\n") diff --git a/benchmarks/data/rdplot_golden.json b/benchmarks/data/rdplot_golden.json new file mode 100644 index 000000000..76968624e --- /dev/null +++ b/benchmarks/data/rdplot_golden.json @@ -0,0 +1,1129 @@ +{ + "metadata": { + "rdrobust_version": "4.0.0", + "rdrobust_tarball_sha256": "78f0d6b4bdec4091cc8f42f6f1598704747f95926446d3aaee381ea1d613a36f", + "seeds": { + "dgp_ties": 123, + "dgp_covs": 2718, + "dgp_small": 555, + "dgp_ladder": 999 + }, + "generator": 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( + RDPlot, + RDPlotResult, +) from diff_diff.results import ( DiDResults, MultiPeriodDiDResults, @@ -519,6 +523,9 @@ "RegressionDiscontinuity", "RegressionDiscontinuityResults", "RDD", + # RDPlot (data-driven RD plots, rdplot parity) + "RDPlot", + "RDPlotResult", # HeterogeneousAdoptionDiD pre-test diagnostics (Phase 3) "qug_test", "stute_test", diff --git a/diff_diff/guides/llms-full.txt b/diff_diff/guides/llms-full.txt index 615d71c68..74b2de0f9 100644 --- a/diff_diff/guides/llms-full.txt +++ b/diff_diff/guides/llms-full.txt @@ -900,6 +900,70 @@ covariate-balance helper are documented follow-ups; missing rows are dropped WITH a warning (R drops silently); N < 20 falls back to full-range bandwidths exactly as rdrobust does (overriding manual h). +### RDPlot + +RD plot diagnostic (Calonico, Cattaneo & Titiunik 2015), parity-targeting R +rdrobust 4.0.0's `rdplot()`. NOT a treatment-effect estimator: builds the +standard exploratory RD plot - per-side global order-p polynomial fits +(default p=4, uniform kernel over each side's full range; deliberately +different defaults from estimation) plus binned local sample means with a +DATA-DRIVEN number of bins per side. All 8 `binselect` selectors = +{evenly (es) / quantile (qs) spaced} x {IMSE-optimal / mimicking variance +(mv)} x {spacings / polynomial-regression (pr) variance estimators}; the +result reports `J`, `J_IMSE`, `J_MV`, the implied scale `rscale = J/J_IMSE`, +and the CCT 2015 Supplement S.1 WIMSE weights (variance `1/(1+rscale^3)`, +bias `rscale^3/(1+rscale^3)`), matching R's `summary.rdplot`. + +```python +RDPlot( + cutoff: float = 0.0, # Known threshold of the running variable + p: int = 4, # Global polynomial order (R's rdplot default) + nbins: int | tuple | None = None, # Manual bins per side (overrides the selector) + binselect: str = "esmv", # es/espr/esmv/esmvpr/qs/qspr/qsmv/qsmvpr + scale: float | tuple | None = None, # Multiplies the selected J (fractional products take CCT 2015 Eq 2's ceiling; R crashes there) + kernel: str = "uniform", # Weighting for the global fit within h + h: float | tuple | None = None, # Global-fit window; default = full per-side range + support: tuple | None = None, # (lower, upper); only WIDENS the observed range + masspoints: str = "adjust", # >= 20% ties: warn + switch spacings binselect to its *pr sibling ("check" warns only; "off" disables) + ci: float | None = None, # Per-bin CI level for plotted error bars (CI columns are ALWAYS computed, at 95 when None) + covs_drop: bool = True, # Drop collinear covariates with a warning; False = strict error +) +``` + +**fit() parameters:** + +```python +rp.fit( + data: pd.DataFrame, + outcome_col: str, + running_col: str, + covariates: list[str] | None = None, # Covariate-adjusted plot (R's covs=, covs_eval="mean"); gammas exposed as covariate_coefficients +) -> RDPlotResult +``` + +```python +from diff_diff import RDPlot + +rp = RDPlot(cutoff=0.0) +res = rp.fit(df, outcome_col="y", running_col="score") +res.J, res.J_IMSE, res.J_MV # selected / IMSE-optimal / mimicking-variance bins per side +res.rscale # implied scale; WIMSE weights via res.wimse_variance_weight / res.wimse_bias_weight +res.to_dataframe() # vars_bins: R-named per-bin columns (rdplot_mean_bin/mean_x/mean_y/min_bin/max_bin/se_y/N/ci_l/ci_r) +res.vars_poly # 500-point global-fit curve per side +print(res.summary()) # R summary.rdplot layout incl. implied scale + WIMSE weights +res.plot() # optional matplotlib rendering (matplotlib NOT a dependency; ImportError if absent) +``` + +Empty bins are dropped from vars_bins (R's tapply) while bin_avg/bin_med +cover all partition cells; single-observation bins get se = 0 (R's NA -> 0 +fixup); n < 20 raises outright (R behavior - no fallback); a side with +zero outcome variance pins its J to 1 with a warning and echoes R's Inf +J_IMSE. With empty LEFT-side bins, R's own rdplot_min_bin/rdplot_max_bin +come from mirrored grid slots and can disagree with rdplot_mean_bin - an +R quirk replicated for parity (rdplot_mean_bin is the reliable bin +identity). Sampling weights and R's subset= are documented seams (filter +the DataFrame instead). + ### StackedDiD Stacked DiD estimator (Wing, Freedman & Hollingsworth 2024). Addresses TWFE bias with corrective Q-weights. diff --git a/diff_diff/guides/llms.txt b/diff_diff/guides/llms.txt index 2bea9ce6d..efeedc5f5 100644 --- a/diff_diff/guides/llms.txt +++ b/diff_diff/guides/llms.txt @@ -77,6 +77,7 @@ Full practitioner guide: call `diff_diff.get_llm_guide("practitioner")` ## Diagnostics and Sensitivity Analysis +- [RD Plots](https://diff-diff.readthedocs.io/en/stable/api/regression_discontinuity.html): Calonico, Cattaneo & Titiunik (2015) optimal data-driven RD plots (`RDPlot`), parity with R rdrobust 4.0.0's `rdplot()` - all 8 `binselect` bin-count selectors (evenly/quantile spaced x IMSE-optimal/mimicking-variance x spacings/polynomial-regression), implied scale + WIMSE weights per Supplement S.1, optional covariate adjustment reusing the RD estimator's pipeline, optional lazy-matplotlib rendering (matplotlib not a dependency) - [Parallel Trends Testing](https://diff-diff.readthedocs.io/en/stable/api/diagnostics.html): Simple and Wasserstein-robust parallel trends tests, equivalence testing (TOST) - [Placebo Tests](https://diff-diff.readthedocs.io/en/stable/api/diagnostics.html): Placebo timing, group, permutation, and leave-one-out diagnostics - [Honest DiD](https://diff-diff.readthedocs.io/en/stable/api/honest_did.html): Rambachan & Roth (2023) sensitivity analysis — robust CI under parallel trends violations, breakdown values diff --git a/diff_diff/rdplot.py b/diff_diff/rdplot.py new file mode 100644 index 000000000..0b37677e9 --- /dev/null +++ b/diff_diff/rdplot.py @@ -0,0 +1,1127 @@ +"""Optimal data-driven regression discontinuity plots (Calonico, Cattaneo & +Titiunik 2015, JASA) with end-to-end parity against R ``rdrobust`` 4.0.0's +``rdplot()``. + +An RD plot has two ingredients (CCT 2015, Section 2.1): global order-``p`` +polynomial fits of the outcome on the running variable, estimated separately +on each side of the cutoff, and binned local sample means of the outcome over +a disjoint partition of each side's support. :class:`RDPlot` selects the +number of bins with the paper's data-driven selectors (IMSE-optimal or +mimicking-variance, evenly-spaced or quantile-spaced partitions, spacings or +polynomial-regression variance estimators - the eight ``binselect`` values) +and returns the full set of plot quantities; rendering is a thin optional +matplotlib layer on the result. + +Parity target: ``rdplot()`` in the CRAN rdrobust 4.0.0 sources (rdplot.R; the +line references in comments below point there). All numerical outputs - +``J``/``J_IMSE``/``J_MV``, the implied scale and WIMSE weights, bin means, +per-bin CIs, and the global-fit coefficients/curves - are golden-tested +against R output on the vendored Senate dataset and simulated designs. + +Notes on scope (documented seams, mirroring :class:`RegressionDiscontinuity`): +sampling weights and R's ``subset=`` are not parameters (filter the DataFrame +instead); ``covs_eval`` is fixed at R's default ``"mean"``. R prints an +unconditional ``message()`` whenever covariates are used with the global +polynomial fit ("may deliver results that are not visually compatible with +the local binned means"); that caveat lives here in the docstring rather than +as an unavoidable runtime warning. +""" + +import math +import warnings +from dataclasses import dataclass +from typing import Any, Dict, List, Optional, Tuple, Union + +import numpy as np +import pandas as pd +from scipy import stats + +from ._rdrobust_port import ( + _covs_gamma, + _normalize_kernel, + covs_drop_fun, + qrXXinv, + rdrobust_kweight, +) +from .utils import validate_covariate_names + +__all__ = ["RDPlot", "RDPlotResult"] + +# binselect value -> (partition meth, variance family, target, description). +# Description strings are transcribed exactly from rdplot.R:395-446. +_BINSELECT_INFO: Dict[str, Tuple[str, str, str, str]] = { + "es": ("es", "hat", "imse", "IMSE-optimal evenly-spaced method using spacings estimators"), + "espr": ("es", "chk", "imse", "IMSE-optimal evenly-spaced method using polynomial regression"), + "esmv": ( + "es", + "hat", + "mv", + "mimicking variance evenly-spaced method using spacings estimators", + ), + "esmvpr": ( + "es", + "chk", + "mv", + "mimicking variance evenly-spaced method using polynomial regression", + ), + "qs": ("qs", "hat", "imse", "IMSE-optimal quantile-spaced method using spacings estimators"), + "qspr": ( + "qs", + "chk", + "imse", + "IMSE-optimal quantile-spaced method using polynomial regression", + ), + "qsmv": ( + "qs", + "hat", + "mv", + "mimicking variance quantile-spaced method using spacings estimators", + ), + "qsmvpr": ( + "qs", + "chk", + "mv", + "mimicking variance quantile-spaced method using polynomial regression", + ), +} + +# masspoints="adjust" remap under heavy ties (rdplot.R:130-135): the spacings +# variance estimators degenerate on tied data, so R switches each spacings +# selection to its polynomial-regression sibling. Explicit *pr picks are +# untouched. +_MASSPOINTS_REMAP = {"es": "espr", "esmv": "esmvpr", "qs": "qspr", "qsmv": "qsmvpr"} + +_KERNEL_TYPE_LABEL = { + "uniform": "Uniform", + "epanechnikov": "Epanechnikov", + "triangular": "Triangular", +} + +_PairF = Tuple[float, float] +_PairI = Tuple[int, int] + + +def _r_seq_by(from_: float, to: float, by: float) -> np.ndarray: + """R ``seq(from, to, by)`` for ``by > 0`` (seq.default): the point count + is ``as.integer((to - from)/by + 1e-10)`` (truncation with R's fuzz) and + the grid is clamped into ``to`` via ``pmin``.""" + m = int((to - from_) / by + 1e-10) + return np.minimum(from_ + np.arange(m + 1) * by, to) + + +def _find_interval(x: np.ndarray, vec: np.ndarray) -> np.ndarray: + """R ``findInterval(x, vec, rightmost.closed=TRUE)`` (1-based interval + ids). ``searchsorted(side='right')`` counts elements ``<= x``, which is + findInterval's answer everywhere except ``x == vec[last]``, where + rightmost.closed folds the point into the last interval. Values beyond + ``vec[last]`` keep id ``len(vec)`` exactly like R - reachable when the + quantile prob grid's last point lands one ulp short of 1 (the ``j/J`` + float products decide whether R's ``pmin`` clamp engages; the identical + IEEE arithmetic here reproduces whichever way it falls).""" + idx = np.searchsorted(vec, x, side="right") + idx[x == vec[-1]] = len(vec) - 1 + return idx + + +def _pow_outer(u: np.ndarray, degree: int) -> np.ndarray: + """R ``outer(u, 0:degree, "^")`` - elementwise pow, NOT the successive + products of ``rdrobust_vander`` (rdplot.R builds every design this way).""" + return np.power.outer(u, np.arange(degree + 1)) + + +def _resolve_pair( + value: Union[None, float, int, Tuple[Any, Any], List[Any]], default: float +) -> Tuple[float, float]: + """R's scalar-or-length-2 argument convention (rdplot.R:77-104).""" + if value is None: + return float(default), float(default) + if isinstance(value, (tuple, list)): + return float(value[0]), float(value[1]) + return float(value), float(value) + + +@dataclass +class RDPlotResult: + """Quantities of a data-driven RD plot (field names follow R's rdplot + return list; ``vars_bins`` columns carry R's ``rdplot_*`` names). + + ``J`` is always integral: a fractional ``scale * J`` product takes the + ceiling per CCT 2015 Equation 2 (see the Deviation note in + :class:`RDPlot` - R 4.0.0 crashes on that surface instead). + ``vars_bins`` contains only NON-EMPTY bins (R's ``tapply`` semantics) + while ``bin_avg``/``bin_med`` summarize ALL bin lengths of the partition. + CI columns are always computed (at ``ci_level``); ``ci_requested`` records + whether ``ci=`` was passed and is the default for drawing error bars in + :meth:`plot`. + """ + + coef: pd.DataFrame + vars_bins: pd.DataFrame + vars_poly: pd.DataFrame + J: _PairF + J_IMSE: _PairF + J_MV: _PairF + scale: _PairF + rscale: _PairF + bin_avg: _PairF + bin_med: _PairF + p: int + cutoff: float + h: _PairF + N: _PairI + N_h: _PairI + binselect: str + kernel_type: str + ci_level: float + ci_requested: bool + covariate_coefficients: Optional[Dict[str, float]] = None + covariates_dropped: Optional[List[str]] = None + + @property + def wimse_variance_weight(self) -> _PairF: + """Implied WIMSE variance weights ``1/(1 + rscale^3)`` per side + (CCT 2015 Supplement S.1; printed by R's ``summary.rdplot``).""" + return (1 / (1 + self.rscale[0] ** 3), 1 / (1 + self.rscale[1] ** 3)) + + @property + def wimse_bias_weight(self) -> _PairF: + """Implied WIMSE bias weights ``rscale^3/(1 + rscale^3)`` per side.""" + return ( + self.rscale[0] ** 3 / (1 + self.rscale[0] ** 3), + self.rscale[1] ** 3 / (1 + self.rscale[1] ** 3), + ) + + def to_dataframe(self) -> pd.DataFrame: + """The non-empty-bin table (``vars_bins``), copied.""" + return self.vars_bins.copy() + + def summary(self) -> str: + """Formatted summary mirroring R's ``summary.rdplot`` layout + (rdplot.R:610-654), including the implied scale and WIMSE weights.""" + + def _num(v: float) -> str: + return f"{v:10.3f}" + + def _int(v: float) -> str: + return f"{v:10g}" + + lines = [ + "RD Plot (CCT 2015 / rdrobust rdplot parity)", + "", + f"Number of Obs. {self.N[0] + self.N[1]:10d}", + f"Kernel {self.kernel_type:>10}", + f"Bin Method {self.binselect}", + "", + f"{'':25}{'Left':>10} {'Right':>10}", + f"Number of Obs. {self.N[0]:10d} {self.N[1]:10d}", + f"Eff. Number of Obs. {self.N_h[0]:10d} {self.N_h[1]:10d}", + f"Order poly. fit (p) {self.p:10d} {self.p:10d}", + f"BW poly. fit (h) {_num(self.h[0])} {_num(self.h[1])}", + f"Number of bins scale {_num(self.scale[0])} {_num(self.scale[1])}", + "", + f"Bins Selected {_int(self.J[0])} {_int(self.J[1])}", + f"Average Bin Length {_num(self.bin_avg[0])} {_num(self.bin_avg[1])}", + f"Median Bin Length {_num(self.bin_med[0])} {_num(self.bin_med[1])}", + "", + f"IMSE-optimal bins {_int(self.J_IMSE[0])} {_int(self.J_IMSE[1])}", + f"Mimicking Variance bins {_int(self.J_MV[0])} {_int(self.J_MV[1])}", + "", + "Relative to IMSE-optimal:", + f"Implied scale {_num(self.rscale[0])} {_num(self.rscale[1])}", + f"WIMSE variance weight {_num(self.wimse_variance_weight[0])}" + f" {_num(self.wimse_variance_weight[1])}", + f"WIMSE bias weight {_num(self.wimse_bias_weight[0])}" + f" {_num(self.wimse_bias_weight[1])}", + ] + if self.covariate_coefficients is not None: + lines += ["", "Covariate adjustment (nuisance coefficients, not causal effects):"] + lines += [f" {k:20} {v: .6g}" for k, v in self.covariate_coefficients.items()] + return "\n".join(lines) + + def plot( + self, + ax: Optional[Any] = None, + title: Optional[str] = None, + xlabel: Optional[str] = None, + ylabel: Optional[str] = None, + dots_color: Optional[str] = None, + line_color: Optional[str] = None, + show_ci: Optional[bool] = None, + ) -> Any: + """Render the RD plot (binned means, global polynomial curves, cutoff + line; error bars when CIs were requested at fit time or forced via + ``show_ci=True``). Requires matplotlib (an optional dependency). + + Returns the matplotlib axes. Rendering is cosmetic - the parity + surface of this port is the NUMBERS on the result object. + """ + try: + import matplotlib.pyplot as plt + except ImportError: + raise ImportError("matplotlib is required for plotting") + + if ax is None: + _, ax = plt.subplots(figsize=(10, 6)) + if show_ci is None: + show_ci = self.ci_requested + + vb = self.vars_bins + if show_ci: + ax.errorbar( + vb["rdplot_mean_bin"], + vb["rdplot_mean_y"], + yerr=np.vstack( + [ + vb["rdplot_mean_y"] - vb["rdplot_ci_l"], + vb["rdplot_ci_r"] - vb["rdplot_mean_y"], + ] + ), + fmt="none", + ecolor="gray", + elinewidth=1, + alpha=0.8, + zorder=1, + ) + ax.scatter( + vb["rdplot_mean_bin"], + vb["rdplot_mean_y"], + color=dots_color or "darkblue", + s=18, + zorder=2, + ) + # Split by construction (500 points per side): both sides carry an + # x == cutoff endpoint with DIFFERENT fitted values (the jump), so an + # x-based mask would misassign the right curve's cutoff point. + vp = self.vars_poly + n_side = len(vp) // 2 + for segment in (vp.iloc[:n_side], vp.iloc[n_side:]): + ax.plot( + segment["rdplot_x"], + segment["rdplot_y"], + color=line_color or "red", + zorder=3, + ) + ax.axvline(self.cutoff, color="black", linewidth=0.8) + ax.set_title(title if title is not None else "RD Plot") + ax.set_xlabel(xlabel if xlabel is not None else "Running variable") + ax.set_ylabel(ylabel if ylabel is not None else "Outcome") + return ax + + +class RDPlot: + """Data-driven RD plot builder (CCT 2015; rdrobust 4.0.0 ``rdplot()`` + parity). + + Parameters mirror R's ``rdplot()`` and keep its defaults - including + ``kernel="uniform"`` and ``p=4``, which deliberately differ from + :class:`RegressionDiscontinuity`'s estimation defaults (triangular, p=1): + the global fit is a descriptive overlay, not the local RD estimator. + + Parameters + ---------- + cutoff : float, default 0.0 + Threshold in the running variable. + p : int, default 4 + Order of the global polynomial fits (R accepts any integer >= 0). + nbins : int or (int, int), optional + Manual number of bins per side; overrides the data-driven selector. + Any positive count is accepted (R permissiveness); note that very + large bin counts allocate proportional jump grids and, with + covariates, a dense per-side bin-dummy matrix (documented + performance seam). + binselect : str, default "esmv" + One of ``es, espr, esmv, esmvpr, qs, qspr, qsmv, qsmvpr`` - partition + scheme (evenly/quantile spaced) x target (IMSE-optimal / mimicking + variance) x variance estimator (spacings / polynomial regression). + The spacings variants assume a continuously distributed OUTCOME + (CCT 2015 Theorems 3-4); heavy outcome ties trigger a warning + recommending the ``*pr`` sibling. QS cutpoints follow R: ``quantile`` + type-7 interpolation, not the paper's inf-form empirical inverse + (documented deviation-from-paper inherited from rdrobust). + scale : float or (float, float), optional + Multiplies the selected number of bins (undersmoothing knob). A + fractional product is resolved with the ceiling of CCT 2015 + Equation 2. Deviation from R: rdrobust 4.0.0 crashes on fractional + products (vector-indexing accident) and rejects a length-2 scale + outright on R >= 4.2 (vectorized-if error); both surfaces work here. + kernel : str, default "uniform" + Weighting for the global polynomial fit within ``h``. + h : float or (float, float), optional + Window for the global fit; default is each side's full range. + support : (float, float), optional + Extended support for bin construction; only WIDENS the observed + range (R semantics). + masspoints : str, default "adjust" + ``"check"`` warns when either side has >= 20% duplicate running + values; ``"adjust"`` additionally switches a spacings ``binselect`` + to its polynomial-regression sibling; ``"off"`` disables detection. + ci : float, optional + Confidence level (percent) for the per-bin intervals drawn by + :meth:`RDPlotResult.plot`. CI columns are ALWAYS computed (at 95 when + ``ci`` is None, matching R); ``ci`` only sets the level and turns the + error bars on. + covs_drop : bool, default True + Drop redundant covariates (R's pipeline: stable name-length sort + + LINPACK-style QR check) with a warning naming the dropped columns; + ``False`` raises a deterministic error on collinear covariates. + """ + + def __init__( + self, + cutoff: float = 0.0, + p: int = 4, + nbins: Union[None, int, Tuple[int, int]] = None, + binselect: str = "esmv", + scale: Union[None, float, Tuple[float, float]] = None, + kernel: str = "uniform", + h: Union[None, float, Tuple[float, float]] = None, + support: Optional[Tuple[float, float]] = None, + masspoints: str = "adjust", + ci: Optional[float] = None, + covs_drop: bool = True, + ): + self.cutoff = cutoff + self.p = p + self.nbins = nbins + self.binselect = binselect + self.scale = scale + self.kernel = kernel + self.h = h + self.support = support + self.masspoints = masspoints + self.ci = ci + self.covs_drop = covs_drop + self._validate_constructor_args() + + # ------------------------------------------------------------------ + # Configuration plumbing (sklearn-like) + # ------------------------------------------------------------------ + @staticmethod + def _is_real_scalar(val: Any) -> bool: + return isinstance(val, (int, float, np.integer, np.floating)) and not isinstance( + val, (bool, np.bool_) + ) + + @staticmethod + def _is_int_scalar(val: Any) -> bool: + return isinstance(val, (int, np.integer)) and not isinstance(val, (bool, np.bool_)) + + @classmethod + def _check_pair( + cls, + name: str, + value: Any, + *, + integer: bool = False, + positive: bool = True, + ) -> None: + """Validate R's scalar-or-length-2 numeric arguments fail-closed.""" + items = list(value) if isinstance(value, (tuple, list)) else [value] + if isinstance(value, (tuple, list)) and len(items) != 2: + raise ValueError(f"{name} must be a scalar or a (left, right) pair; got {value!r}.") + for item in items: + ok = cls._is_int_scalar(item) if integer else cls._is_real_scalar(item) + ok = ok and np.isfinite(item) and (item > 0 or not positive) + if not ok: + kind = "an integer" if integer else "a finite number" + raise ValueError(f"{name} entries must be {kind} > 0; got {item!r}.") + + def _validate_constructor_args(self) -> None: + if not (self._is_real_scalar(self.cutoff) and np.isfinite(self.cutoff)): + raise ValueError(f"cutoff must be finite; got {self.cutoff!r}.") + # R validates p >= 0 and integrality (rdplot.R:173-188); it imposes + # no upper bound (extreme orders fail later in the linear algebra). + if not (self._is_int_scalar(self.p) and self.p >= 0): + raise ValueError(f"p must be an integer >= 0; got {self.p!r}.") + if self.nbins is not None: + self._check_pair("nbins", self.nbins, integer=True) + if self.binselect not in _BINSELECT_INFO: + raise ValueError( + f"binselect must be one of {sorted(_BINSELECT_INFO)}; got {self.binselect!r}." + ) + if self.scale is not None: + self._check_pair("scale", self.scale) + _normalize_kernel(self.kernel) # raises on unknown kernel + if self.h is not None: + self._check_pair("h", self.h) + if self.support is not None: + if not ( + isinstance(self.support, (tuple, list)) + and len(self.support) == 2 + and all(self._is_real_scalar(v) and np.isfinite(v) for v in self.support) + and float(self.support[0]) < float(self.support[1]) + ): + raise ValueError( + "support must be a (lower, upper) pair of finite numbers " + f"with lower < upper; got {self.support!r}." + ) + if self.masspoints not in ("check", "adjust", "off"): + raise ValueError( + f"masspoints must be 'check', 'adjust', or 'off'; got {self.masspoints!r}." + ) + if self.ci is not None and not (self._is_real_scalar(self.ci) and 0 < self.ci < 100): + raise ValueError(f"ci must be a confidence level in (0, 100); got {self.ci!r}.") + if not isinstance(self.covs_drop, (bool, np.bool_)): + raise ValueError(f"covs_drop must be a bool; got {self.covs_drop!r}.") + + def get_params(self) -> Dict[str, Any]: + """Get parameters of this plot builder.""" + return { + "cutoff": self.cutoff, + "p": self.p, + "nbins": self.nbins, + "binselect": self.binselect, + "scale": self.scale, + "kernel": self.kernel, + "h": self.h, + "support": self.support, + "masspoints": self.masspoints, + "ci": self.ci, + "covs_drop": self.covs_drop, + } + + def set_params(self, **params: Any) -> "RDPlot": + """Set parameters (transactional: validates the full candidate + configuration before mutating this instance).""" + current = self.get_params() + unknown = set(params) - set(current) + if unknown: + raise ValueError(f"Invalid parameter(s) for RDPlot: {sorted(unknown)}") + candidate = {**current, **params} + RDPlot(**candidate) # full validation on a throwaway instance + for key, value in candidate.items(): + setattr(self, key, value) + return self + + # ------------------------------------------------------------------ + # Fit + # ------------------------------------------------------------------ + def fit( + self, + data: pd.DataFrame, + outcome_col: str, + running_col: str, + covariates: Optional[List[str]] = None, + ) -> RDPlotResult: + """Build the RD plot quantities. + + Parameters + ---------- + data : pd.DataFrame + Cross-sectional data. + outcome_col, running_col : str + Column names of the outcome and the running variable. + covariates : list of str, optional + Covariate-adjusted plot per rdplot's ``covs=`` (global fit + partials the covariates out with a pooled-across-sides gamma; + bin means use the fitted values of per-side regressions of the + outcome on covariates and bin indicators, R's + ``lm(y ~ z + factor(bin))`` with ``covs_eval="mean"``). Note + R's caveat: covariate adjustment is meant for plotting alongside + covariate-adjusted rdrobust ESTIMATES; the adjusted global fit + may not be visually compatible with unadjusted binned means. + ``covariate_coefficients`` is keyed in R's processing order: + name-length-sorted under ``covs_drop=True`` (the sort decides + which of a collinear set survives), the user-given order under + ``covs_drop=False`` (R sorts only inside its drop pipeline; + same contract as ``RegressionDiscontinuity``). Per-name values + are identical either way on full-rank covariates. + + Returns + ------- + RDPlotResult + """ + if covariates is not None: + if isinstance(covariates, str): + # A bare string would iterate characters; fail closed. + raise ValueError(f"covariates must be a list of column names; got {covariates!r}.") + # Materialize before validating (a generator would be consumed + # by the checks below and silently collapse to empty). + covariates = list(covariates) + if not covariates: + raise ValueError("covariates must be a non-empty list of column names or None.") + if not all(isinstance(name, str) for name in covariates): + raise ValueError(f"covariates must be a list of column names; got {covariates!r}.") + # Duplicate names corrupt the name-keyed coefficient dict and + # the index-based column lookups (same contract as the + # estimator's validate_covariate_names). + validate_covariate_names( + covariates, + [outcome_col, running_col], + estimator="RDPlot", + ) + for col in [outcome_col, running_col] + list(covariates or []): + if col not in data.columns: + raise ValueError(f"Column '{col}' not found in data.") + + c = float(self.cutoff) + y_all = np.asarray(pd.to_numeric(data[outcome_col], errors="coerce"), dtype=np.float64) + x_all = np.asarray(pd.to_numeric(data[running_col], errors="coerce"), dtype=np.float64) + ok = np.isfinite(x_all) & np.isfinite(y_all) + z_all: Optional[np.ndarray] = None + if covariates is not None: + z_all = np.column_stack( + [ + np.asarray(pd.to_numeric(data[col], errors="coerce"), dtype=np.float64) + for col in covariates + ] + ) + ok &= np.all(np.isfinite(z_all), axis=1) + n_dropped = int(x_all.shape[0] - np.sum(ok)) + if n_dropped > 0: + # Deviation from R (which drops silently via complete.cases); + # same warning contract as RegressionDiscontinuity.fit(). + dropped_cols = f"{outcome_col!r}/{running_col!r}" + if covariates is not None: + dropped_cols += "/covariates" + warnings.warn( + f"Dropping {n_dropped} row(s) with missing or non-numeric " + f"values in {dropped_cols}.", + UserWarning, + stacklevel=2, + ) + x, y = x_all[ok], y_all[ok] + z = z_all[ok] if z_all is not None else None + n = x.size + if n == 0: + raise ValueError("No complete observations after dropping missing values.") + + # --- Covariate redundancy pipeline (rdplot.R:144-159; identical to + # the estimator's: stable name-length sort decides which of a + # collinear set survives, QR check on x-sorted rows) --- + model_covariates: Optional[List[str]] = None + covariates_dropped: Optional[List[str]] = None + if covariates is not None and z is not None and self.covs_drop: + model_covariates = sorted(list(covariates), key=len) + z = np.column_stack([z[:, covariates.index(name)] for name in model_covariates]) + keep_idx, rank = covs_drop_fun(z[np.argsort(x, kind="stable")]) + if rank == 0: + raise ValueError( + "All covariates are numerically zero (rank-0 covariate " + "matrix); remove the covariates instead." + ) + covariates_dropped = [] + if rank < len(model_covariates): + covariates_dropped = [ + name + for i, name in enumerate(model_covariates) + if i not in set(keep_idx.tolist()) + ] + warnings.warn( + "Multicollinearity detected in covariates: dropped " + f"redundant column(s) {covariates_dropped} " + "(covs_drop=True; set covs_drop=False for a strict " + "error instead).", + UserWarning, + stacklevel=2, + ) + model_covariates = [ + name for i, name in enumerate(model_covariates) if i in set(keep_idx.tolist()) + ] + z = z[:, keep_idx] + elif covariates is not None: + model_covariates = list(covariates) + + x_min, x_max = float(np.min(x)), float(np.max(x)) + if self.support is not None: # support only WIDENS (rdplot.R:62-67) + if float(self.support[0]) < x_min: + x_min = float(self.support[0]) + if float(self.support[1]) > x_max: + x_max = float(self.support[1]) + if c <= x_min or c >= x_max: + raise ValueError( + f"cutoff must lie strictly inside the range of '{running_col}' " + f"([{x_min}, {x_max}]); got {c}." + ) + if n < 20: + # R stops outright (rdplot.R:190-193) - no small-sample fallback. + raise ValueError(f"Not enough observations to perform bin calculations (n={n} < 20).") + + ind_l, ind_r = x < c, x >= c + x_l, y_l, x_r, y_r = x[ind_l], y[ind_l], x[ind_r], y[ind_r] + n_l, n_r = int(x_l.size), int(x_r.size) + # Defensive guard beyond R: with n >= 20 one side can still be + # near-empty, where R degenerates opaquely (NA variance poisoning an + # `if`). Fail with the side named instead. + if n_l < 2 or n_r < 2: + side = "below" if n_l < 2 else "above" + raise ValueError( + f"Need at least 2 observations on each side of the cutoff; " + f"found {min(n_l, n_r)} {side} cutoff={c}." + ) + # A side whose running variable has a SINGLE support point cannot be + # partitioned: every spacing is zero, the variance components + # collapse to 0, J becomes Inf, and the jump grid divides by zero - + # R crashes opaquely inside seq() there. Defensive clear error. + if np.unique(x_l).size < 2 or np.unique(x_r).size < 2: + side = "below" if np.unique(x_l).size < 2 else "above" + raise ValueError( + f"The running variable has a single distinct value {side} " + f"cutoff={c}; at least 2 distinct values per side are " + "required to construct bins." + ) + range_l, range_r = c - x_min, x_max - c + + scale_l, scale_r = _resolve_pair(self.scale, 1.0) + h_l, h_r = _resolve_pair(self.h, 0.0) + if self.h is None: + h_l, h_r = range_l, range_r + + ci_requested = self.ci is not None + ci_level = float(self.ci) if self.ci is not None else 95.0 + + binselect = self.binselect + # --- Mass points (rdplot.R:116-137) --- + if self.masspoints in ("check", "adjust"): + m_l = np.unique(x_l).size + m_r = np.unique(x_r).size + if (1 - m_l / n_l) >= 0.2 or (1 - m_r / n_r) >= 0.2: + hint = ( + " Try masspoints='adjust'." + if self.masspoints == "check" + else f" binselect switched to '{_MASSPOINTS_REMAP.get(binselect, binselect)}'" + " (polynomial-regression variance estimators; spacings" + " degenerate under ties)." + ) + warnings.warn( + "Mass points detected in the running variable." + hint, + UserWarning, + stacklevel=2, + ) + if self.masspoints == "adjust": + binselect = _MASSPOINTS_REMAP.get(binselect, binselect) + + # Defensive warning beyond R (which runs spacings selectors on any + # outcome): the spacings/concomitant selectors assume a CONTINUOUSLY + # distributed outcome (CCT 2015 Theorems 3-4; Remarks 1-2 route + # noncontinuous outcomes to the series estimators). Heavy outcome + # ties - the masspoints criterion applied to y - invalidate them; + # warn and recommend the *pr sibling. No auto-remap: R does not + # remap, and the numbers must stay golden-comparable. + if _BINSELECT_INFO[binselect][1] == "hat": + ties_l = 1 - np.unique(y_l).size / n_l + ties_r = 1 - np.unique(y_r).size / n_r + if ties_l >= 0.2 or ties_r >= 0.2: + warnings.warn( + "Heavy ties detected in the outcome variable (possibly " + "discrete/binary); the spacings-based bin selectors " + "assume a continuously distributed outcome (CCT 2015 " + "Theorems 3-4). Consider binselect=" + f"'{_MASSPOINTS_REMAP.get(binselect, binselect)}' " + "(polynomial-regression variance estimators, valid for " + "noncontinuous outcomes).", + UserWarning, + stacklevel=2, + ) + + # ------------------------------------------------------------------ + # Global polynomial curve of order p (rdplot.R:206-270) + # ------------------------------------------------------------------ + p = int(self.p) + W_h_l = rdrobust_kweight(x_l, c, h_l, self.kernel) + W_h_r = rdrobust_kweight(x_r, c, h_r, self.kernel) + n_h_l = int(np.sum(W_h_l > 0)) + n_h_r = int(np.sum(W_h_r > 0)) + if n_h_l == 0 or n_h_r == 0: + # Defensive guard beyond R: a manual h smaller than the nearest + # observation's distance zeroes every kernel weight, and R + # silently fits a zero-weight design (ginv of a zero Gram), + # returning an all-zero "curve" that used no data. + side = "below" if n_h_l == 0 else "above" + raise ValueError( + f"h={self.h!r} leaves zero effective observations {side} the " + f"cutoff for the global polynomial fit; increase h (default: " + "each side's full range)." + ) + + R_p_l = _pow_outer(x_l - c, p) + R_p_r = _pow_outer(x_r - c, p) + invG_p_l = qrXXinv(np.sqrt(W_h_l)[:, None] * R_p_l) + invG_p_r = qrXXinv(np.sqrt(W_h_r)[:, None] * R_p_r) + + gamma_p: Optional[np.ndarray] = None + covariate_coefficients: Optional[Dict[str, float]] = None + if z is None: + gamma_p1_l = invG_p_l @ (R_p_l * W_h_l[:, None]).T @ y_l + gamma_p1_r = invG_p_r @ (R_p_r * W_h_r[:, None]).T @ y_r + else: + z_l, z_r = z[ind_l], z[ind_r] + D_l = np.column_stack([y_l, z_l]) + D_r = np.column_stack([y_r, z_r]) + U_p_l = (R_p_l * W_h_l[:, None]).T @ D_l + U_p_r = (R_p_r * W_h_r[:, None]).T @ D_r + beta_p_l = invG_p_l @ U_p_l + beta_p_r = invG_p_r @ U_p_r + ZWD_p_l = (z_l * W_h_l[:, None]).T @ D_l + ZWD_p_r = (z_r * W_h_r[:, None]).T @ D_r + colsZ = slice(1, D_l.shape[1]) + UiGU_l = U_p_l[:, colsZ].T @ (invG_p_l @ U_p_l) + UiGU_r = U_p_r[:, colsZ].T @ (invG_p_r @ U_p_r) + ZWZ_p = (ZWD_p_l[:, colsZ] - UiGU_l[:, colsZ]) + (ZWD_p_r[:, colsZ] - UiGU_r[:, colsZ]) + ZWY_p = (ZWD_p_l[:, :1] - UiGU_l[:, :1]) + (ZWD_p_r[:, :1] - UiGU_r[:, :1]) + diag_pre = np.diag(ZWD_p_l[:, colsZ]) + np.diag(ZWD_p_r[:, colsZ]) + gamma_p, covs_excluded, covs_set_degenerate = _covs_gamma( + ZWZ_p, ZWY_p, diag_pre, bool(self.covs_drop) + ) + assert model_covariates is not None + if covs_excluded.any() or covs_set_degenerate: + # Same guarded Deviation-from-R as the estimator: R's + # ginv(tol=1e-20) inverts noise singular values on exactly- + # degenerate partialled systems, making gamma platform-noise. + parts = [] + if covs_excluded.any(): + excluded_names = [model_covariates[i] for i in np.flatnonzero(covs_excluded)] + parts.append( + f"covariate(s) {excluded_names} are numerically " + "collinear with the polynomial design (e.g. constant) " + "and were excluded from the adjustment" + ) + if covs_set_degenerate: + parts.append( + "the covariate set is numerically rank-deficient " + "after partialling; a stabilized pseudo-inverse cut " + "was used - consider dropping a reference category" + ) + warnings.warn( + "Degenerate covariate adjustment: " + "; ".join(parts) + ".", + UserWarning, + stacklevel=2, + ) + s_Y = np.concatenate([[1.0], -gamma_p[:, 0]]) + gamma_p1_l = beta_p_l @ s_Y + gamma_p1_r = beta_p_r @ s_Y + covariate_coefficients = { + name: float(gamma_p[i, 0]) for i, name in enumerate(model_covariates) + } + + nplot = 500 + x_plot_l = np.linspace(c - h_l, c, nplot) + x_plot_r = np.linspace(c, c + h_r, nplot) + y_hat_l = _pow_outer(x_plot_l - c, p) @ gamma_p1_l + y_hat_r = _pow_outer(x_plot_r - c, p) @ gamma_p1_r + if z is not None and gamma_p is not None: + # covs_eval="mean" (rdplot.R:266-270): a single scalar shift. + gammaZ = float(np.mean(z, axis=0) @ gamma_p[:, 0]) + y_hat_l = y_hat_l + gammaZ + y_hat_r = y_hat_r + gammaZ + + # ------------------------------------------------------------------ + # Bin-count selectors (order-k global fits; rdplot.R:276-388) + # ------------------------------------------------------------------ + # k=4 with a 4 -> 3 -> 2 fallback ladder that fires ONLY when + # qrXXinv itself raises: R's qrXXinv absorbs finite singular Grams + # via an internal ginv fallback at UNCHANGED k, so the try-error + # ladder triggers on non-finite Grams (raw x**8 overflow) or SVD + # failure. scipy raises ValueError on non-finite input, numpy + # LinAlgError on SVD failure - catch both. + def _selector_grams() -> Tuple[int, np.ndarray, np.ndarray, np.ndarray, np.ndarray]: + for k_try in (4, 3, 2): + rk_l_try = _pow_outer(x_l, k_try) + rk_r_try = _pow_outer(x_r, k_try) + try: + return ( + k_try, + rk_l_try, + rk_r_try, + qrXXinv(rk_l_try), + qrXXinv(rk_r_try), + ) + except (ValueError, np.linalg.LinAlgError): + if k_try == 2: + raise + raise AssertionError("unreachable") # pragma: no cover + + k, rk_l, rk_r, invG_k_l, invG_k_r = _selector_grams() + gamma_k1_l = invG_k_l @ rk_l.T @ y_l + gamma_k2_l = invG_k_l @ rk_l.T @ y_l**2 + gamma_k1_r = invG_k_r @ rk_r.T @ y_r + gamma_k2_r = invG_k_r @ rk_r.T @ y_r**2 + + ord_l = np.argsort(x_l, kind="stable") + ord_r = np.argsort(x_r, kind="stable") + x_i_l, y_i_l = x_l[ord_l], y_l[ord_l] + x_i_r, y_i_r = x_r[ord_r], y_r[ord_r] + dxi_l, dyi_l = np.diff(x_i_l), np.diff(y_i_l) + dxi_r, dyi_r = np.diff(x_i_r), np.diff(y_i_r) + x_bar_i_l = (x_i_l[1:] + x_i_l[:-1]) / 2 + x_bar_i_r = (x_i_r[1:] + x_i_r[:-1]) / 2 + + rk_i_l = _pow_outer(x_bar_i_l, k) + rk_i_r = _pow_outer(x_bar_i_r, k) + j_arange = np.arange(1, k + 1) + drk_l = _pow_outer(x_l, k - 1) * j_arange + drk_r = _pow_outer(x_r, k - 1) * j_arange + drk_i_l = _pow_outer(x_bar_i_l, k - 1) * j_arange + drk_i_r = _pow_outer(x_bar_i_r, k - 1) * j_arange + + var_y_l = float(np.var(y_l, ddof=1)) + var_y_r = float(np.var(y_r, ddof=1)) + + mu0_i_l = rk_i_l @ gamma_k1_l + mu0_i_r = rk_i_r @ gamma_k1_r + mu2_i_l = rk_i_l @ gamma_k2_l + mu2_i_r = rk_i_r @ gamma_k2_r + mu0_l = rk_l @ gamma_k1_l + mu0_r = rk_r @ gamma_k1_r + mu2_l = rk_l @ gamma_k2_l + mu2_r = rk_r @ gamma_k2_r + mu1_l = drk_l @ gamma_k1_l[1:] + mu1_r = drk_r @ gamma_k1_r[1:] + mu1_i_l = drk_i_l @ gamma_k1_l[1:] + mu1_i_r = drk_i_r @ gamma_k1_r[1:] + + # Negative variance-function values are floored to the side's sample + # variance (rdplot.R:356-371; an R-source fact absent from the paper). + s2bar_l = mu2_i_l - mu0_i_l**2 + s2bar_r = mu2_i_r - mu0_i_r**2 + s2bar_l[s2bar_l < 0] = var_y_l + s2bar_r[s2bar_r < 0] = var_y_r + s2_l = mu2_l - mu0_l**2 + s2_r = mu2_r - mu0_r**2 + s2_l[s2_l < 0] = var_y_l + s2_r[s2_r < 0] = var_y_r + + B_es = ( + (range_l**2 / (12 * n)) * float(np.sum(mu1_l**2)), + (range_r**2 / (12 * n)) * float(np.sum(mu1_r**2)), + ) + V_es_hat = ( + (0.5 / range_l) * float(np.sum(dxi_l * dyi_l**2)), + (0.5 / range_r) * float(np.sum(dxi_r * dyi_r**2)), + ) + V_es_chk = ( + (1 / range_l) * float(np.sum(dxi_l * s2bar_l)), + (1 / range_r) * float(np.sum(dxi_r * s2bar_r)), + ) + B_qs = ( + (n_l**2 / (24 * n)) * float(np.sum(dxi_l**2 * mu1_i_l**2)), + (n_r**2 / (24 * n)) * float(np.sum(dxi_r**2 * mu1_i_r**2)), + ) + V_qs_hat = ( + (1 / (2 * n_l)) * float(np.sum(dyi_l**2)), + (1 / (2 * n_r)) * float(np.sum(dyi_r**2)), + ) + V_qs_chk = ( + (1 / n_l) * float(np.sum(s2_l)), + (1 / n_r) * float(np.sum(s2_r)), + ) + + # numpy scalar arithmetic so a zero-variance side propagates Inf/NaN + # through the selectors exactly like R (0/0 -> NaN, x/0 -> Inf, no + # exception); the var==0 rescue below then pins J to 1 as R does. + def _J_imse(B: _PairF, V: _PairF) -> _PairF: + # J.fun (functions.R:588) + with np.errstate(divide="ignore", invalid="ignore"): + return ( + float(np.ceil((((2 * np.float64(B[0])) / V[0]) * n) ** (1 / 3))), + float(np.ceil((((2 * np.float64(B[1])) / V[1]) * n) ** (1 / 3))), + ) + + def _J_mv(V: _PairF) -> _PairF: + with np.errstate(divide="ignore", invalid="ignore"): + return ( + float(np.ceil((np.float64(var_y_l) / V[0]) * n / math.log(n) ** 2)), + float(np.ceil((np.float64(var_y_r) / V[1]) * n / math.log(n) ** 2)), + ) + + meth, family, target, binselect_type = _BINSELECT_INFO[binselect] + V_pair = { + ("es", "hat"): V_es_hat, + ("es", "chk"): V_es_chk, + ("qs", "hat"): V_qs_hat, + ("qs", "chk"): V_qs_chk, + }[(meth, family)] + B_pair = B_es if meth == "es" else B_qs + J_IMSE = _J_imse(B_pair, V_pair) + J_MV = _J_mv(V_pair) + J_star_orig = J_IMSE if target == "imse" else J_MV + + # --- J resolution (rdplot.R:449-469) --- + # Deviation from R: for a fractional scale*J product, R 4.0.0 + # propagates the unrounded value into 1:J / vector-indexing + # arithmetic and CRASHES assembling vars_bins (verified live: + # "arguments imply differing number of rows"); a length-2 scale dies + # even earlier on R >= 4.2 (vectorized-if error at rdplot.R:178). + # CCT 2015 Equation 2 places the ceiling over the RESCALED product + # (J = ceil(omega * (2B/V n)^(1/3))), so the paper-faithful integer + # partition is used here instead of replicating an accident. + # np.ceil keeps a NaN/Inf selector value (zero-variance side, rescued + # just below) flowing instead of raising like math.ceil would. The + # max(1, .) clamp keeps an ultra-small positive scale (product below + # the 1e-12 integer-product epsilon) from ceiling to zero bins - the + # Eq 2 partition is at least one bin (NaN passes through max() + # untouched for the variance rescue). + with np.errstate(invalid="ignore"): + J_star_l: float = float(np.maximum(1.0, np.ceil(scale_l * J_star_orig[0] - 1e-12))) + J_star_r: float = float(np.maximum(1.0, np.ceil(scale_r * J_star_orig[1] - 1e-12))) + if self.nbins is not None: + nb_l, nb_r = _resolve_pair(self.nbins, 0) + J_star_l, J_star_r = float(nb_l), float(nb_r) + # R keeps this label even when binselect selected quantile + # spacing - the BINS below still follow `meth` (label-only quirk). + binselect_type = "manually evenly spaced" + if var_y_l == 0: + J_star_l = 1.0 + warnings.warn( + "Not enough variability in the outcome variable below the threshold", + UserWarning, + stacklevel=2, + ) + if var_y_r == 0: + J_star_r = 1.0 + warnings.warn( + "Not enough variability in the outcome variable above the threshold", + UserWarning, + stacklevel=2, + ) + rscale = (J_star_l / J_IMSE[0], J_star_r / J_IMSE[1]) + + # --- Bin construction (rdplot.R:471-508) --- + jump_l = range_l / J_star_l + jump_r = range_r / J_star_r + if meth == "es": + jumps_l = _r_seq_by(x_min, c, jump_l) + jumps_r = _r_seq_by(c, x_max, jump_r) + else: + probs_l = _r_seq_by(0.0, 1.0, 1 / J_star_l) + probs_r = _r_seq_by(0.0, 1.0, 1 / J_star_r) + # R type-7 quantile == numpy's default linear interpolation (the + # default has been type-7 in every numpy release; the explicit + # method="linear" kwarg only exists from numpy 1.22 and the + # library floor is 1.20, so the default is relied on and the + # type-7 convention is locked by a hand-computed test). + jumps_l = np.quantile(x_l, probs_l) + jumps_r = np.quantile(x_r, probs_r) + + bin_x_l = _find_interval(x_l, jumps_l) - J_star_l - 1 + bin_x_r = _find_interval(x_r, jumps_r).astype(float) + + # Number of full bins actually delimited by the jump grid (1:J with + # R's fractional truncation). + t_l = int(J_star_l) + t_r = int(J_star_r) + bin_length_l = jumps_l[1 : t_l + 1] - jumps_l[:t_l] + bin_length_r = jumps_r[1 : t_r + 1] - jumps_r[:t_r] + bin_avg = (float(np.mean(bin_length_l)), float(np.mean(bin_length_r))) + bin_med = (float(np.median(bin_length_l)), float(np.median(bin_length_r))) + + # Covariate-adjusted bin means (rdplot.R:492-499): per-side fitted + # values of y ~ 1 + z + bin dummies, then bin means of the fits. + # Fitted values are the projection onto the column space, so lstsq's + # min-norm solution reproduces R's pivoted-QR lm() fits exactly even + # under rank deficiency (this is a projection for display, not an + # inference path - solve_ols's rank guards are not wanted here). + y_for_bins_l, y_for_bins_r = y_l, y_r + if z is not None: + for side_bins, side_z, side_y, setter in ( + (bin_x_l, z[ind_l], y_l, "l"), + (bin_x_r, z[ind_r], y_r, "r"), + ): + levels, codes = np.unique(side_bins, return_inverse=True) + dummies = np.zeros((side_y.size, levels.size - 1)) + nonref = codes >= 1 + dummies[np.arange(side_y.size)[nonref], codes[nonref] - 1] = 1.0 + X = np.column_stack([np.ones(side_y.size), side_z, dummies]) + coefs, *_ = np.linalg.lstsq(X, side_y, rcond=None) + fitted = X @ coefs + if setter == "l": + y_for_bins_l = fitted + else: + y_for_bins_r = fitted + + def _side_rows( + bins: np.ndarray, + xs: np.ndarray, + ys_mean: np.ndarray, + ys_sd: np.ndarray, + jumps: np.ndarray, + t_full: int, + offset: float, + left: bool, + ) -> List[Dict[str, float]]: + present = np.sort(np.unique(bins)) + # R-quirk (rdplot.R:503-508 vs 551-552): mean_bin is indexed by + # the TRUE grid slot (b + J + 1 on the left, b on the right), + # but min_bin/max_bin on the LEFT side are indexed by + # `rev(-bins)` - the ASCENDING-sorted |bin| ids, i.e. the + # occupied slots REFLECTED around the side's center. Identical + # when no left bin is empty; with empty left bins R's own + # min/max edges disagree with mean_bin (verified live: 10 of 20 + # senate/esmvpr left rows have mean_bin != (min+max)/2). + # Replicated exactly; documented in REGISTRY. + edge_slots = np.sort(np.abs(present)) if left else present + rows = [] + for i, b in enumerate(present): + m = bins == b + N_bin = int(np.sum(m)) + mean_y = float(np.mean(ys_mean[m])) + sd = float(np.std(ys_sd[m], ddof=1)) if N_bin > 1 else 0.0 + se = sd / math.sqrt(N_bin) + quant = -float(stats.t.ppf((1 - ci_level / 100) / 2, max(N_bin - 1, 1))) + # 1-based slots; out-of-range (grid undershoot overflow bin) + # gives NA in R and NaN here. int() mirrors R's truncating + # numeric indexing. + true_slot = int(b + offset) + edge_slot = int(edge_slots[i]) + if 1 <= true_slot <= t_full: + mean_bin = float(jumps[true_slot - 1] + jumps[true_slot]) / 2 + else: + mean_bin = float("nan") + if 1 <= edge_slot <= t_full: + min_bin = float(jumps[edge_slot - 1]) + max_bin = float(jumps[edge_slot]) + else: + min_bin = float("nan") + max_bin = float("nan") + rows.append( + { + "rdplot_mean_bin": mean_bin, + "rdplot_mean_x": float(np.mean(xs[m])), + "rdplot_mean_y": mean_y, + "rdplot_min_bin": min_bin, + "rdplot_max_bin": max_bin, + "rdplot_se_y": se, + "rdplot_N": N_bin, + "rdplot_ci_l": mean_y - quant * se, + "rdplot_ci_r": mean_y + quant * se, + } + ) + return rows + + rows = _side_rows(bin_x_l, x_l, y_for_bins_l, y_l, jumps_l, t_l, J_star_l + 1, True) + rows += _side_rows(bin_x_r, x_r, y_for_bins_r, y_r, jumps_r, t_r, 0.0, False) + vars_bins = pd.DataFrame(rows) + vars_poly = pd.DataFrame( + { + "rdplot_x": np.concatenate([x_plot_l, x_plot_r]), + "rdplot_y": np.concatenate([y_hat_l, y_hat_r]), + } + ) + coef = pd.DataFrame( + {"Left": np.asarray(gamma_p1_l).ravel(), "Right": np.asarray(gamma_p1_r).ravel()}, + index=pd.Index(range(p + 1), name="power"), + ) + + return RDPlotResult( + coef=coef, + vars_bins=vars_bins, + vars_poly=vars_poly, + J=(J_star_l, J_star_r), + J_IMSE=J_IMSE, + J_MV=J_MV, + scale=(scale_l, scale_r), + rscale=rscale, + bin_avg=bin_avg, + bin_med=bin_med, + p=p, + cutoff=c, + h=(h_l, h_r), + N=(n_l, n_r), + N_h=(n_h_l, n_h_r), + binselect=binselect_type, + kernel_type=_KERNEL_TYPE_LABEL[_normalize_kernel(self.kernel)], + ci_level=ci_level, + ci_requested=ci_requested, + covariate_coefficients=covariate_coefficients, + covariates_dropped=covariates_dropped, + ) diff --git a/docs/api/regression_discontinuity.rst b/docs/api/regression_discontinuity.rst index 398e5eff0..362cc8a4d 100644 --- a/docs/api/regression_discontinuity.rst +++ b/docs/api/regression_discontinuity.rst @@ -45,10 +45,20 @@ naming them (``covs_drop=True``, R's default). **Scope of this release.** Sharp, fuzzy, and covariate-adjusted designs with the nearest-neighbor variance estimator (rdrobust's - default). Cluster-robust variance, weights, kink estimands, - weak-IV-robust fuzzy inference, a packaged covariate-balance helper, - and the rdplot/density-test diagnostics are documented follow-ups - - see the methodology registry for the full deviations and seams list. + default), plus the ``RDPlot`` diagnostic below. Cluster-robust + variance, weights, kink estimands, weak-IV-robust fuzzy inference, a + packaged covariate-balance helper, and the density-test diagnostic + are documented follow-ups - see the methodology registry for the full + deviations and seams list. + +**RD plots** (``RDPlot``; Calonico, Cattaneo & Titiunik 2015, parity with +R's ``rdplot()``): the standard exploratory companion - per-side global +polynomial fits plus binned local means with a data-driven number of bins +(all 8 ``binselect`` selectors: evenly/quantile spaced, IMSE-optimal or +mimicking-variance, spacings or polynomial-regression variance estimators), +implied-scale/WIMSE-weight reporting, optional covariate adjustment, and an +optional matplotlib rendering via ``RDPlotResult.plot()`` (matplotlib is +not a dependency; the numbers are the parity surface). RegressionDiscontinuity ----------------------- @@ -67,3 +77,21 @@ RegressionDiscontinuityResults :members: :undoc-members: :show-inheritance: + +RDPlot +------ + +.. autoclass:: diff_diff.RDPlot + :no-index: + :members: + :undoc-members: + :show-inheritance: + +RDPlotResult +------------ + +.. autoclass:: diff_diff.RDPlotResult + :no-index: + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/doc-deps.yaml b/docs/doc-deps.yaml index 68e2119b9..36554e505 100644 --- a/docs/doc-deps.yaml +++ b/docs/doc-deps.yaml @@ -423,6 +423,26 @@ sources: - path: docs/choosing_estimator.rst section: "SE methodology + Survey Design Support tables" type: user_guide + diff_diff/rdplot.py: + drift_risk: medium + docs: + - path: docs/methodology/REGISTRY.md + section: "RDPlot" + type: methodology + note: "rdplot() parity pin (same CRAN 4.0.0 tarball as the estimator port), the deviation labels (fractional/pair scale ceiling, covs message), and the replicated left-edge slot-reflection quirk live here; any change must re-run the golden suite (tests/test_rdplot.py) against benchmarks/data/rdplot_golden.json." + - path: docs/api/regression_discontinuity.rst + type: api_reference + - path: README.md + section: "Diagnostics & Sensitivity (one-line catalog entry)" + type: user_guide + - path: docs/references.rst + type: user_guide + - path: diff_diff/guides/llms.txt + section: "Diagnostics and Sensitivity Analysis" + type: user_guide + - path: diff_diff/guides/llms-full.txt + section: "RDPlot" + type: user_guide diff_diff/_rdrobust_port.py: drift_risk: high docs: diff --git a/docs/methodology/REGISTRY.md b/docs/methodology/REGISTRY.md index 305aafcf7..7be8fe6c2 100644 --- a/docs/methodology/REGISTRY.md +++ b/docs/methodology/REGISTRY.md @@ -32,6 +32,7 @@ This document provides the academic foundations and key implementation requireme - [SpilloverDiD](#spilloverdid) 4. [Regression Discontinuity](#regression-discontinuity) - [RegressionDiscontinuity](#regressiondiscontinuity) + - [RDPlot](#rdplot) 5. [Diagnostics and Sensitivity](#diagnostics-and-sensitivity) - [PlaceboTests](#placebotests) - [BaconDecomposition](#bacondecomposition) @@ -3871,9 +3872,10 @@ per-side and comb chains. ships; `hc0`-`hc3` and cluster modes raise `NotImplementedError`. Weights, kink estimands (`deriv`), `scalepar`, `stdvars`, per-side manual bandwidths, weak-IV-robust fuzzy inference - (Feir-Lemieux-Marmer), the rdplot/density diagnostics, and a packaged - covariate-balance helper are documented follow-ups; cluster/weights - are not parameters at all. The port's `deriv` machinery is + (Feir-Lemieux-Marmer), the density/manipulation-test diagnostic, and a + packaged covariate-balance helper are documented follow-ups (the + rdplot diagnostic ships as `RDPlot` - see its section below); + cluster/weights are not parameters at all. The port's `deriv` machinery is golden-covered for `deriv in {0, 1}` only. - **Note (p/q surface, R-exact):** public `p`/`q` validation mirrors rdrobust.R:47-57 exactly - integers in 0:20 with `q > p`; `p=0` is R's @@ -3947,13 +3949,159 @@ CI-shrinkage anchors). `docs/methodology/papers/calonico-cattaneo-titiunik-2014-review.md` (CCT 2014, Econometrica - robust bias-corrected RD inference), `calonico-cattaneo-titiunik-2015-review.md` (JASA - optimal data-driven RD -plots; bin-count selector formulas for the planned rdplot diagnostic), +plots; bin-count selector formulas, implemented as `RDPlot`), `calonico-cattaneo-farrell-titiunik-2017-review.md` (Stata Journal - the rdrobust software reference this port parity-targets), `calonico-cattaneo-farrell-2018-review.md` (JASA - CER-optimal bandwidths), `calonico-cattaneo-farrell-titiunik-2019-review.md` (REStat - covariate adjustment, implemented). +## RDPlot + +**Primary source:** Calonico, S., Cattaneo, M. D., & Titiunik, R. (2015). +Optimal Data-Driven Regression Discontinuity Plots. *JASA*, 110(512), +1753-1769. https://doi.org/10.1080/01621459.2015.1017578 (+ Supplemental +Appendix). Full formula extraction in +`docs/methodology/papers/calonico-cattaneo-titiunik-2015-review.md`. + +**What it is:** the exploratory/graphical companion to +`RegressionDiscontinuity` (`diff_diff/rdplot.py`) - NOT a treatment-effect +estimator. Two ingredients (CCT 2015 Section 2.1): per-side global order-`p` +polynomial fits (default `p=4`, uniform kernel over each side's full range - +R's rdplot defaults, deliberately different from estimation), and binned +local sample means of the outcome over a per-side partition with a +data-driven number of bins. Parity target: `rdplot()` in CRAN rdrobust 4.0.0 +(same tarball as the estimator port), golden-tested end-to-end +(`benchmarks/data/rdplot_golden.json`, 24 configs incl. the vendored Senate +data; the supplement's Figures SA-1/SA-2 selector outputs on that dataset - +esmv J=(15,35), es (8,9), qs (21,16), qsmv (28,49) - are asserted as +JSON-independent paper anchors in `tests/test_rdplot.py`). + +**Bin selectors (rdplot.R:276-447):** all 8 `binselect` values = +{ES, QS partition} x {IMSE-optimal, mimicking-variance target} x +{spacings (`hat`), polynomial-regression (`chk`, the `*pr` names) variance +estimator}. The bias estimator is SHARED between the spacings and `pr` +families (main-text Eq 8/10 raw-observation form - the source read resolved +the paper review's Gap #3); only the variance estimator differs. Selector +fits use `k=4` raw-power Vandermonde regressions of `y` and `y^2` +(unweighted, full samples, NOT centered at the cutoff) with a `k=4 -> 3 -> 2` +fallback ladder that fires ONLY when `qrXXinv` itself raises (non-finite +Gram from `x^8` overflow / SVD failure); finite singular Grams are absorbed +inside `qrXXinv`'s pseudo-inverse fallback at unchanged `k`, mirroring R's +internal `ginv` fallback exactly. `J = ceil(((2B/V) n)^(1/3))` (IMSE) or +`ceil((var(y_side)/V) n / log(n)^2)` (mimicking variance); the result +reports the selected pair alongside `J_IMSE`, `J_MV`, the implied scale +`rscale = J/J_IMSE`, and the Supplement-S.1 WIMSE weights +`(1/(1+rscale^3), rscale^3/(1+rscale^3))`, matching R's `summary.rdplot`. + +**Edge cases (R-verified):** +- **`n < 20` hard error** (rdplot.R:190-193) - unlike the estimator's + small-sample bandwidth fallback, rdplot refuses outright; replicated. +- **Mass points** (rdplot.R:116-137): >= 20% duplicate running values on + either side warns; `masspoints="adjust"` remaps a spacings `binselect` to + its `*pr` sibling (spacings degenerate under ties); `"check"` warns + without remapping; explicit `*pr` picks are untouched. +- **Zero outcome variance on a side**: R propagates `Inf`/`NaN` through the + selectors (`J_IMSE = Inf`) and rescues `J = 1` with a warning; replicated + including the `Inf` echo (numpy division semantics, no exception). +- **Negative variance-function values** in the `chk` estimators are floored + to the side's sample variance (rdplot.R:356-371; an R-source fact absent + from the paper). +- **Empty bins** are dropped from `vars_bins` (R's `tapply`), while + `bin_avg`/`bin_med` summarize ALL partition cells; per-bin CI columns are + ALWAYS computed (level 95 when `ci` unset - the flag only turns on plotted + error bars, matching R), and single-observation bins get `se = 0` via R's + `NA -> 0` sd fixup. +- **Note (left-edge slot reflection, R quirk replicated):** R indexes the + left side's `rdplot_min_bin`/`rdplot_max_bin` by `rev(-bins)` - the + ascending-sorted ABSOLUTE bin ids, i.e. the occupied slots reflected + around the side's center - while `rdplot_mean_bin` uses the true slots + (rdplot.R:503-508 vs 551-552). With any empty left-side bin the reported + left edges therefore disagree with `rdplot_mean_bin` (live-verified: 10 of + 20 left rows on the Senate `esmvpr` config have + `mean_bin != (min+max)/2` in R itself). Replicated exactly for parity; + `rdplot_mean_bin` is the reliable per-row bin identity. +- **Note (defensive guard):** `n >= 20` can still leave a side with < 2 + observations, where R degenerates opaquely (`NA` variance poisoning an + `if`); a clear `ValueError` naming the side is raised instead. +- **Note (defensive guard, zero-weight global fit):** a manual `h` smaller + than the nearest observation's distance zeroes every kernel weight; R + silently fits the zero-weight design (`ginv` of a zero Gram) and returns + an all-zero "curve" that used no data. A clear `ValueError` naming the + side is raised instead. +- **Note (defensive guard, single-support-point side):** a side whose + running variable has only ONE distinct value cannot be partitioned - + every spacing is zero, the variance components collapse, `J` becomes + `Inf`, and R crashes opaquely inside `seq()` on the zero jump. A clear + `ValueError` naming the side is raised instead (>= 2 distinct running + values per side required). +- **Note (missing rows warned; deviation from R):** rows with missing or + non-numeric outcome/running/covariate values are dropped WITH a + `UserWarning` reporting the count (R's `complete.cases` drops silently) - + the same contract as `RegressionDiscontinuity.fit()`. +- **Note (defensive warning, noncontinuous outcomes):** the + spacings/concomitant selectors assume a CONTINUOUSLY distributed outcome + (CCT 2015 Theorems 3-4; Remarks 1-2 route noncontinuous outcomes to the + series estimators), but R runs them on any `y` without comment. When a + spacings `binselect` is active and either side's outcome has >= 20% + duplicates (the masspoints criterion applied to `y` - e.g. a binary + first-stage plot), a `UserWarning` recommends the `*pr` sibling. No + auto-remap (R does not remap; numbers stay golden-comparable). +- **Note (QS cutpoints, R deviates from the paper):** CCT 2015 defines the + QS partition through the empirical generalized inverse + `Fhat^{-1}(y) = inf{x : Fhat(x) >= y}`, but R's rdplot builds the QS + jumps with `quantile()` at its default TYPE-7 (linearly interpolated) + quantiles (rdplot.R:477-478). The port follows R (numpy's default + `np.quantile` is the same type-7 estimator), so QS bin edges are + interpolated values rather than order statistics; golden-locked. +- **Deviation from R (fractional / pair scale):** R 4.0.0 crashes on a + fractional `scale * J` product ("arguments imply differing number of + rows", a vector-indexing accident) and rejects a length-2 `scale` outright + on R >= 4.2 (vectorized-`if` error at rdplot.R:178). CCT 2015 Equation 2 + places the ceiling over the rescaled product, so `RDPlot` applies that + paper-faithful ceiling and supports per-side scale pairs; integer-product + configs remain golden-identical to R. +- **Deviation from R (covs message):** R prints an unconditional `message()` + whenever `covs=` is used with the global fit; that caveat lives in the + `fit()` docstring instead of an unavoidable runtime warning. +- **`nbins` label quirk (replicated):** a manual `nbins` sets the + description string to "manually evenly spaced" even when `binselect` + chose quantile spacing - the bins themselves stay quantile-spaced + (rdplot.R:452-456; locked by test). + +**Covariate adjustment (`fit(..., covariates=...)`; rdplot.R:139-159, +223-270, 492-499):** the global fit partials covariates out with the SAME +pooled-across-sides partialled-normal-equations gamma as the estimator +(nchar-sorted `covs_drop` pipeline, degenerate-system guard, and +`covs_drop=False` strict-error contract all REUSED from the #691 machinery - +including its documented Deviation from R's noise-singular-value `ginv`); +the plotted curve adds the scalar `colMeans(Z) @ gamma` (R's +`covs_eval="mean"`, the only mode in v1), and bin means use per-side fitted +values of `y ~ 1 + Z + bin dummies` (R's `lm(y ~ z + factor(bin))`; +computed via least-squares projection, which reproduces R's pivoted-QR +fitted values exactly even under rank deficiency). Gammas are exposed +name-keyed as `covariate_coefficients` - nuisance coefficients, not causal +effects. + +**Scope seams (documented):** sampling weights and R's `subset=` are not +parameters (filter the DataFrame; consistent with the estimator's weights +seam); `covs_eval` variants beyond `"mean"`; rendering is an optional lazy +matplotlib `.plot(ax=...)` on the result (numbers are the parity surface - +no ggplot cosmetic parity is claimed). Manual `nbins`/`scale` accept any +positive value (R permissiveness); very large bin counts allocate +proportional jump grids and - with covariates - a dense per-side bin-dummy +matrix (performance seam, no cap imposed). `covariate_coefficients` key +order follows R's processing order: nchar-sorted under `covs_drop=True`, +user-given under `covs_drop=False` (R sorts only inside the drop pipeline; +identical per-name values on full-rank covariates - locked by test). + +**Regression tests:** `tests/test_rdplot.py` (24-config golden parity at +rtol=1e-9, paper anchors, quirk locks incl. the reflection quirk via the +esmvpr golden, masspoints remap/check, zero-variance rescue, fallback-ladder +overflow config with integer-only assertions plus a finite-singular +no-downgrade companion, deviation locks for fractional/pair scale, API +contracts, and the matplotlib ImportError path). + --- diff --git a/docs/references.rst b/docs/references.rst index 0cee64928..478b9c0c9 100644 --- a/docs/references.rst +++ b/docs/references.rst @@ -77,6 +77,10 @@ Nonparametric Bias-Corrected Inference Source of the bias-combined design matrix used by the in-house ``lprobust`` port that backs ``HeterogeneousAdoptionDiD`` Phase 1c (continuous-dose paths) for the bias-corrected weighted-robust SE. +- **Calonico, S., Cattaneo, M. D., & Titiunik, R. (2015).** "Optimal Data-Driven Regression Discontinuity Plots." *Journal of the American Statistical Association*, 110(512), 1753-1769. https://doi.org/10.1080/01621459.2015.1017578 + + Source of the :class:`diff_diff.RDPlot` diagnostic: IMSE-optimal and mimicking-variance bin-count selectors for evenly- and quantile-spaced RD plots (spacings and polynomial-regression implementations), and the WIMSE implied-weights map behind the reported scale factors. + - **Calonico, S., Cattaneo, M. D., & Farrell, M. H. (2018).** "On the Effect of Bias Estimation on Coverage Accuracy in Nonparametric Inference." *Journal of the American Statistical Association*, 113(522), 767-779. https://doi.org/10.1080/01621459.2017.1285776 - **Calonico, S., Cattaneo, M. D., & Farrell, M. H. (2019).** "nprobust: Nonparametric Kernel-Based Estimation and Robust Bias-Corrected Inference." *Journal of Statistical Software*, 91(8), 1-33. https://doi.org/10.18637/jss.v091.i08 diff --git a/tests/test_rdplot.py b/tests/test_rdplot.py new file mode 100644 index 000000000..d480b135d --- /dev/null +++ b/tests/test_rdplot.py @@ -0,0 +1,612 @@ +"""RDPlot tests: golden parity vs R rdrobust 4.0.0 rdplot(), R-quirk locks, +documented deviations, and API contracts. + +Golden fixtures come from benchmarks/R/generate_rdplot_golden.R +(benchmarks/data/rdplot_golden.json); tests skip if the JSON is absent. +""" + +import json +import warnings +from pathlib import Path + +import numpy as np +import pandas as pd +import pytest +from numpy.testing import assert_allclose + +from diff_diff import RDPlot, RDPlotResult + +GOLDEN_PATH = Path(__file__).resolve().parents[1] / "benchmarks" / "data" / "rdplot_golden.json" +SENATE_PATH = Path(__file__).resolve().parents[1] / "benchmarks" / "data" / "rdrobust_senate.csv" + +RTOL = 1e-9 + +VARS_BINS_COLS = { + "mean_bin": "rdplot_mean_bin", + "mean_x": "rdplot_mean_x", + "mean_y": "rdplot_mean_y", + "min_bin": "rdplot_min_bin", + "max_bin": "rdplot_max_bin", + "se_y": "rdplot_se_y", + "N": "rdplot_N", + "ci_l": "rdplot_ci_l", + "ci_r": "rdplot_ci_r", +} + + +@pytest.fixture(scope="module") +def golden(): + if not GOLDEN_PATH.exists(): + pytest.skip( + "rdplot golden file not generated; run " "Rscript benchmarks/R/generate_rdplot_golden.R" + ) + with open(GOLDEN_PATH) as f: + return json.load(f) + + +def _senate_frame(): + if not SENATE_PATH.exists(): + pytest.skip(f"Vendored Senate CSV not found at {SENATE_PATH}") + return pd.read_csv(SENATE_PATH) + + +def _frame(golden, dgp_name): + if dgp_name == "senate": + return _senate_frame().rename(columns={"vote": "y", "margin": "x"}) + entry = golden[dgp_name] + cols = {"x": entry["x"], "y": entry["y"]} + for name in ("zlong", "zb", "zdup"): + if name in entry: + cols[name] = entry[name] + return pd.DataFrame(cols) + + +def _maybe_pair(value): + if value is None: + return None + if isinstance(value, list): + return tuple(value) + return value + + +def _num_array(values): + """jsonlite writes R NA values as the string "NA"; map them to NaN.""" + return np.array([np.nan if v is None or v == "NA" else float(v) for v in values], dtype=float) + + +def _build(cfg): + a = cfg["args"] + return RDPlot( + cutoff=a["c"], + p=a["p"], + nbins=_maybe_pair(a["nbins"]), + binselect=a["binselect"], + scale=_maybe_pair(a["scale"]), + kernel=a["kernel"], + h=_maybe_pair(a["h"]), + support=_maybe_pair(a["support"]), + masspoints=a["masspoints"], + ci=a["ci"], + covs_drop=a["covs_drop"], + ) + + +def _fit(golden, dgp_name, cfg): + df = _frame(golden, dgp_name) + covariates = None + if cfg["args"]["covs_in"]: + names = cfg["args"]["covs_names"] + covariates = [names] if isinstance(names, str) else list(names) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + return _build(cfg).fit(df, "y", "x", covariates=covariates) + + +class TestRDPlotGoldenParity: + """End-to-end parity against R rdplot() on every stored config.""" + + def test_all_configs(self, golden): + n_checked = 0 + for dgp_name, entry in golden.items(): + if dgp_name == "metadata": + continue + for cfg_name, cfg in entry["configs"].items(): + r = _fit(golden, dgp_name, cfg) + label = f"{dgp_name}/{cfg_name}" + # Integer/string surfaces (asserted for EVERY config, + # including the BLAS-sensitive overflow-ladder DGP). + assert_allclose(r.J, cfg["J"], rtol=0, atol=0, err_msg=f"{label}: J") + assert_allclose(r.J_IMSE, cfg["J_IMSE"], rtol=0, atol=0, err_msg=f"{label}: J_IMSE") + assert_allclose(r.J_MV, cfg["J_MV"], rtol=0, atol=0, err_msg=f"{label}: J_MV") + assert r.N == tuple(cfg["N"]), f"{label}: N" + assert r.N_h == tuple(cfg["N_h"]), f"{label}: N_h" + assert r.binselect == cfg["binselect_type"], f"{label}: binselect_type" + assert r.kernel_type == cfg["kernel_type"], f"{label}: kernel_type" + assert len(r.vars_bins) == len(cfg["vars_bins"]["mean_y"]), f"{label}: rows" + if dgp_name == "dgp_ladder": + # Float surfaces of the post-ladder k=3 fit on |x|~1e39 + # data are dominated by catastrophic cancellation and + # are not comparable across BLAS builds; the integer + # outputs above pin the ladder behavior. + n_checked += 1 + continue + assert_allclose(r.scale, cfg["scale_out"], rtol=RTOL, err_msg=f"{label}: scale") + assert_allclose(r.rscale, cfg["rscale"], rtol=RTOL, err_msg=f"{label}: rscale") + assert_allclose(r.bin_avg, cfg["bin_avg"], rtol=RTOL, err_msg=f"{label}: bin_avg") + assert_allclose(r.bin_med, cfg["bin_med"], rtol=RTOL, err_msg=f"{label}: bin_med") + assert_allclose(r.h, cfg["h_out"], rtol=RTOL, err_msg=f"{label}: h") + # atol floors the high-order coefficients: on x in [-100, 100] + # the x^4 coefficient is O(1e-9) and BLAS accumulation order + # wobbles it at O(1e-18) absolute (assert-allclose convention). + assert_allclose( + r.coef["Left"], + cfg["coef_left"], + rtol=RTOL, + atol=1e-14, + err_msg=f"{label}: coef L", + ) + assert_allclose( + r.coef["Right"], + cfg["coef_right"], + rtol=RTOL, + atol=1e-14, + err_msg=f"{label}: coef R", + ) + for key, col in VARS_BINS_COLS.items(): + got = np.asarray(r.vars_bins[col], dtype=float) + want = _num_array(cfg["vars_bins"][key]) + assert_allclose( + got, + want, + rtol=RTOL, + atol=1e-12, + equal_nan=True, + err_msg=f"{label}: vars_bins.{col}", + ) + idx = np.asarray(cfg["vars_poly_idx"], dtype=int) - 1 # R 1-based + assert_allclose( + r.vars_poly["rdplot_x"].to_numpy()[idx], + cfg["vars_poly_x"], + rtol=RTOL, + err_msg=f"{label}: vars_poly x", + ) + # atol floors the curve where it CROSSES ZERO: the fitted + # values wobble at O(1e-12) absolute across BLAS builds + # (Accelerate vs OpenBLAS), which exceeds rtol=1e-9 only at + # near-zero crossings (observed: dgp_small, 1 of 42 points, + # 2.3e-12 abs = 1.03e-9 rel on the Linux pure-python leg). + assert_allclose( + r.vars_poly["rdplot_y"].to_numpy()[idx], + cfg["vars_poly_y"], + rtol=RTOL, + atol=1e-10, + err_msg=f"{label}: vars_poly y", + ) + if cfg["args"]["covs_in"]: + assert r.covariate_coefficients is not None, f"{label}: coef_covs" + assert_allclose( + list(r.covariate_coefficients.values()), + cfg["coef_covs"], + rtol=RTOL, + err_msg=f"{label}: coef_covs", + ) + n_checked += 1 + # 24 configurations; fail loudly if the golden shrinks. + assert n_checked == 24 + + def test_covariate_names_nchar_sorted(self, golden): + # R sorts covariate columns by name length (order(nchar)); the + # coefficient dict must be keyed in that order: zb before zlong. + cfg = golden["dgp_covs"]["configs"]["covs_default"] + r = _fit(golden, "dgp_covs", cfg) + assert r.covariate_coefficients is not None + assert list(r.covariate_coefficients.keys()) == ["zb", "zlong"] + + def test_collinear_covariate_dropped_with_name(self, golden): + # The nchar sort decides WHICH of a collinear set survives: sorted + # order is (zb, zdup, zlong), the stable QR keeps the first + # independent columns {zb, zdup}, so zlong - although it GENERATED + # zdup - is the one dropped (R-identical; the covs_collinear golden + # pins the (zb, zdup) gamma). + df = _frame(golden, "dgp_covs") + with pytest.warns(UserWarning, match="Multicollinearity"): + r = RDPlot().fit(df, "y", "x", covariates=["zlong", "zb", "zdup"]) + assert r.covariates_dropped == ["zlong"] + assert r.covariate_coefficients is not None + assert list(r.covariate_coefficients.keys()) == ["zb", "zdup"] + + +class TestPaperAnchors: + """CCT 2015 supplement Figures SA-1/SA-2 report the selector outputs on + exactly this Senate dataset; assert them independently of the JSON.""" + + @pytest.mark.parametrize( + "binselect, expected_J", + [("esmv", (15, 35)), ("es", (8, 9)), ("qs", (21, 16)), ("qsmv", (28, 49))], + ) + def test_senate_anchor(self, binselect, expected_J): + df = _senate_frame() + r = RDPlot(binselect=binselect).fit(df, "vote", "margin") + assert r.J == tuple(float(v) for v in expected_J) + if binselect in ("esmv", "es"): + assert r.J_IMSE == (8.0, 9.0) + + def test_senate_wimse_weights_inverse_map(self): + # Supplement S.1 inverse map on the default esmv fit: rscale is + # exactly (15/8, 35/9) via the paper-anchored J values, and the + # implied WIMSE weights are (1/(1+rscale^3), rscale^3/(1+rscale^3)), + # summing to 1. Explicit values guard the map itself, not just the + # identity. + r = RDPlot().fit(_senate_frame(), "vote", "margin") + assert r.rscale == pytest.approx((15 / 8, 35 / 9)) + assert r.wimse_variance_weight[0] == pytest.approx(1 / (1 + (15 / 8) ** 3)) + assert r.wimse_variance_weight[1] == pytest.approx(1 / (1 + (35 / 9) ** 3)) + for side in (0, 1): + assert (r.wimse_variance_weight[side] + r.wimse_bias_weight[side]) == pytest.approx(1.0) + + +class TestRQuirkLocks: + def test_empty_bins_dropped_but_lengths_cover_all(self): + # Senate default: J = 15 + 35 = 50 bins but one left bin is empty -> + # 49 vars_bins rows; counts still add up to the side samples. + r = RDPlot().fit(_senate_frame(), "vote", "margin") + assert r.J == (15.0, 35.0) + assert len(r.vars_bins) == 49 + assert int(np.asarray(r.vars_bins["rdplot_N"]).sum()) == r.N[0] + r.N[1] + + def test_single_obs_bin_se_zero_ci_collapses(self): + r = RDPlot().fit(_senate_frame(), "vote", "margin") + singles = r.vars_bins[r.vars_bins["rdplot_N"] == 1] + assert len(singles) > 0 + assert (singles["rdplot_se_y"] == 0).all() + assert_allclose(singles["rdplot_ci_l"], singles["rdplot_mean_y"], rtol=0) + assert_allclose(singles["rdplot_ci_r"], singles["rdplot_mean_y"], rtol=0) + + def test_qs_nbins_keeps_manual_label_but_quantile_bins(self, golden): + # rdplot.R:452-456 sets the label "manually evenly spaced" even when + # binselect chose quantile spacing; the bins themselves stay + # quantile-spaced. Label AND bin edges both locked. + cfg = golden["senate"]["configs"]["qs_nbins"] + r = _fit(golden, "senate", cfg) + assert r.binselect == "manually evenly spaced" + df = _senate_frame() + ok = df["vote"].notna() & df["margin"].notna() # rdplot's complete-case filter + x_l = np.sort(df.loc[ok & (df["margin"] < 0), "margin"].to_numpy()) + # hand-computed TYPE-7 quantile at prob 1/8 (R's default; numpy's + # default linear interpolation) - locks the convention independently + # of numpy's quantile implementation: position h = (n-1)p, linear + # interpolation between the flanking order statistics. + h_pos = (x_l.size - 1) * (1 / 8) + lo = int(np.floor(h_pos)) + expected_first_edge = x_l[lo] + (h_pos - lo) * (x_l[lo + 1] - x_l[lo]) + assert r.vars_bins["rdplot_max_bin"].iloc[0] == pytest.approx( + expected_first_edge, abs=1e-12 + ) + + def test_masspoints_adjust_equals_explicit_pr_variant(self, golden): + # adjust remaps esmv -> esmvpr; fitting esmvpr with detection off + # must give the identical result (and no remap warning). + df = _frame(golden, "dgp_ties") + with pytest.warns(UserWarning, match="Mass points"): + r_adjust = RDPlot(masspoints="adjust").fit(df, "y", "x") + with warnings.catch_warnings(): + warnings.simplefilter("error") + r_explicit = RDPlot(binselect="esmvpr", masspoints="off").fit(df, "y", "x") + assert r_adjust.J == r_explicit.J + assert r_adjust.binselect == r_explicit.binselect + pd.testing.assert_frame_equal(r_adjust.vars_bins, r_explicit.vars_bins) + + def test_masspoints_check_warns_without_remap(self, golden): + df = _frame(golden, "dgp_ties") + with pytest.warns(UserWarning, match="Mass points"): + r_check = RDPlot(masspoints="check").fit(df, "y", "x") + with warnings.catch_warnings(): + warnings.simplefilter("error") + r_off = RDPlot(masspoints="off").fit(df, "y", "x") + # check mode detects but does NOT remap: same spacings selectors. + assert r_check.J == r_off.J + assert r_check.binselect == r_off.binselect + + def test_zero_variance_side_pins_J_to_one_with_inf_imse(self): + rng = np.random.default_rng(7) + x = np.concatenate([-rng.uniform(0.1, 1, 15), rng.uniform(0.1, 1, 15)]) + y = np.where(x < 0, 3.0, rng.normal(size=30)) + df = pd.DataFrame({"y": y, "x": x}) + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter("always") + r = RDPlot().fit(df, "y", "x") + messages = [str(w.message) for w in caught] + # the constant side fires BOTH the variability rescue and the + # outcome-ties (discrete outcome) defensive warning + assert any("variability" in m and "below" in m for m in messages) + assert any("Heavy ties" in m for m in messages) + assert r.J[0] == 1.0 + assert np.isinf(r.J_IMSE[0]) # R propagates Inf through J.fun + assert r.rscale[0] == 0.0 + assert np.isfinite(r.J[1]) and r.J[1] >= 1 + + def test_discrete_outcome_warns_under_spacings_selector(self): + # Spacings selectors assume a continuously distributed outcome + # (CCT 2015 Theorems 3-4); a binary y warns and recommends the *pr + # sibling (R is silent - documented defensive warning, no remap). + rng = np.random.default_rng(21) + x = np.concatenate([-rng.uniform(0.1, 1, 40), rng.uniform(0.1, 1, 40)]) + y = (rng.uniform(size=80) < 0.4).astype(float) + df = pd.DataFrame({"y": y, "x": x}) + with pytest.warns(UserWarning, match="esmvpr"): + r = RDPlot().fit(df, "y", "x") + # warn-only: the spacings selectors still ran + assert r.binselect == "mimicking variance evenly-spaced method using spacings estimators" + with warnings.catch_warnings(): + warnings.simplefilter("error") + RDPlot(binselect="esmvpr").fit(df, "y", "x") # pr variant: silent + + def test_manual_h_with_zero_effective_obs_raises(self): + rng = np.random.default_rng(22) + x = np.concatenate([-rng.uniform(0.5, 1, 15), rng.uniform(0.5, 1, 15)]) + df = pd.DataFrame({"y": rng.normal(size=30), "x": x}) + # h below the nearest observation's distance on both sides: R would + # silently fit a zero-weight design; we raise naming the side. + with pytest.raises(ValueError, match="zero effective observations"): + RDPlot(h=0.1).fit(df, "y", "x") + + def test_finite_singular_selector_gram_does_not_downgrade(self): + # Companion to the overflow-ladder golden: a finite rank-deficient + # k=4 Gram (4 distinct x values per side) is absorbed inside + # qrXXinv's pinv fallback at UNCHANGED k, mirroring R's internal + # ginv fallback - the fit must succeed, not ladder down or raise. + rng = np.random.default_rng(11) + x = np.tile([-2.0, -1.0, 1.0, 2.0, -3.0, 3.0, -4.0, 4.0], 5) + y = 0.5 * x + rng.normal(size=x.size) + df = pd.DataFrame({"y": y, "x": x}) + r = RDPlot(masspoints="off").fit(df, "y", "x") + assert np.isfinite(r.J).all() and min(r.J) >= 1 + + +class TestDocumentedDeviations: + """Surfaces where R 4.0.0's rdplot crashes by accident and this port + implements the paper's formula instead (REGISTRY Deviation notes).""" + + def test_fractional_scale_applies_paper_ceiling(self): + # R errors ("arguments imply differing number of rows"); CCT 2015 + # Eq 2 puts the ceiling over the rescaled product: 2.5*15 -> 38, + # 2.5*35 -> 88. + df = _senate_frame() + r = RDPlot(scale=2.5).fit(df, "vote", "margin") + assert r.J == (38.0, 88.0) + assert not r.vars_bins.isna().any().any() + + def test_pair_scale_supported(self): + # R >= 4.2 rejects a length-2 scale via its vectorized-if accident. + df = _senate_frame() + r = RDPlot(scale=(2, 3)).fit(df, "vote", "margin") + assert r.J == (30.0, 105.0) + + def test_integer_scale_product_unchanged_by_ceiling(self, golden): + cfg = golden["senate"]["configs"]["scale2"] + r = _fit(golden, "senate", cfg) + assert r.J == tuple(float(v) for v in cfg["J"]) + + def test_ultra_small_scale_clamps_to_one_bin(self): + # A positive scale below the ceiling's 1e-12 epsilon must not + # produce zero bins (Eq 2's partition is at least one bin per side). + r = RDPlot(scale=1e-14).fit(_senate_frame(), "vote", "margin") + assert r.J == (1.0, 1.0) + assert len(r.vars_bins) == 2 # one (non-empty) bin per side + + +class TestValidationAndErrors: + def test_n_below_20_raises(self): + rng = np.random.default_rng(3) + x = np.concatenate([-rng.uniform(0.1, 1, 9), rng.uniform(0.1, 1, 10)]) + df = pd.DataFrame({"y": rng.normal(size=19), "x": x}) + with pytest.raises(ValueError, match="Not enough observations"): + RDPlot().fit(df, "y", "x") + + def test_one_sided_tiny_side_raises_named(self): + rng = np.random.default_rng(4) + x = np.concatenate([[-0.5], rng.uniform(0.1, 1, 24)]) + df = pd.DataFrame({"y": rng.normal(size=25), "x": x}) + with pytest.raises(ValueError, match="each side"): + RDPlot().fit(df, "y", "x") + + def test_single_support_point_side_raises(self): + # All running values tied on one side (>= 2 obs, 1 distinct value): + # every spacing is zero, J would be Inf, and the jump grid would + # divide by zero (R crashes opaquely inside seq()). Clear error + # under the default masspoints="adjust" (the remap cannot rescue a + # single support point). + rng = np.random.default_rng(23) + x = np.concatenate([np.full(15, -0.5), rng.uniform(0.1, 1, 15)]) + df = pd.DataFrame({"y": rng.normal(size=30), "x": x}) + # the guard fires BEFORE masspoints detection (fail-fast) + with pytest.raises(ValueError, match="single distinct value below"): + RDPlot().fit(df, "y", "x") + + def test_missing_rows_dropped_with_warning(self): + df = _senate_frame() + n_missing = int(np.asarray(df["vote"].isna()).sum()) + assert n_missing > 0 # the vendored CSV has NA votes + with pytest.warns(UserWarning, match=f"Dropping {n_missing} row"): + r = RDPlot().fit(df, "vote", "margin") + assert r.N[0] + r.N[1] == len(df) - n_missing + + def test_cutoff_outside_range_raises(self): + df = _senate_frame() + with pytest.raises(ValueError, match="strictly inside"): + RDPlot(cutoff=1000.0).fit(df, "vote", "margin") + + def test_missing_column_raises(self): + df = _senate_frame() + with pytest.raises(ValueError, match="not found"): + RDPlot().fit(df, "vote", "nope") + + def test_empty_covariates_list_raises(self): + df = _senate_frame() + with pytest.raises(ValueError, match="non-empty"): + RDPlot().fit(df, "vote", "margin", covariates=[]) + + def test_duplicate_covariate_names_raise(self, golden): + df = _frame(golden, "dgp_covs") + with pytest.raises(ValueError, match="[Dd]uplicate"): + RDPlot().fit(df, "y", "x", covariates=["zlong", "zlong"]) + + def test_bare_string_covariates_raise(self): + df = _senate_frame() + with pytest.raises(ValueError, match="list of column names"): + RDPlot().fit(df, "vote", "margin", covariates="margin") + + def test_covs_drop_false_strict_error_on_collinear(self, golden): + df = _frame(golden, "dgp_covs") + with pytest.raises(ValueError, match="collinear"): + RDPlot(covs_drop=False).fit(df, "y", "x", covariates=["zlong", "zb", "zdup"]) + + def test_covs_drop_false_keeps_user_covariate_order(self, golden): + # R sorts covariate columns by name length ONLY inside its + # covs_drop pipeline; with covs_drop=False the user-given order is + # preserved (same contract as RegressionDiscontinuity). Per-name + # gamma values are identical either way on full-rank covariates. + df = _frame(golden, "dgp_covs") + r_sorted = RDPlot(covs_drop=True).fit(df, "y", "x", covariates=["zlong", "zb"]) + r_strict = RDPlot(covs_drop=False).fit(df, "y", "x", covariates=["zlong", "zb"]) + assert r_sorted.covariate_coefficients is not None + assert r_strict.covariate_coefficients is not None + assert list(r_sorted.covariate_coefficients.keys()) == ["zb", "zlong"] + assert list(r_strict.covariate_coefficients.keys()) == ["zlong", "zb"] + for name in ("zb", "zlong"): + assert r_strict.covariate_coefficients[name] == pytest.approx( + r_sorted.covariate_coefficients[name], rel=1e-12 + ) + pd.testing.assert_frame_equal(r_strict.vars_bins, r_sorted.vars_bins) + + @pytest.mark.parametrize( + "kwargs", + [ + {"cutoff": np.inf}, + {"p": -1}, + {"p": True}, + {"p": 1.5}, + {"nbins": 0}, + {"nbins": (10, 14, 3)}, + {"nbins": (10, True)}, + {"binselect": "esvm"}, + {"scale": 0}, + {"scale": (-1, 2)}, + {"kernel": "gaussian"}, + {"h": -5}, + {"support": (3, 1)}, + {"support": (1,)}, + {"masspoints": "on"}, + {"ci": 0}, + {"ci": 100}, + {"ci": True}, + {"covs_drop": "yes"}, + ], + ) + def test_constructor_rejects(self, kwargs): + with pytest.raises(ValueError): + RDPlot(**kwargs) + + +class TestAPIContracts: + def test_get_params_round_trip(self): + rp = RDPlot(binselect="qs", ci=90, scale=(1, 2)) + params = rp.get_params() + assert set(params) == { + "cutoff", + "p", + "nbins", + "binselect", + "scale", + "kernel", + "h", + "support", + "masspoints", + "ci", + "covs_drop", + } + clone = RDPlot(**params) + assert clone.get_params() == params + + def test_set_params_transactional(self): + rp = RDPlot() + before = rp.get_params() + with pytest.raises(ValueError): + rp.set_params(binselect="qs", ci=200) # ci invalid -> nothing applied + assert rp.get_params() == before + with pytest.raises(ValueError): + rp.set_params(bandwidth=3) # unknown name + rp.set_params(binselect="qs") + assert rp.binselect == "qs" + + def test_fit_idempotent_on_config(self): + df = _senate_frame() + rp = RDPlot(ci=90) + before = rp.get_params() + r1 = rp.fit(df, "vote", "margin") + r2 = rp.fit(df, "vote", "margin") + assert rp.get_params() == before + assert r1.J == r2.J + pd.testing.assert_frame_equal(r1.vars_bins, r2.vars_bins) + + def test_ci_columns_always_present(self): + df = _senate_frame() + r_default = RDPlot().fit(df, "vote", "margin") + assert {"rdplot_ci_l", "rdplot_ci_r"} <= set(r_default.vars_bins.columns) + assert r_default.ci_level == 95.0 and r_default.ci_requested is False + r_90 = RDPlot(ci=90).fit(df, "vote", "margin") + assert r_90.ci_level == 90.0 and r_90.ci_requested is True + # narrower level -> strictly narrower intervals on multi-obs bins + multi = r_default.vars_bins["rdplot_N"] > 1 + width_95 = (r_default.vars_bins["rdplot_ci_r"] - r_default.vars_bins["rdplot_ci_l"])[multi] + width_90 = (r_90.vars_bins["rdplot_ci_r"] - r_90.vars_bins["rdplot_ci_l"])[multi] + assert (width_90 < width_95).all() + + def test_to_dataframe_returns_copy(self): + r = RDPlot().fit(_senate_frame(), "vote", "margin") + df1 = r.to_dataframe() + df1.iloc[0, 0] = -999 + assert r.vars_bins.iloc[0, 0] != -999 + + def test_summary_contains_r_layout_fields(self): + r = RDPlot().fit(_senate_frame(), "vote", "margin") + s = r.summary() + for needle in ( + "Bins Selected", + "IMSE-optimal bins", + "Mimicking Variance bins", + "Implied scale", + "WIMSE variance weight", + "WIMSE bias weight", + ): + assert needle in s + + def test_plot_importerror_without_matplotlib(self, monkeypatch): + import builtins + + real_import = builtins.__import__ + + def _no_mpl(name, *args, **kwargs): + if name.startswith("matplotlib"): + raise ImportError("no matplotlib") + return real_import(name, *args, **kwargs) + + r = RDPlot().fit(_senate_frame(), "vote", "margin") + monkeypatch.setattr(builtins, "__import__", _no_mpl) + with pytest.raises(ImportError, match="matplotlib is required"): + r.plot() + + def test_plot_renders_when_matplotlib_available(self): + mpl = pytest.importorskip("matplotlib") + mpl.use("Agg") + r = RDPlot(ci=90).fit(_senate_frame(), "vote", "margin") + ax = r.plot() + assert len(ax.lines) >= 3 # two curve segments + cutoff line + assert isinstance(r, RDPlotResult) + # curve segments split by construction: each side's line has exactly + # 500 points and owns its OWN x == cutoff endpoint (an x-based mask + # would hand the right curve's cutoff point to the left segment) + curve_lines = [ln for ln in ax.lines if len(ln.get_xdata()) == 500] + assert len(curve_lines) == 2 + assert curve_lines[0].get_xdata()[-1] == r.cutoff + assert curve_lines[1].get_xdata()[0] == r.cutoff From a2f9cd5c8d7022d16e9d2de65119333564fab84a Mon Sep 17 00:00:00 2001 From: igerber Date: Sat, 18 Jul 2026 18:33:03 -0400 Subject: [PATCH 2/2] fix(rdd): exact-constancy variance rescue for constant-float outcome sides np.var of a constant 0.7 vector is ~1.3e-32 (single-pass mean roundoff) while R's two-pass var() is exactly 0, so the var==0 rescue was skipped for constant non-representable-float outcomes and the resulting Inf J_MV crashed bin construction on a zero jump. Route the side variances through the port's _var0 exact-constancy helper (the R semantics already ported for the fuzzy identification stop), reproducing R's J=1 rescue, its Inf J_IMSE / NaN J_MV echoes, and the variability warning. Regression test with a constant-0.7 side added. Co-Authored-By: Claude Fable 5 Claude-Session: https://claude.ai/code/session_01VwhFMnFQGBumYfwbeQaUjm --- diff_diff/rdplot.py | 12 ++++++++++-- tests/test_rdplot.py | 21 +++++++++++++++++++++ 2 files changed, 31 insertions(+), 2 deletions(-) diff --git a/diff_diff/rdplot.py b/diff_diff/rdplot.py index 0b37677e9..35c4a58b4 100644 --- a/diff_diff/rdplot.py +++ b/diff_diff/rdplot.py @@ -39,6 +39,7 @@ from ._rdrobust_port import ( _covs_gamma, _normalize_kernel, + _var0, covs_drop_fun, qrXXinv, rdrobust_kweight, @@ -854,8 +855,15 @@ def _selector_grams() -> Tuple[int, np.ndarray, np.ndarray, np.ndarray, np.ndarr drk_i_l = _pow_outer(x_bar_i_l, k - 1) * j_arange drk_i_r = _pow_outer(x_bar_i_r, k - 1) * j_arange - var_y_l = float(np.var(y_l, ddof=1)) - var_y_r = float(np.var(y_r, ddof=1)) + # R's two-pass var() is EXACTLY zero on a constant vector; numpy's + # single-pass mean leaves ~1e-32 roundoff for constants like 0.7, + # which would skip the var==0 rescue below and crash the bin grid on + # a zero jump (J_MV = Inf). Route through the port's exact-constancy + # helper (_var0, the #686-ported R semantics) so constant sides give + # var_y = 0.0 exactly, reproducing R's rescue and its 0/0 -> NaN + # selector echoes. + var_y_l = 0.0 if _var0(y_l) else float(np.var(y_l, ddof=1)) + var_y_r = 0.0 if _var0(y_r) else float(np.var(y_r, ddof=1)) mu0_i_l = rk_i_l @ gamma_k1_l mu0_i_r = rk_i_r @ gamma_k1_r diff --git a/tests/test_rdplot.py b/tests/test_rdplot.py index d480b135d..8ffb092b1 100644 --- a/tests/test_rdplot.py +++ b/tests/test_rdplot.py @@ -350,6 +350,27 @@ def test_manual_h_with_zero_effective_obs_raises(self): with pytest.raises(ValueError, match="zero effective observations"): RDPlot(h=0.1).fit(df, "y", "x") + def test_constant_float_outcome_side_rescued_exactly(self): + # np.var of a constant 0.7 vector is ~1.3e-32, NOT 0 (single-pass + # mean roundoff), while R's two-pass var() is exactly 0 - without + # the _var0 exact-constancy routing the var==0 rescue is skipped + # and the zero jump crashes bin construction (ZeroDivisionError; + # caught by CI codex on the rebased head). R fires the rescue: + # J=1 with the variability warning, Inf J_IMSE, NaN J_MV. + rng = np.random.default_rng(7) + x = np.concatenate([-rng.uniform(0.1, 1, 15), rng.uniform(0.1, 1, 15)]) + y = np.where(x < 0, 0.7, rng.normal(size=30)) + df = pd.DataFrame({"y": y, "x": x}) + with warnings.catch_warnings(record=True) as caught: + warnings.simplefilter("always") + r = RDPlot().fit(df, "y", "x") + messages = [str(w.message) for w in caught] + assert any("variability" in m and "below" in m for m in messages) + assert r.J[0] == 1.0 + assert np.isinf(r.J_IMSE[0]) + assert np.isnan(r.J_MV[0]) # 0/0 through the MV selector, as in R + assert np.isfinite(r.J[1]) and r.J[1] >= 1 + def test_finite_singular_selector_gram_does_not_downgrade(self): # Companion to the overflow-ladder golden: a finite rank-deficient # k=4 Gram (4 distinct x values per side) is absorbed inside