From cfaae71bccdbd977922aeb8fe7d36f82e06ad368 Mon Sep 17 00:00:00 2001
From: Grant McDermott <grant.mcdermott@gmail.com>
Date: Mon, 11 Dec 2023 16:04:25 -0800
Subject: [PATCH] Add tests

---
 inst/tinytest/test_aggr_es.R | 92 ++++++++++++++++++++++++++++++++++++
 1 file changed, 92 insertions(+)
 create mode 100644 inst/tinytest/test_aggr_es.R

diff --git a/inst/tinytest/test_aggr_es.R b/inst/tinytest/test_aggr_es.R
new file mode 100644
index 0000000..0c7ab98
--- /dev/null
+++ b/inst/tinytest/test_aggr_es.R
@@ -0,0 +1,92 @@
+library(ggfixest)
+library(tinytest)
+
+#
+# Datasets and models ----
+
+data("base_did", package = "fixest")
+
+est = fixest::feols(
+	fml = y ~ x1 + i(period, treat, 5) | id + period,
+	data = base_did
+)
+
+aggr_post = aggr_es(est)                             # default post
+aggr_cum  = aggr_es(est, aggregation = "cumulative") # cumulative instead of mean effects
+aggr_pre  = aggr_es(est, period = "pre")             # pre period instead of post
+aggr_both = aggr_es(est, period = "both")            # pre & post periods separately
+aggr_rhs1 = aggr_es(est, period = "pre", rhs = -1)   # pre period with H0 value of 1
+
+#
+# Known output ----
+
+aggr_post_known = data.frame(
+	term = "post-treatment (mean)",
+	estimate = 3.906554122950695,
+	std.error = 0.8598575665281263,
+	statistic = 4.543257249830702,
+	p.value = 5.5391585244779775e-06,
+	s.value = 17.461901741925015,
+	conf.low = 2.2212642607213144,
+	conf.high = 5.591843985180075
+)
+
+
+aggr_cum_known = data.frame(
+	term = "post-treatment (cumulative)",
+	estimate = 19.532770614753474,
+	std.error = 4.299287821377354,
+	statistic = 4.543257261733131,
+	p.value = 5.539158211582727e-06,
+	s.value = 17.461901823419783,
+	conf.low = 11.106321325682188,
+	conf.high = 27.95921990382476
+)
+
+aggr_pre_known = data.frame(
+	term = "pre-treatment (mean)",
+	estimate = -1.1798706992411545,
+	std.error = 0.8561963882056884,
+	statistic = -1.3780374637106132,
+	p.value = 0.16819172163573184,
+	s.value = 2.5718213967199377,
+	conf.low = -2.857984783817578,
+	conf.high = 0.49824338533526924
+)
+
+aggr_both_known =
+	data.frame(
+		term = c("pre-treatment (mean)", "post-treatment (mean)"),
+		estimate = c(-1.1798706992411545, 3.906554122950695),
+		std.error = c(0.8561963882056884, 0.8598575665281263),
+		statistic = c(-1.3780374637106132, 4.543257249830702),
+		p.value = c(0.16819172163573184, 5.5391585244779775e-06),
+		s.value = c(2.5718213967199377, 17.461901741925015),
+		conf.low = c(-2.857984783817578, 2.2212642607213144),
+		conf.high = c(0.49824338533526924, 5.591843985180075)
+	)
+
+aggr_rhs1_known = data.frame(
+	term = "pre-treatment (mean)",
+	estimate = -0.1798706992411545,
+	std.error = 0.8561963882056884,
+	statistic = -0.21008112358206216,
+	p.value = 0.8336043579365446,
+	s.value = 0.26256527509602834,
+	conf.low = -1.8579847838175783,
+	conf.high = 1.4982433853352692
+)
+
+
+#
+# tests ----
+tol = 1e-6
+
+for (col in c("term", "estimate", "std.error", "statistic", "p.value", "s.value",
+							"conf.low", "conf.high")) {
+	expect_equivalent(aggr_post[[col]], aggr_post_known[[col]], tolerance = tol)
+	expect_equivalent(aggr_cum[[col]], aggr_cum_known[[col]], tolerance = tol)
+	expect_equivalent(aggr_pre[[col]], aggr_pre_known[[col]], tolerance = tol)
+	expect_equivalent(aggr_both[[col]], aggr_both_known[[col]], tolerance = tol)
+	expect_equivalent(aggr_rhs1[[col]], aggr_rhs1_known[[col]], tolerance = tol)
+}