Merge pull request #372 from DeclareDesign/cran-patch-again

lfe tests removed per CRAN email
DeclareDesign · Feb 26, 2021 · 23154bc · 23154bc
2 parents 1820dc9 + e3439a7
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: estimatr
 Type: Package
 Title: Fast Estimators for Design-Based Inference
-Version: 0.30.0
+Version: 0.30.2
 Authors@R: c(person("Graeme", "Blair", email = "[email protected]", role = c("aut", "cre")),
              person("Jasper", "Cooper", email = "[email protected]", role = c("aut")),
              person("Alexander", "Coppock", email = "[email protected]", role = c("aut")),

diff --git a/NEWS.md b/NEWS.md
@@ -1,3 +1,7 @@
+# estimatr 0.30.1
+
+* Remove lfe from tests
+
 # estimatr 0.30.0
 
 * Test suite changes (skip if not installed for checking against other packages)

diff --git a/cran-comments.md b/cran-comments.md
@@ -1,7 +1,9 @@
 ## Submission
 
-Thank you for accepting this patch, which skips tests for suggests packages if they are not installed per email from Prof. Ripley. This fixes all ERRORs on CRAN now. This also addresses the DOI problems raised in the submission.
+Thank you for accepting this patch, which addresses the issue raised by Kurt that we had an unstated dependency on lfe. We removed the tests associated with lfe.
 
 There are no changes to worse in reverse depends. 
 
-We have checked this in win builder and on OS X, linux, and Windows in release, oldrel, and devel on Github Actions with no issues.
+We have checked this in win builder and on OS X, linux, and Windows in release, oldrel, and devel on Github Actions with no issues. 
+
+
diff --git a/tests/testthat/test-iv-robust-fes.R b/tests/testthat/test-iv-robust-fes.R
@@ -104,139 +104,6 @@ test_that("FE matches with multiple FEs and covars", {
   }
 })
 
-test_that("IV FE matches lfe including proj r2", {
-  skip_if_not_installed("lfe")
-  skip_on_cran()
-  ## unweighted
-
-  ## Classical
-  rfo <- iv_robust(Y ~ X1 + X2 | Z + X2, fixed_effects = ~ B + B2, data = dat, se_type = "classical")
-  feo <- lfe::felm(Y ~ X2 | B + B2 | (X1 ~ Z), data = dat)
-  sfeo <- lfe:::summary.felm(feo)
-
-  expect_equivalent(
-    tidy(rfo)[rfo$term %in% c("X1", "X2"), c("estimate", "std.error")],
-    data.frame(feo$coefficients, feo$se)[c(2, 1), ]
-  )
-
-  expect_equivalent(
-    rfo[c("r.squared", "adj.r.squared", "proj_r.squared", "proj_adj.r.squared")],
-    sfeo[c("r.squared", "adj.r.squared", "P.r.squared", "P.adj.r.squared")]
-  )
-
-  expect_equivalent(
-    rfo[["proj_fstatistic"]],
-    sfeo[["P.fstat"]][c("F", "df1", "df2")]
-  )
-
-  ## HC1
-  rfo <- iv_robust(Y ~ X1 + X2 | Z + X2, fixed_effects = ~ B + B2, data = dat, se_type = "HC1")
-
-  feo <- lfe::felm(Y ~ X2 | B + B2 | (X1 ~ Z), data = dat)
-  sfeo <- lfe:::summary.felm(feo, robust = T)
-
-  expect_equivalent(
-    tidy(rfo)[rfo$term %in% c("X1", "X2"), c("estimate", "std.error")],
-    data.frame(feo$coefficients, sqrt(diag(feo$robustvcv)))[c(2, 1), ]
-  )
-
-  expect_equivalent(
-    rfo[c("r.squared", "adj.r.squared", "proj_r.squared", "proj_adj.r.squared")],
-    sfeo[c("r.squared", "adj.r.squared", "P.r.squared", "P.adj.r.squared")]
-  )
-
-  expect_equivalent(
-    rfo[["proj_fstatistic"]],
-    sfeo[["P.fstat"]][c("F", "df1", "df2")]
-  )
-
-  ## CR1stata
-  rfo <- iv_robust(Y ~ X1 + X2 | Z + X2, fixed_effects = ~ B + B2, clusters = cl, data = dat, se_type = "stata")
-  feo <- lfe::felm(Y ~ X2 | B + B2 | (X1 ~ Z) | cl, data = dat)
-
-  sfeo <- lfe:::summary.felm(feo)
-
-  expect_equivalent(
-    tidy(rfo)[rfo$term %in% c("X1", "X2"), c("estimate", "std.error")],
-    data.frame(feo$coefficients, sqrt(diag(feo$clustervcv)))[c(2, 1), ]
-  )
-
-  expect_equivalent(
-    rfo[c("r.squared", "adj.r.squared", "proj_r.squared", "proj_adj.r.squared")],
-    sfeo[c("r.squared", "adj.r.squared", "P.r.squared", "P.adj.r.squared")]
-  )
-
-  expect_true(
-    max(abs(
-      rfo[["proj_fstatistic"]] - sfeo[["P.fstat"]][c("F", "df1", "df2")]
-    )) < 1e-7
-  )
-
-  ## Weighted
-  rfo <- iv_robust(Y ~ X1 + X2 | Z + X2, fixed_effects = ~ B + B2, data = dat, weights = w, se_type = "classical")
-  feo <- lfe::felm(Y ~ X2 | B + B2 | (X1 ~ Z), data = dat, weights = dat$w)
-  sfeo <- lfe:::summary.felm(feo)
-
-  expect_equivalent(
-    tidy(rfo)[rfo$term %in% c("X1", "X2"), c("estimate", "std.error")],
-    data.frame(feo$coefficients, feo$se)[c(2, 1), ]
-  )
-
-  expect_equivalent(
-    rfo[c("r.squared", "adj.r.squared", "proj_r.squared", "proj_adj.r.squared")],
-    sfeo[c("r.squared", "adj.r.squared", "P.r.squared", "P.adj.r.squared")]
-  )
-
-  expect_equivalent(
-    rfo[["proj_fstatistic"]],
-    sfeo[["P.fstat"]][c("F", "df1", "df2")]
-  )
-
-  ## HC1
-  rfo <- iv_robust(Y ~ X1 + X2 | Z + X2, fixed_effects = ~ B + B2, data = dat, weights = w, se_type = "HC1")
-
-  feo <- lfe::felm(Y ~ X2 | B + B2 | (X1 ~ Z), data = dat,  weights = dat$w)
-  sfeo <- lfe:::summary.felm(feo, robust = T)
-
-  expect_equivalent(
-    tidy(rfo)[rfo$term %in% c("X1", "X2"), c("estimate", "std.error")],
-    data.frame(feo$coefficients, sqrt(diag(feo$robustvcv)))[c(2, 1), ]
-  )
-
-  expect_equivalent(
-    rfo[c("r.squared", "adj.r.squared", "proj_r.squared", "proj_adj.r.squared")],
-    sfeo[c("r.squared", "adj.r.squared", "P.r.squared", "P.adj.r.squared")]
-  )
-
-  expect_equivalent(
-    rfo[["proj_fstatistic"]],
-    sfeo[["P.fstat"]][c("F", "df1", "df2")]
-  )
-
-  ## CR1stata
-  rfo <- iv_robust(Y ~ X1 + X2 | Z + X2, fixed_effects = ~ B + B2, clusters = cl, data = dat, weights = w, se_type = "stata")
-  feo <- lfe::felm(Y ~ X2 | B + B2 | (X1 ~ Z) | cl, data = dat, weights = dat$w)
-
-  sfeo <- lfe:::summary.felm(feo)
-
-  expect_equivalent(
-    tidy(rfo)[rfo$term %in% c("X1", "X2"), c("estimate", "std.error")],
-    data.frame(feo$coefficients, sqrt(diag(feo$clustervcv)))[c(2, 1), ]
-  )
-
-  expect_equivalent(
-    rfo[c("r.squared", "adj.r.squared", "proj_r.squared", "proj_adj.r.squared")],
-    sfeo[c("r.squared", "adj.r.squared", "P.r.squared", "P.adj.r.squared")]
-  )
-
-  expect_true(
-    max(abs(
-      rfo[["proj_fstatistic"]] - sfeo[["P.fstat"]][c("F", "df1", "df2")]
-    )) < 1e-6
-  )
-
-})
-
 test_that("IV FE warns about diagnostics", {
 
   expect_warning(

diff --git a/tests/testthat/test-lm-robust-fes.R b/tests/testthat/test-lm-robust-fes.R
@@ -337,26 +337,6 @@ test_that("FEs handle collinear FEs", {
     }
   }
 
-  # lo <- lfe::felm(Y ~ Z + X|B + B2 + Bdup, data = dat)
-  #
-  # mtcars$cyl2 <- mtcars$cyl
-  # # Doesn't properly deal if collinearity not in first two
-  # lfe:::summary.felm(lfe::felm(mpg ~ hp | cyl + am + cyl2, data = mtcars))$coefficients
-  # library(estimatr)
-  # mtcars$cyl2 <- mtcars$cyl
-  # tidy(lm_robust(mpg ~ hp, fixed_effects = ~ cyl + cyl2 + am, data = mtcars))
-  #
-  # tidy(lm_robust(mpg ~ hp, fixed_effects = ~ cyl + am, data = mtcars))
-  # tidy(lm_robust(mpg ~ hp + factor(cyl) + factor(am), data = mtcars))[2,]
-  #
-  # tidy(lm_robust(mpg ~ hp, fixed_effects = ~ cyl + cyl2 + am, data = mtcars))
-  # tidy(lm_robust(mpg ~ hp + factor(cyl) + factor(cyl2)  + factor(am), data = mtcars))[2,]
-  # tidy(lm_robust(mpg ~ hp + factor(cyl) + factor(am), data = mtcars))[2,]
-  #
-  # # LFE does the right thing if the dependency is in the first two
-  # lfe:::summary.felm(lfe::felm(mpg ~ hp | cyl + cyl2 + am, data = mtcars))$coefficients
-  # tidy(lm_robust(mpg ~ hp + factor(cyl) + factor(cyl3) + factor(am), data = mtcars, se_type = "classical"))[2,]
-
   ## Collinear factors
   for (se_type in cr_se_types) {
     ro <- tidy(lm_robust(Y ~ Z + X + factor(B) + factor(Bdup) + factor(B2), clusters = cl, data = dat, se_type = se_type))
@@ -454,76 +434,6 @@ test_that("test matches stata absorb", {
 
 })
 
-
-test_that("FEs give correct projected F-stats", {
-
-  skip_if_not_installed("lfe")
-  skip_on_cran()
-
-  feo <- lfe::felm(Y ~ Z + X | B + B2, data = dat)
-  sfeo <- lfe:::summary.felm(feo)
-  sfeor <- lfe:::summary.felm(feo, robust = TRUE)
-
-  cfeo <- lfe::felm(Y ~ Z + X | B + B2 | 0 | cl, data = dat)
-  sfeoc <- lfe:::summary.felm(cfeo, robust = TRUE)
-
-  # classical
-  rfo <- lm_robust(Y ~ Z + X, fixed_effects = ~ B + B2, data = dat, se_type = "classical")
-
-  expect_equivalent(
-    tidy(rfo)[rfo$term %in% c("Z", "X"), c("estimate", "std.error", "statistic", "p.value")],
-    as.data.frame(sfeo$coefficients[, 1:4])
-  )
-
-  expect_equivalent(
-    rfo[c("r.squared", "adj.r.squared", "proj_r.squared", "proj_adj.r.squared")],
-    sfeo[c("r.squared", "adj.r.squared", "P.r.squared", "P.adj.r.squared")]
-  )
-
-  expect_equivalent(
-    rfo[["proj_fstatistic"]],
-    sfeo[["P.fstat"]][c("F", "df1", "df2")]
-  )
-
-  ## HC1
-  rfo <- lm_robust(Y ~ Z + X, fixed_effects = ~ B + B2, data = dat, se_type = "HC1")
-
-  expect_equivalent(
-    tidy(rfo)[rfo$term %in% c("Z", "X"), c("estimate", "std.error", "statistic", "p.value")],
-    as.data.frame(sfeor$coefficients[, 1:4])
-  )
-
-  expect_equivalent(
-    rfo[c("r.squared", "adj.r.squared", "proj_r.squared", "proj_adj.r.squared")],
-    sfeor[c("r.squared", "adj.r.squared", "P.r.squared", "P.adj.r.squared")]
-  )
-
-  expect_equivalent(
-    rfo[["proj_fstatistic"]],
-    sfeor[["P.fstat"]][c("F", "df1", "df2")]
-  )
-
-  ## CR stata
-  rfo <- lm_robust(Y ~ Z + X, fixed_effects = ~ B + B2, clusters = cl, data = dat, se_type = "stata")
-
-  # Different pval because lfe doesn't use J-1 as it's DoF
-  expect_equivalent(
-    tidy(rfo)[rfo$term %in% c("Z", "X"), c("estimate", "std.error")],
-    as.data.frame(sfeoc$coefficients[, c(1, 2)])
-  )
-
-  expect_equivalent(
-    rfo[c("r.squared", "adj.r.squared", "proj_r.squared", "proj_adj.r.squared")],
-    sfeoc[c("r.squared", "adj.r.squared", "P.r.squared", "P.adj.r.squared")]
-  )
-
-  expect_equivalent(
-    rfo[["proj_fstatistic"]],
-    sfeoc[["P.fstat"]][c("F", "df1", "df2")]
-  )
-
-})
-
 test_that("FE matches lm_robust with one block", {
 
   skip_if_not_installed("sandwich")