Pull Request follow-up for #30: Example and Vignette runtime

.Rbuildignore

@@ -4,3 +4,4 @@ README
 deltaSD.png
 ^LICENSE\.md$
 .github
+vignettes/precompute.R

.github/workflows/tests.yml

@@ -15,8 +15,8 @@ jobs:
     strategy:
       matrix:
         config:
-          - {os: macos-latest,  r: 'release'}
           - {os: ubuntu-latest, r: 'release'}
+          - {os: macos-latest,  r: 'release'}
 
     steps:
     - name: Checkout code
@@ -53,6 +53,8 @@ jobs:
       uses: r-lib/actions/setup-tinytex@v2
 
     - name: Check build
+      env:
+        HONESTDID_RUN_TESTS: '1'
       run: |
         devtools::document()
         devtools::check()

R/arp-nuisance.R

@@ -534,16 +534,19 @@
     lpDualSoln = .lp_dual_fn(y_T = y_T_ARP, X_T = X_T_ARP, eta = linSoln$eta_star,
                              gamma_tilde = linSoln$lambda, sigma = sigma_ARP)
 
-    sigma_B_dual = base::sqrt( base::t(lpDualSoln$gamma_tilde) %*% sigma_ARP %*% lpDualSoln$gamma_tilde)
+    sigma_B_dual2 = base::t(lpDualSoln$gamma_tilde) %*% sigma_ARP %*% lpDualSoln$gamma_tilde
 
     #If sigma_B_dual is 0 to numerical precision, reject iff eta > 0
-    if(sigma_B_dual < 10^(-10)){
+    if ( base::abs(sigma_B_dual2) < .Machine$double.eps ) {
       base::return(base::list(reject = base::ifelse(linSoln$eta_star > 0, 1, 0),
                               eta = linSoln$eta_star,
                               delta = linSoln$delta_star, 
                               lambda = linSoln$lambda))
+    } else if ( sigma_B_dual2 < 0 ) {
+      base::stop(".vlo_vup_dual_fn returned a negative variance")
     }
 
+    sigma_B_dual = base::sqrt(sigma_B_dual2)
     maxstat = lpDualSoln$eta/sigma_B_dual
 
     # HYBRID: Modify vlo, vup for the hybrid test

R/honest_sunab.R

@@ -1,36 +1,38 @@
+#' @title sunab_beta_vcv
+#'
 #' @description
 #' This function takes a regression estimated using fixest with the sunab option
 #' and extracts the aggregated event-study coefficients and their variance-covariance matrix
+#'
 #' @param sunab_fixest The result of a fixest call using the sunab option
+#'
 #' @returns A list containing beta (the event-study coefficients),
 #'          sigma (the variance-covariance matrix), and
 #'          cohorts (the relative times corresponding to beta, sigma)
-
-sunab_beta_vcv <-
-function(sunab_fixest){
+sunab_beta_vcv <- function(sunab_fixest) {
 
   ## The following code block extracts the weights on individual coefs used in
   # the fixest aggregation ##
   sunab_agg   <- sunab_fixest$model_matrix_info$sunab$agg_period
-  sunab_names <- names(sunab_fixest$coefficients)
-  sunab_sel   <- grepl(sunab_agg, sunab_names, perl=TRUE)
+  sunab_names <- base::names(sunab_fixest$coefficients)
+  sunab_sel   <- base::grepl(sunab_agg, sunab_names, perl=TRUE)
   sunab_names <- sunab_names[sunab_sel]
-  if(!is.null(sunab_fixest$weights)){
-    sunab_wgt <- colSums(sunab_fixest$weights * sign(model.matrix(sunab_fixest)[, sunab_names, drop=FALSE]))
+  if(!base::is.null(sunab_fixest$weights)){
+    sunab_wgt <- base::colSums(sunab_fixest$weights * base::sign(stats::model.matrix(sunab_fixest)[, sunab_names, drop=FALSE]))
   } else {
-    sunab_wgt <- colSums(sign(model.matrix(sunab_fixest)[, sunab_names, drop=FALSE]))
+    sunab_wgt <- base::colSums(base::sign(stats::model.matrix(sunab_fixest)[, sunab_names, drop=FALSE]))
   }
 
   #Construct matrix sunab_trans such that sunab_trans %*% non-aggregated coefs = aggregated coefs,
-  sunab_cohorts <- as.numeric(gsub(paste0(".*", sunab_agg, ".*"), "\\2", sunab_names, perl=TRUE))
-  sunab_mat     <- model.matrix(~ 0 + factor(sunab_cohorts))
-  sunab_trans   <- solve(t(sunab_mat) %*% (sunab_wgt * sunab_mat)) %*% t(sunab_wgt * sunab_mat)
+  sunab_cohorts <- base::as.numeric(base::gsub(base::paste0(".*", sunab_agg, ".*"), "\\2", sunab_names, perl=TRUE))
+  sunab_mat     <- stats::model.matrix(~ 0 + base::factor(sunab_cohorts))
+  sunab_trans   <- base::solve(base::t(sunab_mat) %*% (sunab_wgt * sunab_mat)) %*% base::t(sunab_wgt * sunab_mat)
 
   #Get the coefs and vcv
-  sunab_coefs   <- sunab_trans %*% cbind(sunab_fixest$coefficients[sunab_sel])
-  sunab_vcov    <- sunab_trans %*% sunab_fixest$cov.scaled[sunab_sel, sunab_sel] %*% t(sunab_trans)
+  sunab_coefs   <- sunab_trans %*% base::cbind(sunab_fixest$coefficients[sunab_sel])
+  sunab_vcov    <- sunab_trans %*% sunab_fixest$cov.scaled[sunab_sel, sunab_sel] %*% base::t(sunab_trans)
 
-  return(list(beta    = sunab_coefs,
-              sigma   = sunab_vcov,
-              cohorts = sort(unique(sunab_cohorts))))
+  base::return(base::list(beta    = sunab_coefs,
+                          sigma   = sunab_vcov,
+                          cohorts = base::sort(base::unique(sunab_cohorts))))
 }

README.md

@@ -383,27 +383,27 @@ function(sunab_fixest){
   ## The following code block extracts the weights on individual coefs used in
   # the fixest aggregation ##
   sunab_agg   <- sunab_fixest$model_matrix_info$sunab$agg_period
-  sunab_names <- names(sunab_fixest$coefficients)
-  sunab_sel   <- grepl(sunab_agg, sunab_names, perl=TRUE)
+  sunab_names <- base::names(sunab_fixest$coefficients)
+  sunab_sel   <- base::grepl(sunab_agg, sunab_names, perl=TRUE)
   sunab_names <- sunab_names[sunab_sel]
-  if(!is.null(sunab_fixest$weights)){
-    sunab_wgt <- colSums(sunab_fixest$weights * sign(model.matrix(sunab_fixest)[, sunab_names, drop=FALSE]))
+  if(!base::is.null(sunab_fixest$weights)){
+    sunab_wgt <- base::colSums(sunab_fixest$weights * base::sign(stats::model.matrix(sunab_fixest)[, sunab_names, drop=FALSE]))
   } else {
-    sunab_wgt <- colSums(sign(model.matrix(sunab_fixest)[, sunab_names, drop=FALSE]))
+    sunab_wgt <- base::colSums(base::sign(stats::model.matrix(sunab_fixest)[, sunab_names, drop=FALSE]))
   }
 
   #Construct matrix sunab_trans such that sunab_trans %*% non-aggregated coefs = aggregated coefs,
-  sunab_cohorts <- as.numeric(gsub(paste0(".*", sunab_agg, ".*"), "\\2", sunab_names, perl=TRUE))
-  sunab_mat     <- model.matrix(~ 0 + factor(sunab_cohorts))
-  sunab_trans   <- solve(t(sunab_mat) %*% (sunab_wgt * sunab_mat)) %*% t(sunab_wgt * sunab_mat)
+  sunab_cohorts <- base::as.numeric(base::gsub(base::paste0(".*", sunab_agg, ".*"), "\\2", sunab_names, perl=TRUE))
+  sunab_mat     <- stats::model.matrix(~ 0 + base::factor(sunab_cohorts))
+  sunab_trans   <- base::solve(base::t(sunab_mat) %*% (sunab_wgt * sunab_mat)) %*% base::t(sunab_wgt * sunab_mat)
 
   #Get the coefs and vcv
-  sunab_coefs   <- sunab_trans %*% cbind(sunab_fixest$coefficients[sunab_sel])
-  sunab_vcov    <- sunab_trans %*% sunab_fixest$cov.scaled[sunab_sel, sunab_sel] %*% t(sunab_trans)
+  sunab_coefs   <- sunab_trans %*% base::cbind(sunab_fixest$coefficients[sunab_sel])
+  sunab_vcov    <- sunab_trans %*% sunab_fixest$cov.scaled[sunab_sel, sunab_sel] %*% base::t(sunab_trans)
 
-  return(list(beta    = sunab_coefs,
-              sigma   = sunab_vcov,
-              cohorts = sort(unique(sunab_cohorts))))
+  base::return(base::list(beta    = sunab_coefs,
+                          sigma   = sunab_vcov,
+                          cohorts = base::sort(base::unique(sunab_cohorts))))
 }
 ```
 
@@ -475,8 +475,6 @@ honest_did <- function(...) UseMethod("honest_did")
 #'  points for computational reasons.
 #' @param ... Parameters to pass to `createSensitivityResults` or
 #'  `createSensitivityResults_relativeMagnitudes`.
-#' @inheritParams HonestDiD::createSensitivityResults
-#' @inheritParams HonestDid::createSensitivityResults_relativeMagnitudes
 honest_did.AGGTEobj <- function(es,
                                 e          = 0,
                                 type       = c("smoothness", "relative_magnitude"),

man/VignetteResults.Rd

@@ -0,0 +1,18 @@
+\docType{data}
+\name{VignetteResults}
+\alias{VignetteResults}
+\title{
+  Pre-computed results to use in the Vignette.
+}
+\description{
+    This list contains pre-comuted time-intensite results used in the vignette.
+}
+\format{
+  \describe{A list, containing 5 results shown in the vignette; refer to the text for details.
+    \item{BC_DeltaSDNB_RobustResults}{}
+    \item{BC_DeltaSDRM_RobustResults}{}
+    \item{BC_OriginalResults}{}
+    \item{LW_DeltaSD_RobustResults}{}
+    \item{LW_DeltaSDD_RobustResults}{}
+  }
+}

man/createEventStudyPlot.Rd

@@ -52,6 +52,7 @@ Rambachan, Ashesh and Jonathan Roth. "An Honest Approach to Parallel Trends." 20
 Ashesh Rambachan
 }
 \examples{
+\dontrun{
   # Simple use case; for more detailed examples,
   # see <https://github.com/asheshrambachan/HonestDiD#honestdid>
   createEventStudyPlot(betahat         = BCdata_EventStudy$betahat,
@@ -62,3 +63,4 @@ Ashesh Rambachan
                        timeVec         = BCdata_EventStudy$timeVec,
                        referencePeriod = BCdata_EventStudy$referencePeriod)
 }
+}

man/createSensitivityPlot.Rd

@@ -39,6 +39,7 @@ Returns ggplot object of the sensitivity plot.
   Ashesh Rambachan
 }
 \examples{
+\dontrun{
   # Simple use case; for more detailed examples,
   # see <https://github.com/asheshrambachan/HonestDiD#honestdid>
   robustResults <-
@@ -55,3 +56,4 @@ Returns ggplot object of the sensitivity plot.
                         alpha          = 0.05)
   createSensitivityPlot(robustResults, originalResults)
 }
+}

man/createSensitivityPlot_relativeMagnitudes.Rd

@@ -40,16 +40,16 @@ Returns ggplot object of the sensitivity plot.
   Ashesh Rambachan
 }
 \examples{
+\dontrun{
   # Simple use case. For more detailed examples,
-  # see <https://github.com/asheshrambachan/HonestDiD#honestdid>;
-  # for additional precision, increase the number of gridPoints.
+  # see <https://github.com/asheshrambachan/HonestDiD#honestdid>
   kwargs <- list(betahat        = BCdata_EventStudy$betahat,
                  sigma          = BCdata_EventStudy$sigma,
                  numPrePeriods  = length(BCdata_EventStudy$prePeriodIndices),
                  numPostPeriods = length(BCdata_EventStudy$postPeriodIndices),
                  alpha          = 0.05)
-  robustResults   <- do.call(createSensitivityResults_relativeMagnitudes,
-                             c(kwargs, list(gridPoints=100)))
+  robustResults   <- do.call(createSensitivityResults_relativeMagnitudes, kwargs)
   originalResults <- do.call(constructOriginalCS, kwargs)
   createSensitivityPlot_relativeMagnitudes(robustResults, originalResults)
 }
+}

man/createSensitivityResults.Rd

@@ -75,6 +75,7 @@ Rambachan, Ashesh and Jonathan Roth. "An Honest Approach to Parallel Trends." 20
 Ashesh Rambachan
 }
 \examples{
+\dontrun{
   # Simple use case; for more detailed examples,
   # see <https://github.com/asheshrambachan/HonestDiD#honestdid>
   createSensitivityResults(betahat        = BCdata_EventStudy$betahat,
@@ -83,3 +84,4 @@ Ashesh Rambachan
                            numPostPeriods = length(BCdata_EventStudy$postPeriodIndices),
                            alpha          = 0.05)
 }
+}

man/createSensitivityResults_relativeMagnitudes.Rd

@@ -32,7 +32,7 @@ createSensitivityResults_relativeMagnitudes(betahat, sigma,
   Covariance matrix of event study coefficients.
   }
   \item{numPrePeriods}{
-  Number of pre-periods. If user selects bound = "deviation from linear trends" (Delta^{SDRM} as base choice of Delta), then numPrePeriods must be greater than one. See details for further explanation.
+  Number of pre-periods. If user selects bound = "deviation from linear trends" (\eqn{Delta^{SDRM}} as base choice of Delta), then numPrePeriods must be greater than one. See details for further explanation.
   }
   \item{numPostPeriods}{
   Number of post-periods.
@@ -99,14 +99,14 @@ Rambachan, Ashesh and Jonathan Roth. "An Honest Approach to Parallel Trends." 20
 Ashesh Rambachan
 }
 \examples{
+\dontrun{
   # Simple use case. For more detailed examples,
-  # see <https://github.com/asheshrambachan/HonestDiD#honestdid>;
-  # for additional precision, increase the number of gridPoints.
+  # see <https://github.com/asheshrambachan/HonestDiD#honestdid>
   kwargs <- list(betahat        = BCdata_EventStudy$betahat,
                  sigma          = BCdata_EventStudy$sigma,
                  numPrePeriods  = length(BCdata_EventStudy$prePeriodIndices),
                  numPostPeriods = length(BCdata_EventStudy$postPeriodIndices),
-                 alpha          = 0.05,
-                 gridPoints     = 100)
+                 alpha          = 0.05)
   do.call(createSensitivityResults_relativeMagnitudes, kwargs)
 }
+}