Test plot, update documentation

DESCRIPTION

@@ -23,3 +23,4 @@ RoxygenNote: 6.1.1
 URL: https://github.com/kolesarm/RDHonest
 VignetteBuilder: knitr
 Language: en-US
+BugReports: https://github.com/kolesarm/RDHonest/issues

R/bnm.R

@@ -1,35 +1,34 @@
 #' Critical values for CIs based on a biased Gaussian estimator.
 #'
-#' Computes the critical value \eqn{cv_{1-\alpha}(B)} such that the confidence
-#' interval \eqn{X\pm cv_{1-\alpha}(B)} will have coverage \eqn{1-\alpha}, where
-#' \eqn{X} is Normally distributed with variance equal to \eqn{1} and maximum
-#' bias at most \eqn{B}.
+#' Computes the critical value \eqn{cv_{1-\alpha}(B)}{cv_{1-alpha}(B)} such that
+#' the confidence interval \eqn{X\pm cv_{1-\alpha}(B)}{X +/- cv_{1-alpha}(B)}
+#' will have coverage \eqn{1-\alpha}{1-alpha}, where \eqn{X} is normally
+#' distributed with variance equal to \eqn{1} and maximum bias at most \eqn{B}.
 #'
 #' @param B Maximum bias, vector of non-negative numbers.
-#' @param alpha Determines CI level, \code{1-alpha}. Needs to be between 0 and
-#'     1. Can be a vector of values.
+#' @param alpha Determines CI level, \eqn{1-\alpha}{1-alpha}. Vector of values
+#'     between 0 and 1.
 #' @return Data frame with the following columns:
 #'
 #' \describe{
 #'
 #' \item{bias}{Value of bias as specified by \code{B}}
 #'
-#' \item{alpha}{Value of \eqn{\alpha} as specified by \code{alpha}}
+#' \item{alpha}{Value of \eqn{\alpha}{alpha} as specified by \code{alpha}}
 #'
 #' \item{cv}{Critical value}
 #'
-#' \item{TeXDescription}{LaTeX-friendly description of current row}}
+#' \item{TeXDescription}{LaTeX-friendly description of the current row}}
 #' @examples
 #' ## 90% critical value:
 #' CVb(B = 1, alpha = 0.1)
 #' ## Returns data frame with 4 rows
 #' CVb(B = c(0, 0.5, 1), alpha = c(0.05, 0.1))
-#' @importFrom stats pnorm qnorm
 #' @export
 CVb <- function(B, alpha=0.05) {
-    ## Single B
-    cv <- function(B, alpha = 0.05) {
-        if(is.na(B)) return(NA)
+    ## Single B and alpha
+    cv <- function(B, alpha) {
+        if (is.na(B)) return(NA)
         stopifnot(B >= 0 & alpha > 0 & alpha < 1)
         if (B<10)
             return(sqrt(stats::qchisq(1-alpha, df = 1, ncp = B^2)))
@@ -38,9 +37,9 @@ CVb <- function(B, alpha=0.05) {
     }
 
     d <- data.frame(expand.grid(bias=B, alpha=alpha), cv=NA)
-    d$cv <- vapply(seq_len(nrow(d)),
+    d$cv <- vapply(seq_along(d$bias),
                    function(j) cv(d$bias[j], d$alpha[j]), numeric(1))
-    d$TeXDescription <- paste("$\\alpha=", d$alpha, "$", sep="")
+    d$TeXDescription <- paste0("$\\alpha=", d$alpha, "$")
 
     d
 }

R/documentation.R

@@ -2,9 +2,9 @@
 #'
 #' Subset of Ludwig-Miller data. Counties with missing poverty rate, of with
 #' both outcomes missing (\code{hs} and \code{mortality}) were removed. In the
-#' original dataset, Yellowstone County, MT (\code{oldcode==27056}) was entered
+#' original dataset, Yellowstone County, MT (\code{oldcode = 27056}) was entered
 #' twice, here the duplicate is removed. Yellowstone National Park, MT
-#' (\code{oldcode==27057}) is also removed due to it being an outlier for both
+#' (\code{oldcode = 27057}) is also removed due to it being an outlier for both
 #' outcomes. Counties with \code{oldcode} equal to (3014, 32032, 47010, 47040,
 #' 47074, 47074, 47078, 47079, 47096) matched more than one FIPS entry, so the
 #' county labels may not be correct. Mortality data is missing for Alaska.

R/kernels.R

@@ -1,10 +1,11 @@
 #' Equivalent kernel for local linear regression.
 #'
 #' Calculates equivalent kernel for local polynomial regression.
-#' @param kernel kernel type. Can be a function supported on \eqn{[-1, 1]}
-#'     (boundary kernel) or \eqn{[0,1]} (interior kernel), or else one of
-#'     \code{"uniform"} (\eqn{I(|u|<1)}), \code{"triangular"} (\eqn{(1-|u|)I(|u|<1)}),
-#'     or \code{"epanechnikov"} (k(u)=(3/4)(1-u^2)I(|u|<1)).
+#' @param kernel kernel type. Can be a function supported on \eqn{[0, 1]}
+#'     (boundary kernel) or \eqn{[-1, 1]} (interior kernel), or else one of
+#'     \code{"triangular"} (\eqn{k(u)=(1-|u|)_{+}}), \code{"epanechnikov"}
+#'     (\eqn{k(u)=(3/4)(1-u^2)_{+}}), or \code{"uniform"} (\eqn{k(u)=
+#'     (|u|<1)/2}).
 #' @param boundary Logical scalar, specifying whether we are at a boundary.
 #' @param order Order of local polynomial: \code{0} means local constant,
 #'     \code{1} local linear, \code{2} local quadratic etc.

R/plots.R

@@ -15,6 +15,8 @@
 #'     \code{avg=Inf}). Otherwise the size of points is constant.
 #' @examples
 #' plot_RDscatter(RDData(lee08, cutoff=0), avg=20)
+#' plot_RDscatter(RDData(data.frame(y=log(cghs$earnings), x=cghs$yearat14),
+#'                cutoff=1947), avg=Inf, propdotsize=TRUE)
 #' @export
 plot_RDscatter <- function(d, avg=10, xlab=NULL, ylab=NULL,
                            window=NULL, vert=TRUE, propdotsize=FALSE) {

readme.md

@@ -3,14 +3,16 @@
 # RDHonest
 
 Honest and efficient confidence intervals in regression discontinuity designs
-using procedures
-from
-[Armstrong and Kolesár (2018)](https://arxiv.org/abs/1606.01200),
-[Armstrong and Kolesár (2018)](https://arxiv.org/abs/1511.06028),
-and [Kolesár and Rothe (2018)](https://arxiv.org/abs/1606.04086)
+using procedures from [Armstrong and Kolesár
+(2018)](https://arxiv.org/abs/1606.01200), [Armstrong and Kolesár
+(2018)](https://doi.org/10.3982/ECTA14434)
+([preprint](https://arxiv.org/abs/1511.06028)), and [Kolesár and Rothe
+(2018)](https://doi.org/10.1257/aer.20160945)
+([preprint](https://arxiv.org/abs/1606.04086))
 
 See vignette `RDHonest.pdf` (stored under `doc/`) for description of the
-package.
+package, and `manual.pdf` (also stored under `doc/`) for documentation of the
+package functions.
 
 This software package is based upon work supported by the National Science
 Foundation under grant numbers SES-1628939 (Armstrong) and SES-1628878

tests/testthat/test_rd.R

@@ -12,6 +12,16 @@ test_that("IK bandwidth calculations", {
     expect_equal(sqrt(mean(d$sigma2m)), 12.4627517531)
 })
 
+test_that("Plots", {
+    if (requireNamespace("ggplot2", quietly = TRUE)) {
+        expect_silent(invisible(plot_RDscatter(
+            RDData(data.frame(y=log(cghs$earnings),
+                              x=cghs$yearat14),
+                   cutoff=1947), avg=Inf, propdotsize=TRUE)))
+        }
+})
+
+
 test_that("Honest inference in Lee and LM data",  {
 
     ## Replicate 1606.01200v2