Update interface to match assesspy 2.0

CITATION.cff

@@ -2,6 +2,6 @@ message: "If you use this software, please cite it as below."
 authors:
 - family-names: "Cook County Assessor's Office"
 title: "AssessR"
-version: 0.6.0
+version: 1.0.0
 date-released: 2019-01-01
 url: "https://github.com/ccao-data/assessr"

DESCRIPTION

@@ -1,13 +1,14 @@
 Package: assessr
 Type: Package
 Title: Measure Property Assessment Performance
-Version: 0.6.0
+Version: 1.0.0
 Authors@R: c(
     person(given = "Dan", family = "Snow", email="[email protected]", role=c("aut", "cre")),
     person(given = "William", family = "Ridgeway", email="[email protected]", role=c("ctb")),
     person(given = "Rob", family = "Ross", role=c("ctb")),
     person(given = "Nathan", family = "Dignazio", role=c("ctb")),
-    person(given = "Damon", family = "Major", role=c("ctb"))
+    person(given = "Damon", family = "Major", role=c("ctb")),
+    person(given = "Jean", family = "Cochrane", role=c("ctb"))
     )
 Date: 2023-08-07
 Description: An R package to measure the performance of property assessments
@@ -19,7 +20,7 @@ Encoding: UTF-8
 LazyData: true
 Imports:
     stats
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.2
 Suggests:
     covr,
     devtools,
@@ -37,5 +38,5 @@ Suggests:
     testthat,
     tibble,
     tidyr
-Depends: 
+Depends:
     R (>= 3.5.0)

NAMESPACE

@@ -4,8 +4,8 @@ export(boot_ci)
 export(cod)
 export(cod_ci)
 export(cod_met)
-export(detect_chasing)
 export(is_outlier)
+export(is_sales_chased)
 export(ki)
 export(mki)
 export(mki_met)

R/ci.R

@@ -4,45 +4,57 @@
 #'   for a given numeric input and a chosen function.
 #'
 #' @param FUN Function to bootstrap. Must return a single value.
-#' @param nboot Default 100. Number of iterations to use to estimate
+#' @param estimate A numeric vector of estimated values. Must be the same
+#'   length as \code{sale_price}.
+#' @param sale_price A numeric vector of sale prices. Must be the same
+#'   length as \code{estimate}.
+#' @param nboot Default 1000. Number of iterations to use to estimate
 #'   the output statistic confidence interval.
 #' @param alpha Default 0.05. Numeric value indicating the confidence
 #'   interval to return. 0.05 will return the 95\% confidence interval.
 #' @param na.rm Default FALSE. A boolean value indicating whether or not to
 #'   remove NA values. If missing values are present but not removed the
 #'   function will output NA.
-#' @param ... Named arguments passed on to \code{FUN}.
 #'
 #' @return A two-long numeric vector containing the bootstrapped confidence
 #'   interval of the input vector(s).
 #'
 #' @examples
 #'
 #' # Calculate COD confidence interval
-#' boot_ci(cod, nboot = 100, ratio = ratios_sample$ratio)
+#' boot_ci(cod, ratios_sample$estimate, ratios_sample$sale_price, nboot = 100)
 #'
 #' # Calculate PRD confidence interval
 #' boot_ci(
 #'   prd,
 #'   nboot = 100,
-#'   assessed = ratios_sample$assessed,
+#'   estimate = ratios_sample$estimate,
 #'   sale_price = ratios_sample$sale_price,
 #'   na.rm = FALSE
 #' )
 #' @export
-boot_ci <- function(FUN = NULL, nboot = 100, alpha = 0.05, na.rm = FALSE, ...) { # nolint
+boot_ci <- function(
+    FUN,
+    estimate,
+    sale_price,
+    nboot = 1000,
+    alpha = 0.05,
+    na.rm = FALSE) {
+  # Input checking and error handling
+  check_inputs(estimate, sale_price)
 
   # Check that the input function returns a numeric vector
-  est <- FUN(...)
+  est <- FUN(estimate, sale_price)
   stopifnot(
     length(est) == 1,
     is.numeric(est),
-    is.logical(na.rm)
+    is.logical(na.rm),
+    nboot > 0
   )
 
   # Create an index of missing values, where TRUE when missing.
   # If na.rm is FALSE and index contains TRUE, return NA
-  missing_idx <- index_na(...)
+  missing_idx <- index_na(estimate, sale_price)
   if (any(missing_idx) && !na.rm) {
     return(NA_real_)
   }
@@ -62,7 +74,10 @@ boot_ci <- function(FUN = NULL, nboot = 100, alpha = 0.05, na.rm = FALSE, ...) {
     # For each of the input vectors to FUN, subset by first removing any
     # index positions that have a missing value, then take a random sample of
     # each vector using the sample index
-    sampled <- lapply(list(...), function(x) x[!missing_idx][idx])
+    sampled <- lapply(
+      list(estimate, sale_price),
+      function(x) x[!missing_idx][idx]
+    )
 
     # For each bootstrap sample, apply the function and output an estimate for
     # that sample
@@ -85,16 +100,21 @@ boot_ci <- function(FUN = NULL, nboot = 100, alpha = 0.05, na.rm = FALSE, ...) {
 #' @examples
 #'
 #' # Calculate COD confidence interval
-#' cod_ci(ratios_sample$ratio)
+#' cod_ci(ratios_sample$estimate, ratios_sample$sale_price)
 #' @export
-cod_ci <- function(ratio, nboot = 100, alpha = 0.05, na.rm = FALSE) { # nolint
-
+cod_ci <- function(
+    estimate,
+    sale_price,
+    nboot = 1000,
+    alpha = 0.05,
+    na.rm = FALSE) {
   cod_ci <- boot_ci(
     cod,
+    estimate = estimate,
+    sale_price = sale_price,
     nboot = nboot,
     alpha = alpha,
-    na.rm = na.rm,
-    ratio = ratio
+    na.rm = na.rm
   )
 
   return(cod_ci)
@@ -108,17 +128,21 @@ cod_ci <- function(ratio, nboot = 100, alpha = 0.05, na.rm = FALSE) { # nolint
 #' @examples
 #'
 #' # Calculate PRD confidence interval
-#' prd_ci(ratios_sample$assessed, ratios_sample$sale_price)
+#' prd_ci(ratios_sample$estimate, ratios_sample$sale_price)
 #' @export
-prd_ci <- function(assessed, sale_price, nboot = 100, alpha = 0.05, na.rm = FALSE) { # nolint
-
+prd_ci <- function(
+    estimate,
+    sale_price,
+    nboot = 1000,
+    alpha = 0.05,
+    na.rm = FALSE) {
   prd_ci <- boot_ci(
     prd,
+    estimate = estimate,
+    sale_price = sale_price,
     nboot = nboot,
     alpha = alpha,
-    na.rm = na.rm,
-    assessed = assessed,
-    sale_price = sale_price
+    na.rm = na.rm
   )
 
   return(prd_ci)
@@ -132,25 +156,24 @@ prd_ci <- function(assessed, sale_price, nboot = 100, alpha = 0.05, na.rm = FALS
 #' @examples
 #'
 #' # Calculate PRD confidence interval
-#' prb_ci(ratios_sample$assessed, ratios_sample$sale_price)
+#' prb_ci(ratios_sample$estimate, ratios_sample$sale_price)
 #' @export
-prb_ci <- function(assessed, sale_price, alpha = 0.05, na.rm = FALSE) { # nolint
-
+prb_ci <- function(estimate, sale_price, alpha = 0.05, na.rm = FALSE) {
   # Input checking and error handling
-  check_inputs(assessed, sale_price)
+  check_inputs(estimate, sale_price)
 
   # Remove NAs from input vectors. Otherwise, return NA if the input vectors
   # contain any NA values
-  idx <- index_na(assessed, sale_price)
+  idx <- index_na(estimate, sale_price)
   if (na.rm) {
-    assessed <- assessed[!idx]
+    estimate <- estimate[!idx]
     sale_price <- sale_price[!idx]
   } else if (any(idx) && !na.rm) {
     return(NA_real_)
   }
 
   # Calculate PRB model
-  prb_model <- calc_prb(assessed, sale_price)
+  prb_model <- calc_prb(estimate, sale_price)
 
   # Extract PRB CI from model
   prb_ci <- stats::confint(prb_model, level = (1 - alpha))[2, ]

R/data.R

@@ -3,13 +3,30 @@
 #' This sample was take from Evanston and New Trier in 2019. Ratios are
 #' calculated using assessor certified (post-appeal) fair market values.
 #'
-#' @format A data frame with 979 observation and 4 variables:
+#' @format A data frame with 979 observation and 3 variables:
 #' \describe{
-#'   \item{assessed}{The fair market assessed value predicted by CCAO assessment
+#'   \item{estimate}{The fair market assessed value predicted by CCAO assessment
 #'     models, including any successful appeals}
 #'   \item{sale_price}{The recorded sale price of this property}
-#'   \item{ratio}{Sales ratio representing fair market value / sale price}
 #'   \item{town}{Township name the property is in}
 #' }
 #'
 "ratios_sample"
+
+#' Sample of sales and estimated market values provided by Quintos in the
+#'    following MKI papers:
+#'
+#' @references
+#' Quintos, C. (2020). A Gini measure for vertical equity in property
+#' assessments. <https://researchexchange.iaao.org/jptaa/vol17/iss2/2>
+#'
+#' Quintos, C. (2021). A Gini decomposition of the sources of inequality in
+#' property assessments. <https://researchexchange.iaao.org/jptaa/vol18/iss2/6>
+#'
+#' @format A data frame with 30 observation and 2 variables:
+#' \describe{
+#'   \item{estimate}{Assessed fair market value}
+#'   \item{sale_price}{Recorded sale price of this property}
+#' }
+#'
+"quintos_sample"