Merge pull request #15 from biometryhub/dev

Dev

Author: wvjgsuhp

DESCRIPTION

@@ -1,5 +1,5 @@
 Type: Package
-Package: RankedSetSampling
+Package: InPlotSampling
 Title: Easing the Application of Ranked Set Sampling in Practice
 Version: 0.1.0
 Date: 2021-02-09
@@ -11,15 +11,15 @@ Authors@R: c(
            comment = c(ORCID = "0000-0001-5291-3600")),
     person("Peter", "Kasprzak", , "peter.kasprzak@adelaide.edu.au", role = "aut")
   )
-Description: The RankedSetSampling package provides a way for researchers
+Description: The InPlotSampling package provides a way for researchers
     to easily implement Ranked Set Sampling in practice.  Ranked Set
     Sampling was originally described by McIntyre (1952) (reprinted in
     2005) <doi:10.1198/000313005X54180>. This package takes work by Omer
     and Kravchuk (2021) <https://doi.org/10.1007/s13253-021-00439-1> and
     enables easy use of the methods.
 License: MIT + file LICENSE
-URL: https://biometryhub.github.io/RankedSetSampling/
-BugReports: https://github.com/biometryhub/RankedSetSampling/issues
+URL: https://biometryhub.github.io/InPlotSampling/
+BugReports: https://github.com/biometryhub/InPlotSampling/issues
 Depends:
     R (>= 3.5.0)
 Imports: 
@@ -36,4 +36,4 @@ LinkingTo:
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2

NAMESPACE

@@ -6,10 +6,11 @@ export(rss_jps_estimate)
 export(rss_sample)
 export(sbs_pps_estimate)
 export(sbs_pps_sample)
+export(two_stage_cluster_sample)
 importFrom(Rcpp,sourceCpp)
 importFrom(stats,aggregate)
 importFrom(stats,qt)
 importFrom(stats,rnorm)
 importFrom(stats,sd)
 importFrom(stats,var)
-useDynLib(RankedSetSampling, .registration = TRUE)
+useDynLib(InPlotSampling, .registration = TRUE)

R/CoefF.R

@@ -1,6 +1,6 @@
 #' This function computes the coefficient of variance estimator
 #'
-#' @param H Set size for each raking group.
+#' @param H Set size for each ranking group.
 #' @param n Sample size.
 #'
 #' @return

R/RankedSetSampling-package.R renamed to R/InPlotSampling-package.R

@@ -1,11 +1,11 @@
-#' @name RankedSetSampling
+#' @name InPlotSampling
 "_PACKAGE"
 
 # The following block is used by usethis to automatically manage
 # roxygen namespace tags. Modify with care!
 ## usethis namespace: start
 #' @importFrom Rcpp sourceCpp
-#' @useDynLib RankedSetSampling, .registration = TRUE
+#' @useDynLib InPlotSampling, .registration = TRUE
 ## usethis namespace: end
 NULL
 

R/RcppExports.R

@@ -0,0 +1,8 @@
+# Generated by using Rcpp::compileAttributes() -> do not edit by hand
+# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
+
+#' @keywords internal
+pascal <- function(m, popsize, set) {
+    .Call(`_InPlotSampling_pascal`, m, popsize, set)
+}
+

R/jps_estimate.R

@@ -1,7 +1,7 @@
 #' Computes the estimator for JPS data
 #'
 #' @param data The data to use for estimation.
-#' @param set_size Set size for each raking group.
+#' @param set_size Set size for each ranking group.
 #' @param replace Logical (default `TRUE`). Sample with replacement?
 #' @param model_based An inference mode:
 #' - `FALSE`: design based inference

R/jps_estimate_single.R

@@ -2,7 +2,7 @@
 #'
 #' @param ranks Ranks of Y.
 #' @param y Response measurements.
-#' @param set_size Set size for each raking group.
+#' @param set_size Set size for each ranking group.
 #' @param N Finite population size.
 #' @param coef Coefficients used in variance computation when sample size is n.
 #' @param coef_del Coefficients used in variance computation when the i-th unit is deleted.

R/jps_sample.R

@@ -1,7 +1,12 @@
 #' Generate JPS sampling on the provided population.
 #'
-#' @inheritParams rss_sample
+#' @param pop Population that will be sampled.
+#' @param n Sample size.
+#' @param H Set size for each ranking group.
+#' @param K Number of rankers.
 #' @param tau A parameter which controls ranking quality.
+#' @param replace A boolean which specifies whether to sample with replacement or not.
+#' @param with_index A boolean which specifies whether to return the index of the sampled population.
 #'
 #' @return A matrix with ranks from each ranker.
 #' @export
@@ -36,10 +41,11 @@
 #' #>  [9,]  8.701285  2  1  2
 #' #> [10,] 13.323884  3  3  3
 #'
-jps_sample <- function(pop, n, H, tau, K, replace = FALSE) {
-  verify_jps_params(pop, n, H, tau, K, replace)
+jps_sample <- function(pop, n, H, tau, K, replace = FALSE, with_index = FALSE) {
+  verify_jps_params(pop, n, H, tau, K, replace, with_index)
 
-  sampling_matrix <- matrix(sample(pop, n * H, replace = replace), ncol = H, nrow = n)
+  sampling_indices <- sample(seq_along(pop), n * H, replace = replace)
+  sampling_matrix <- matrix(pop[sampling_indices], ncol = H, nrow = n)
 
   # rank each SRS unit post experimentally
   jps_matrix <- matrix(0, ncol = K + 1, nrow = n)
@@ -55,6 +61,10 @@ jps_sample <- function(pop, n, H, tau, K, replace = FALSE) {
   }
 
   colnames(jps_matrix) <- c("Y", paste0("R", 1:K))
+  if (with_index) {
+    jps_matrix <- cbind(sampling_indices[1:n], jps_matrix)
+    colnames(jps_matrix)[1] <- "i"
+  }
+
   return(jps_matrix)
 }
-#' @export

R/rss_jps_estimate.R

@@ -36,7 +36,7 @@
 #' taus <- sigma * sqrt(1 / rhos^2 - 1)
 #' population <- qnorm((1:population_size) / (population_size + 1), mu, sigma)
 #'
-#' data <- RankedSetSampling::jps_sample(population, n, H, taus, n_rankers, with_replacement)
+#' data <- InPlotSampling::jps_sample(population, n, H, taus, n_rankers, with_replacement)
 #' data <- data[order(data[, 2]), ]
 #'
 #' rss_jps_estimate(
@@ -75,7 +75,7 @@
 #' x <- population + tau * rnorm(population_size, 0, 1)
 #'
 #' population <- cbind(population, x)
-#' data <- RankedSetSampling::rss_sample(population, n, H, n_rankers, with_replacement)
+#' data <- InPlotSampling::rss_sample(population, n, H, n_rankers, with_replacement)
 #' data <- data[order(data[, 2]), ]
 #'
 #' rss_estimates <- rss_jps_estimate(

R/rss_sample.R

@@ -50,7 +50,7 @@ rss_sample <- function(pop, n, H, K, replace = FALSE) {
 #'
 #' @param pop Population that will be sampled with an auxiliary parameter in the second column.
 #' @param n Sample size.
-#' @param H Set size for each raking group.
+#' @param H Set size for each ranking group.
 #' @param K Number of rankers.
 #'
 #' @return A matrix with ranks from each ranker.