function join_count

NAMESPACE

@@ -6,7 +6,8 @@ export(DSI)
 export(DSI2)
 export(doublets.test)
 export(fit_gradients)
-export(oruns.test)
+export(join_count)
+export(oruns_test)
 export(plot_AFSD)
 export(theme_r4pde)
 importFrom(dplyr,"%>%")

R/join_count.R

@@ -0,0 +1,117 @@
+
+#' Test for Spatial Join Count Statistics
+#'
+#' @description
+#' The function `join_count` calculates spatial statistics for a matrix.
+#' It identifies patterns of aggregation for values in a binary matrix based on
+#' join count statistics. The results determine whether the observed spatial
+#' arrangement is aggregated, random, or uniform regarding the presence of 1s
+#' ("D") and 0s ("H").
+#'
+#' @param matrix_data A binary matrix (with elements 0 and 1) representing the
+#'   spatial distribution of two types of points. Rows and columns of the matrix
+#'   indicate coordinates while the 0s and 1s represent different categories of
+#'   observations.
+#'
+#' @return A list containing several statistical measures:
+#'   \itemize{
+#'     \item Observed_01_10: The number of observed 01 or 10 sequences.
+#'     \item Observed_11: The number of observed 11 sequences.
+#'     \item Expected_ER_01: The expected number of 01 or 10 sequences under
+#'           random distribution.
+#'     \item Expected_ER_11: The expected number of 11 sequences under random
+#'           distribution.
+#'     \item sR_01: Standard deviation for 01 or 10 sequences.
+#'     \item sR_11: Standard deviation for 11 sequences.
+#'     \item ZHD: The Z-score for 01 or 10 sequences.
+#'     \item ZDD: The Z-score for 11 sequences.
+#'     \item PatternHD: Descriptive result for 01 or 10 sequences - "Aggregated"
+#'           or "Not Aggregated".
+#'     \item PatternDD: Descriptive result for 11 sequences - "Aggregated" or
+#'           "Not Aggregated".
+#'   }
+#'
+#' @details
+#' The function first calculates the count of specific binary sequences in the
+#' matrix both horizontally and vertically. It then computes expected values
+#' and standard deviations based on the spatial arrangement dimensions and uses
+#' these for Z-score calculations to identify the pattern of the matrix.
+#'
+#' @examples
+#' matrix_data <- matrix(c(1,1,1,0,0,
+#'                         1,1,1,0,0,
+#'                         1,1,1,0,0,
+#'                         1,1,1,0,0,
+#'                         0,0,0,0,0), ncol = 5, byrow = TRUE)
+#' join_count(matrix_data)
+#'
+#' @export
+join_count <- function(matrix_data) {
+
+  count_sequences <- function(matrix_data) {
+    count_01_10 <- 0  # previously count_HD_DH
+    count_11 <- 0     # previously count_DD
+
+    for (i in 1:nrow(matrix_data)) {
+      for (j in 1:(ncol(matrix_data)-1)) {
+        if ((matrix_data[i,j] == 0 && matrix_data[i,j+1] == 1) ||
+            (matrix_data[i,j] == 1 && matrix_data[i,j+1] == 0)) {
+          count_01_10 <- count_01_10 + 1
+        } else if (matrix_data[i,j] == 1 && matrix_data[i,j+1] == 1) {
+          count_11 <- count_11 + 1
+        }
+      }
+    }
+
+    for (j in 1:ncol(matrix_data)) {
+      for (i in 1:(nrow(matrix_data)-1)) {
+        if ((matrix_data[i,j] == 0 && matrix_data[i+1,j] == 1) ||
+            (matrix_data[i,j] == 1 && matrix_data[i+1,j] == 0)) {
+          count_01_10 <- count_01_10 + 1
+        } else if (matrix_data[i,j] == 1 && matrix_data[i+1,j] == 1) {
+          count_11 <- count_11 + 1
+        }
+      }
+    }
+
+    return(list(One_Zero_or_Zero_One = count_01_10, One_One = count_11))
+  }
+
+  results <- count_sequences(matrix_data)
+
+  # Calculate matrix dimensions and S-values
+  num_rows <- nrow(matrix_data)
+  num_cols <- ncol(matrix_data)
+
+  S0 <- 2 * ((2*num_rows * num_cols) - num_rows - num_cols)
+  S1 <- 2 * S0
+  S2 <- 8 * ((8*num_rows * num_cols) - (7 * num_rows) - (7 * num_cols) + 4)
+
+  # Calculate y as the mean of the numeric matrix
+  y <- mean(matrix_data)
+
+  # Calculate expected values and standard deviations
+  ER_01 <- S0 * y * (1 - y)  # Expected random 01 or 10 sequences
+  sR_01 <- sqrt(S2 * y * (1 - y) + 4 * (S1 - S2) * y^2 * (1 - y)^2) / 2  # Std dev
+
+  ER_11 <- S0 * y^2 / 2  # Expected random 11 sequences
+  sR_11 <- sqrt(S1 * y^2 + (S2 - 2 * S1) * y^3 + (S1 - S2) * y^4) / 2  # Std dev
+
+  # Calculate Z value
+  ZHD <- (results$One_Zero_or_Zero_One - ER_01 - 0.5) / sR_01
+
+  pattern1 <- ifelse(ZHD < -1.64, "Aggregated", "Not Aggregated")
+
+  ZDD <- (results$One_One - ER_11 + 0.5) / sR_11
+
+  pattern2 <- ifelse(ZDD > 1.64, "Aggregated", "Not Aggregated")
+
+  # Return results
+  return(list(Observed_01_10 = results$One_Zero_or_Zero_One, Observed_11 = results$One_One,
+              Expected_ER_01 = ER_01, Expected_ER_11 = ER_11,
+              sR_01 = sR_01, sR_11 = sR_11,
+              ZHD = ZHD, ZDD = ZDD, PatternHD = pattern1, PatternDD = pattern2))
+}
+
+
+

R/oruns.test.R → R/oruns_test.R

@@ -5,11 +5,11 @@
 #' @param x A numeric vector representing the input data
 #' @return A list with elements U (number of runs), EU (expected number of runs), pvalue, and result (either "clustering" or "randomness").
 #' @examples
-#' oruns.test(c(1, 0, 1, 1, 0, 1, 0, 0, 1, 1))
+#' oruns_test(c(1, 0, 1, 1, 0, 1, 0, 0, 1, 1))
 #'
 #' @importFrom stats pnorm
 #' @export
-oruns.test <- function(x) {
+oruns_test <- function(x) {
   # S is the input data
   S <- x
 

docs/pkgdown.yml

@@ -2,5 +2,5 @@ pandoc: 2.19.2
 pkgdown: 2.0.7
 pkgdown_sha: ~
 articles: {}
-last_built: 2023-10-14T16:07Z
+last_built: 2023-10-17T01:04Z