Merge branch 'main' into 79-new-function-install-depricated-package

Author: SanderDevisscher

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: fistools
 Title: Tools & data used for wildlife management & invasive species in Flanders
-Version: 1.2.13
+Version: 1.2.14
 Authors@R: c(
     person(given = "Sander", middle = "", family = "Devisscher", "sander.devisscher@inbo.be", 
       role = c("aut", "cre"), comment = c(ORCID = "0000-0003-2015-5731")),

NAMESPACE

@@ -16,7 +16,12 @@ export(install_depricated)
 export(install_sp)
 export(label_converter)
 export(label_selecter)
+export(qd_pci1)
+export(qd_pci2)
+export(qd_pci2_D)
 export(rename_ct_files)
+export(sunsetter)
+export(sunsetter2)
 importClassesFrom(sp,CRS)
 importFrom(magrittr,"%>%")
 importFrom(sp,CRS)

R/PCI.R

@@ -0,0 +1,203 @@
+# --- Potential for Conflict Index --- #
+# Vaske, J. J., Beaman, J., Barreto, H., & Shelby, L. B. (2010).
+# An Extension and Further Validation of the Potential for Conflict Index.
+# Leisure Sciences, 32(X), 240–254
+
+############################
+### >>> qd_pci1
+############################
+
+#' Potential conflict index (first variant)
+#'
+#' questionnaire data analysis: potential conflict index
+#' @param x vector with scores of the respondents
+#' @param scale_values vector with levels; default: -2:2
+#' @param x_is_table if TRUE, x is table with the distribution of the scores
+#'
+#' @return PCI-score (potential for conflict index)
+#' @export
+#' @family plotting
+#'
+#' @examples
+#' \dontrun{
+#'  set.seed(201)
+#'  Xv <- sample(-2:2, size = 100, replace = TRUE) #random responses
+#'  Yv <- rep(c(-2,2),50) #most extreme difference
+#'  Zv <- rep(2,100) #minimal difference
+
+#'  #qd_pci1
+#'  qd_pci1(Xv, scale_values = -2:2, x_is_table = FALSE) # 0.4
+#'  qd_pci1(Yv, scale_values = -2:2, x_is_table = FALSE) # 1
+#'  qd_pci1(Zv, scale_values = -2:2, x_is_table = FALSE) # 0
+#' }
+#'
+qd_pci1 <- function(x, scale_values = c(-2:2),
+                    x_is_table = FALSE){
+
+  ### ERROR CONTROL AND PREPARE DATA
+  if (scale_values[1] != -scale_values[length(scale_values)])
+    stop("index should be symmetric")
+  if (x_is_table) {
+    if (length(x) != length(scale_values))
+      stop("table of x should contain countdata for every scale-value")
+  } else {
+    x <- table(factor(x, levels = scale_values))
+  }
+  S <- NULL #To avoid the compilation NOTE
+
+  ### PREP DATA
+  countdata <- data.frame(N = as.numeric(x),
+                          X = abs(scale_values),
+                          S = sign(scale_values))
+  negatives <- subset(countdata, S == -1)
+  positives <- subset(countdata, S == 1)
+  neutrals  <- subset(countdata, S == 0)
+
+  #CALC DATA
+  sum_Xa <- sum(positives$N * positives$X)
+  sum_Xu <- sum(negatives$N * negatives$X)
+  Xt     <- sum_Xa + sum_Xu
+  n      <- sum(positives$N) + sum(negatives$N) + sum(neutrals$N)
+  Z      <- n * max(c(min(scale_values), max(scale_values)))
+
+  #RETURN RESULT
+  (1 - abs((sum_Xa / Xt) - (sum_Xu / Xt))) * Xt/Z
+}
+
+
+
+
+###########################
+### >>> qd_pci2
+###########################
+
+
+#' Distance matrix for qd_pci2
+#'
+#'Calculates distance matrix for the function qd_pci2
+#' @param x vector with the scores of the respondents
+#' @param m m value in the formula (see details)
+#' @param p power value in the formula (see details)
+#' @details
+#' \deqn{Dp_{x,y} = (|r_{x} - r_{y}|) - (m-1))^{p}}
+#' \deqn{if sign(r_{x} \neq r_{y}) \\
+#' else d_{x,y} = 0}
+#' Dp_x,y = (|r_x - r_y| - (m-1))^p
+#' @return single value containing pci index
+#' @examples
+#' \dontrun{
+#' #'set.seed(201)
+#'Xv <- sample(-2:2, size = 100, replace = TRUE) #random responses
+#'qd_pci2(Xv, scale_values = -2:2, x_is_table = FALSE, m = 1, p = 1) # 0.37
+#' }
+#' @export
+#' @family plotting
+
+
+qd_pci2_D <- function(x, m=1, p=1){
+  d <- matrix(nrow = length(x), ncol = length(x), data = NA)
+  for (i in 1:nrow(d)) {
+    for (j in 1:i) {
+      if (abs(c(sign(x[i]) - sign(x[j]))) == 2) {
+        d[i,j] <- d[j,i] <- (abs(x[i] - x[j]) - (m - 1)) ^ p
+      }
+      else {
+        d[i,j] <- d[j,i] <- 0
+      }
+    }
+  }
+  return(d)
+}
+
+###----------------
+
+#' Potential conflict index (second variant)
+#'
+#' Calculates the potential conflict index based on the distance matrix between responses.
+#'
+#' @param x vector with scores of the respondents
+#' @param scale_values vector with levels; default: -2:2
+#' @param x_is_table if TRUE, x is table with the distribution of the scores
+#' @param m correction; default: m = 1
+#' @param p power; default: p = 1
+#' @param print flag; if TRUE print results
+#'
+#' @return PCI-score (potential for conflict index)
+#' @export
+#' @family plotting
+#'
+#' @examples
+#' \dontrun{
+#'set.seed(201)
+#'Xv <- sample(-2:2, size = 100, replace = TRUE) #random responses
+#'Yv <- rep(c(-2,2),50) #most extreme difference
+#'Zv <- rep(2,100) #minimal difference
+#' #qd_pci2 - using D2 (m=1)
+#'qd_pci2(Xv, scale_values = -2:2, x_is_table = FALSE, m = 1, p = 1) # 0.37
+#'qd_pci2(Yv, scale_values = -2:2, x_is_table = FALSE, m = 1, p = 1) # 1
+#'qd_pci2(Zv, scale_values = -2:2, x_is_table = FALSE, m = 1, p = 1) # 0
+
+#qd_pci2 - using D1 (m=2)
+#'qd_pci2(Xv, scale_values = -2:2, x_is_table = FALSE, m = 2, p = 1) # 0.31
+#'qd_pci2(Yv, scale_values = -2:2, x_is_table = FALSE, m = 2, p = 1) # 1
+#'qd_pci2(Zv, scale_values = -2:2, x_is_table = FALSE, m = 2, p = 1) # 0
+#' }
+qd_pci2 <- function(x, scale_values = c(-2:2),
+                    x_is_table = FALSE, m = 1, p = 1, print = FALSE){
+
+  ### ERROR CONTROL AND PREPARE DATA
+
+  if (scale_values[1] != -scale_values[length(scale_values)])
+    stop("index should be symmetric")
+  if (x_is_table) {
+    if (length(x) != length(scale_values))
+      stop("table of x should contain countdata for every scale-value")
+  } else {
+    x <- table(factor(x, levels = scale_values))
+  }
+
+  ### PREP DATA
+
+  #Total N
+  Ntot <- sum(x)
+
+  #call distance function
+  d <- qd_pci2_D(scale_values, m = m, p = p)
+
+  #matrix with counts
+  n <- matrix(nrow = length(x), ncol = length(x), data = rep(x, length(x)))
+
+  #Actual Distance
+  #n = nk, t(n) = nh
+  #d is distance matrix between the scale_value levels
+  #d * nk * nh accounts for number of elements in each scale_value level
+  #rowsums(d*n*t(n)) calculates the deltax for each level
+  #diag(d)*diag(n)^2 actual distance with itself is subtracted
+  #sum(...) sums the results for each level
+
+  weightedsum <- sum(rowSums(d * n * t(n)) - (diag(d) * diag(n) * diag(n)))
+
+  #Maximum Possible Distance
+  #dmax = max distance between 2 single elements
+  #even N: multiply with Ntot^2 = max distance
+  #  if each element is at the extremes
+  #odd N: multiply with Ntot^2 - 1
+  dmax <- max(d)
+
+  delta <- dmax * (Ntot^2 - Ntot %% 2) / 2
+
+  #return the normalized sum
+  if (print == TRUE) {
+    cat("\nqd_pci2 (m =", m, ", p =", p, ",
+       levels =", length(scale_values), ")\n")
+    cat("------------------------------------\n")
+    cat("Total actual distance:", weightedsum, "\n")
+    cat("Maximum total distance:", delta, "\n")
+    cat("Maximum distance:", dmax, "\n")
+    cat("\nqd_pci2:", round(weightedsum / delta, 2),"\n")
+  }
+
+  return(invisible(weightedsum / delta))
+}
+
+