v2.3

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: shar
 Title: Species-Habitat Associations
-Version: 2.2.1
+Version: 2.3
 Authors@R: c(person("Maximilian H.K.", "Hesselbarth", email = "[email protected]",
              role = c("aut", "cre"), comment = c(ORCID = "0000-0003-1125-9918")),
              person("Marco", "Sciaini", email = "[email protected]",
@@ -45,7 +45,7 @@ Suggests:
     rmarkdown,
     spatstat (>= 2.0-0),
     testthat (>= 3.0.0)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.0
 VignetteBuilder: knitr
 Encoding: UTF-8
 LazyData: true

NEWS.md

@@ -1,3 +1,10 @@
+# shar 2.3
+* Improvements
+  * Option to return fitted parameters during `fit_point_process`
+  * Using `NA` instead of "NA"
+* Various
+  * Iterations and stopping criterion are vectors instead of list during `reconstruct_algorithm`
+
 # shar 2.2.1
 * Improvements
   * Simplify roxygen2 docs

R/fit_point_process.R

@@ -6,6 +6,7 @@
 #' @param n_random Integer with number of randomizations.
 #' @param process Character specifying which point process model to use.
 #' Either \code{"poisson"} or \code{"cluster"}.
+#' @param return_para Logical if fitted parameters should be returned.
 #' @param return_input Logical if the original input data is returned.
 #' @param simplify Logical if only pattern will be returned if \code{n_random = 1}
 #' and \code{return_input = FALSE}.
@@ -32,11 +33,8 @@
 #' ecology. Chapman and Hall/CRC Press, Boca Raton. ISBN 978-1-4200-8254-8
 #'
 #' @export
-fit_point_process <- function(pattern,
-                              n_random = 1, process = "poisson",
-                              return_input = TRUE,
-                              simplify = FALSE,
-                              verbose = TRUE){
+fit_point_process <- function(pattern, n_random = 1, process = "poisson", return_para = FALSE,
+                              return_input = TRUE, simplify = FALSE, verbose = TRUE){
 
   # check if n_random is >= 1
   if (!n_random >= 1) {
@@ -45,8 +43,6 @@ fit_point_process <- function(pattern,
 
   }
 
-  iterations_list <- as.list(rep(NA, times = n_random))
-
   # unmark pattern
   if (spatstat.geom::is.marked(pattern)) {
 
@@ -72,6 +68,16 @@ fit_point_process <- function(pattern,
 
     })
 
+    # calc parameters
+    if (return_para) {
+
+      number_points <- pattern$n
+      lambda <- spatstat.geom::intensity(pattern)
+
+      param_vec <- c(number_points = number_points,lambda = lambda)
+
+    }
+
   } else if (process == "cluster") {
 
     # fit cluster process
@@ -81,6 +87,18 @@ fit_point_process <- function(pattern,
                                                            correction = "best"),
                                            method = "mincon", improve.type = "none")
 
+    # calc parameters
+    if (return_para) {
+
+      number_parents <- fitted_process$clustpar[["kappa"]] * spatstat.geom::area(pattern$window)
+      number_points <- fitted_process$mu
+      cluster_area <- fitted_process$clustpar[["scale"]] ^ 2 * pi
+
+      param_vec <- c(number_parents = number_parents, number_points = number_points,
+                     cluster_area = cluster_area)
+
+    }
+
     result <- lapply(seq_len(n_random), function(x) {
 
       # simulate clustered pattern
@@ -130,13 +148,20 @@ fit_point_process <- function(pattern,
 
   }
 
+  # set param to NA
+  if (!return_para) param_vec <- NA
+
   # set names
   names(result) <- paste0("randomized_", seq_len(n_random))
 
   # combine to one list
-  result <- list(randomized = result, observed = pattern,
-                 method = "fit_point_process()", energy_df = "NA",
-                 stop_criterion = "NA", iterations = iterations_list)
+  result <- list(randomized = result, observed = pattern, method = "fit_point_process()",
+                 energy_df = NA, stop_criterion = NA, iterations = NA, param = param_vec)
+
+  # add param
+  if (return_para) {
+    result$param <- param_vec
+  }
 
   # set class of result
   class(result) <- "rd_pat"
@@ -145,7 +170,7 @@ fit_point_process <- function(pattern,
   if (!return_input) {
 
     # set observed to NA
-    result$observed <- "NA"
+    result$observed <- NA
 
     # check if output should be simplified
     if (simplify) {

R/list_to_randomized.R

@@ -64,20 +64,15 @@
   # return observed if present or NA if not
   if (is.null(observed)) {
 
-    observed <- "NA"
+    observed <- NA
 
   }
 
   # set names
   names(list) <- paste0("randomized_", seq_along(list))
 
-  # create empty iterations list
-  iterations_list <- as.list(rep(NA, times = length(list)))
-
   # combine to one list
-  result <- list(randomized = list, observed = observed,
-                 method = "list_to_randomized()", energy_df = "NA",
-                 stop_criterion = "NA", iterations = iterations_list)
+  result <- list(randomized = list, observed = observed, method = "list_to_randomized()")
 
   class(result) <- result_class
 

R/plot_energy.R

@@ -39,7 +39,7 @@ plot_energy <- function(pattern,
 
   }
 
-  if (all(pattern$energy_df == "NA")) {
+  if (!is.list(pattern$energy_df)) {
 
     stop("There is no 'energy_df' slot. Please use pattern reconstruction for valid input data.",
          call. = FALSE)

R/print.rd_mar.R

@@ -33,7 +33,7 @@
 
     number_patterns_obs <- 0
 
-    includes_observed <- "NA"
+    includes_observed <- NA
 
   # observed pattern is present
   } else {
@@ -53,7 +53,7 @@
   number_patterns <- length(x$randomized) + number_patterns_obs
 
   # calculate mean iterations
-  mean_iterations <- round(mean(unlist(x$iterations)), digits = digits)
+  mean_iterations <- round(mean(x$iterations), digits = digits)
 
   # count stop criterions
   stop_criterion <- tryCatch(expr = table(do.call(c, x$stop_criterion), useNA = "ifany"),
@@ -75,7 +75,7 @@
 
   } else {
 
-    mean_energy <- "NA"
+    mean_energy <- NA
 
   }
 

R/print.rd_pat.R

@@ -36,7 +36,7 @@
 
     number_patterns_obs <- 0
 
-    includes_observed <- "NA"
+    includes_observed <- NA
 
   # observed pattern is present
   } else {
@@ -55,7 +55,7 @@
   number_patterns <- length(x$randomized) + number_patterns_obs
 
   # calculate mean iterations
-  mean_iterations <- round(mean(unlist(x$iterations)), digits = digits)
+  mean_iterations <- round(mean(x$iterations), digits = digits)
 
   # count stop criterions
   stop_criterion <- tryCatch(expr = table(do.call(c, x$stop_criterion), useNA = "ifany"),
@@ -77,7 +77,7 @@
 
   } else {
 
-    mean_energy <- "NA"
+    mean_energy <- NA
 
   }
 

R/print.rd_ras.R

@@ -37,7 +37,7 @@ print.rd_ras <- function(x, ...) {
 
     number_raster_obs <- 0
 
-    includes_observed <- "NA"
+    includes_observed <- NA
 
   # observed pattern is present
   } else {

R/randomize_raster.R

@@ -193,7 +193,7 @@ randomize_raster <- function(raster,
   if (!return_input) {
 
     # set observed to NA
-    randomization$observed <- "NA"
+    randomization$observed <- NA
 
     # check if output should be simplified
     if (simplify) {

R/reconstruct_algorithm.R

@@ -24,19 +24,9 @@
 #' @return list
 #'
 #' @keywords internal
-reconstruct_algorithm <- function(pattern,
-                                  method,
-                                  n_random,
-                                  e_threshold,
-                                  max_runs,
-                                  no_change,
-                                  annealing,
-                                  weights,
-                                  r_length,
-                                  r_max,
-                                  stoyan,
-                                  verbose,
-                                  plot){
+reconstruct_algorithm <- function(pattern, method, n_random, e_threshold, max_runs,
+                                  no_change, annealing, weights, r_length, r_max,
+                                  stoyan, verbose, plot){
 
   # check if n_random is >= 1
   if (n_random < 1) {
@@ -90,14 +80,14 @@ reconstruct_algorithm <- function(pattern,
 
   # create empty lists for results
   energy_list <- vector("list", length = n_random)
-  iterations_list <- vector("list", length = n_random)
-  stop_criterion_list <- as.list(rep("max_runs", times = n_random))
+  iterations_vec <- vector(mode = "numeric", length = n_random)
+  stop_criterion_vec <- rep("max_runs", times = n_random)
   result_list <- vector("list", length = n_random)
 
   # set names
   names(energy_list) <- names_randomization
-  names(iterations_list) <- names_randomization
-  names(stop_criterion_list) <- names_randomization
+  names(iterations_vec) <- names_randomization
+  names(stop_criterion_vec) <- names_randomization
   names(result_list) <- names_randomization
 
   # calculate summary functions
@@ -286,8 +276,8 @@ reconstruct_algorithm <- function(pattern,
       if (energy <= e_threshold || energy_counter > no_change) {
 
         # set stop criterion due to energy
-        stop_criterion_list[[i]] <- ifelse(test = energy <= e_threshold,
-                                           yes = "e_threshold", no = "no_change")
+        stop_criterion_vec[i] <- ifelse(test = energy <= e_threshold,
+                                        yes = "e_threshold", no = "no_change")
 
         break
 
@@ -302,15 +292,15 @@ reconstruct_algorithm <- function(pattern,
     }
 
     # remove NAs if stopped due to energy
-    if (stop_criterion_list[[i]] %in% c("e_threshold", "no_change")) {
+    if (stop_criterion_vec[i] %in% c("e_threshold", "no_change")) {
 
       energy_df <- energy_df[1:j, ]
 
     }
 
     # save results in lists
     energy_list[[i]] <- energy_df
-    iterations_list[[i]] <- j
+    iterations_vec[i] <- j
     result_list[[i]] <- simulated
 
   }
@@ -321,8 +311,8 @@ reconstruct_algorithm <- function(pattern,
   # combine to one list
   reconstruction <- list(randomized = result_list, observed = pattern,
                          method = method, energy_df = energy_list,
-                         stop_criterion = stop_criterion_list,
-                         iterations = iterations_list)
+                         stop_criterion = stop_criterion_vec,
+                         iterations = iterations_vec, param = NA)
 
   return(reconstruction)
 }

R/reconstruct_pattern.R

@@ -81,20 +81,11 @@
 #' ecology. Chapman and Hall/CRC Press, Boca Raton. ISBN 978-1-4200-8254-8
 #'
 #' @export
-reconstruct_pattern <- function(pattern, method = "homo",
-                                n_random = 1,
-                                e_threshold = 0.01,
-                                max_runs,
-                                no_change = Inf,
-                                annealing = 0.01,
-                                weights = c(1, 1),
-                                r_length = 255,
-                                r_max = NULL,
-                                stoyan = 0.15,
-                                return_input = TRUE,
-                                simplify = FALSE,
-                                verbose = TRUE,
-                                plot = FALSE) {
+reconstruct_pattern <- function(pattern, method = "homo", n_random = 1, e_threshold = 0.01,
+                                max_runs = 10000, no_change = Inf, annealing = 0.01,
+                                weights = c(1, 1), r_length = 255, r_max = NULL,
+                                stoyan = 0.15, return_input = TRUE, simplify = FALSE,
+                                verbose = TRUE, plot = FALSE) {
 
   # check if correct method is selected
   if (!method %in% c("homo", "hetero", "cluster")) stop("Method must be one of the following: 'homo', 'hetero', or 'cluster'.",
@@ -113,7 +104,7 @@ reconstruct_pattern <- function(pattern, method = "homo",
   if (!return_input) {
 
     # set observed to NA
-    reconstruction$observed <- "NA"
+    reconstruction$observed <- NA
 
     # check if output should be simplified
     if (simplify) {