kmeans fixed

R/auxiliary_functions.R

@@ -444,7 +444,7 @@ mah_dist <- function(x, y, cov) {
 #' @noRd
 #'
 kf <- function(df, n){
-  suppressWarnings(km <- stats::kmeans(env_changed %>% dplyr::select(-c(cell:y)),
+  suppressWarnings(km <- stats::kmeans(df %>% dplyr::select(-c(cell:y)),
                                        centers = n))
   result <- data.frame(cluster = km$cluster, cell = names(km$cluster)) %>%
     dplyr::as_tibble() %>%
@@ -454,7 +454,7 @@ kf <- function(df, n){
     dplyr::group_by() %>%
     dplyr::pull(cell) %>%
     as.numeric()
-  result <- env_changed %>%
+  result <- df %>%
     dplyr::filter(cell %in% result) %>%
     dplyr::select(x, y) %>%
     dplyr::as_tibble()

R/sample_pseudoabs.R

@@ -12,7 +12,7 @@
 #' provide a vector for this argument. The methods implemented are:
 #' \itemize{
 #' \item random: Random allocation of pseudo-absences throughout the area used for model fitting. Usage method='random'.
-#' \item kmeans: Pseudo-absences are sampled randomly within environmental clusters defined by k-means
+#' \item kmeans: Pseudo-absences are sampled randomly within environmental clusters defined by K-means
 #' cluster analysis (the number of clusters will equal than number of pseudo-absences defined in 'n').
 #' For this method, it is necessary to provide a raster object with environmental
 #' variables. Usage method = c('kmeans', env = somevar).
@@ -22,7 +22,7 @@
 #' \item geo_env_const: Pseudo-absences are constrained environmentally (based on Bioclim model) and distributed geographically far from occurrences based on a geographical buffer. For this method, a raster with environmental variables stored as SpatRaster object should be provided. A value of the buffer width in m must be provided if raster (used in rlayer) has a longitude/latitude CRS, or in map units in other cases. Usage method=c('geo_env_const', width='50000', env = somevar).
 #' \item geo_env_km_const: Pseudo-absences are constrained using a three-level procedure; it is similar to
 #' the geo_env_const with an additional step which distributes the pseudo-absences in environmental space
-#' using k-means cluster analysis. For this method, it is necessary to provide a raster object with
+#' using K-means cluster analysis. For this method, it is necessary to provide a raster object with
 #' environmental variables and a value of the buffer width in m if raster (used in rlayer) has a
 #' longitude/latitude CRS, or map units in other cases. Usage method=c('geo_env_km_const',
 #' width='50000', env = somevar).
@@ -105,7 +105,7 @@
 #' points(single_spp[-1], col = "blue", cex = 0.7, pch = 19)
 #' points(ps1, col = "red", cex = 0.7, pch = 19)
 #'
-#' # Pseudo-absences sampled with K-mean approach
+#' # Pseudo-absences sampled with K-means approach
 #' set.seed(123)
 #' ps1 <-
 #'   sample_pseudoabs(