adding missing arguments

R/calkmeans.R

@@ -247,6 +247,14 @@ calkmeans <- function(img, k = NULL, iter.max = 10, algo = c("Hartigan-Wong", "L
 
 #' Plot for the "calkmeans" class
 #'
+#' @param x Object of class "calkmeans".
+#' @param xlab X-axis title.
+#' @param ylab Y-axis title.
+#' @param type Type of line.
+#' @param main Title of the graph.
+#' @param cex Graph line point size.
+#' @param ... Graphical parameters to be passed in \link[base]{plot}.
+#'
 #' @export
 #'
 plot.calkmeans <- function(x, xlab, ylab, type, main, cex, ...){

R/calmla.R

@@ -66,6 +66,8 @@
 #' Cross-Validation (K-fold) approach.
 #' @param iter Number of iterations, i.e number of times the analysis is executed.
 #' @param verbose This paramater is Logical. It Prints progress messages during execution.
+#' @param ... Parameters to be passed in machine learning algorithm. Please see \link[e1071]{svm}, \link[randomForest]{randomForest},
+#' \link[e1071]{naiveBayes}, \link[caret]{train}, \link[nnet]{nnet} and \link[caret]{knn3}.
 #'
 #' @examples
 #' library(ForesToolboxRS)

R/fusionRS.R

@@ -120,6 +120,8 @@ fusionRS <- function(x, y, stand.varb = TRUE, na = FALSE, verbose = FALSE) {
 
 #' Print for the "fusionRS" class
 #'
+#' @param x Object of class "fusionRS".
+#'
 #' @export
 #'
 print.fusionRS <- function(x){

R/mla.R

@@ -47,6 +47,8 @@
 #' @param training_split For splitting samples into two subsets, i.e. training data and for
 #' testing data.
 #' @param verbose This paramater is Logical. It Prints progress messages during execution.
+#' @param ... Parameters to be passed in machine learning algorithm. Please see \link[e1071]{svm}, \link[randomForest]{randomForest},
+#' \link[e1071]{naiveBayes}, \link[caret]{train}, \link[nnet]{nnet} and \link[caret]{knn3}.
 #'
 #' @examples
 #' library(ForesToolboxRS)
@@ -211,6 +213,8 @@ mla <- function(img, endm, model, training_split = 80, verbose = FALSE, ...){
 
 #' Print for the "mla" class
 #'
+#' @param x Object of class "mla".
+#'
 #' @export
 #'
 print.mla <- function(x){

R/plot.pvts.R

@@ -2,6 +2,15 @@
 #'
 #' @importFrom grDevices adjustcolor
 #'
+#' @param x Object of class "pvts".
+#' @param ylab Y-axis title.
+#' @param type Type of line.
+#' @param col Type of color line.
+#' @param lwd Line thickness size.
+#' @param main Title of the graph.
+#' @param cex Graph line point size.
+#' @param ... Graphical parameters to be passed in \link[base]{plot}.
+#'
 #' @export
 #'
 

R/print.pvts.R

@@ -1,9 +1,11 @@
 #' Printing for the "pvts" class
 #'
+#' @param x Object of class "pvts".
+#'
 #' @export
 #'
 
-print.pvts <- function(x, ...) {
+print.pvts <- function(x) {
   cat("\n  NON-SEASONAL DETECTION APPROACH
         PVts-Beta Approach\n")
   if(!is.na(x$Breakpoint[1])) {

R/rkmeans.R

@@ -37,14 +37,27 @@
 #'
 #' @export
 #'
-rkmeans <- function(img, k, iter.max = 100, nstart = 50, algo = c("Hartigan-Wong", "Lloyd", "Forgy", "MacQueen"), ...){
+rkmeans <- function(img, k, iter.max = 100, nstart = 50, algo = c("Hartigan-Wong", "Lloyd", "Forgy", "MacQueen"), verbose = FALSE, ...){
 
   vr <- getValues(img)
+
+  if(verbose){
+    message(paste0(paste0(rep("*",10), collapse = ""), " Getting NA positions ", paste0(rep("*",10), collapse = "")))
+  }
+
   i <- which(!is.na(vr)) # NA positions
 
+  if(verbose){
+    message(paste0(paste0(rep("*",10), collapse = ""), " Appling kmeans ", paste0(rep("*",10), collapse = "")))
+  }
+
   # Applying kmeans
   km <- kmeans(na.omit(vr), k, iter.max, nstart, algorithm = algo, ...)
 
+  if(verbose){
+    message(paste0(paste0(rep("*",10), collapse = ""), " Obtained raster ", paste0(rep("*",10), collapse = "")))
+  }
+
   raster_class <- raster(img)
   raster_class[i] <- km$cluster
 

R/smootH.R

@@ -29,7 +29,7 @@
 #' @importFrom zoo na.approx na.spline
 #'
 #' @param x Numeric, matrix.
-#' @param interp Four interpolation methods are presented, "na.interp",
+#' @param method.interp Four interpolation methods are presented, "na.interp",
 #' "na.approx" and "na.spline". By default is the method "na.interp".
 #'
 #' @examples