Added documentation to knnDE function

R/knnDE.R

@@ -1,20 +1,39 @@
+#' @param X an n by d matrix of coordinates containing points used int the
+#' density estimation, where n is the number of points and d is the dimension
+#' @param Grid an m by d matrix of coordinates, where m is the number of points
+#' in the grid and d is the dimension
+#' @param k a number used as the smoothing parameter
+#' @return a vector of length m (number of points in Grid) containing the value
+#' of the knn Density Estimator for each point in the grid
+
 knnDE <-
 function(X, Grid, k){
-
+
+  # ensure input X is a matrix of numbers
   if (!is.numeric(X) && !is.data.frame(X)) {
     stop("X should be a matrix of coordinates")
   }
+  # ensure input Grid is a matrix of numbers
   if (!is.numeric(Grid) && !is.data.frame(Grid)) {
     stop("Grid should be a matrix of coordinates")
   }
+  # ensure that k is a positive integer
   if (!is.numeric(k) || length(k) != 1 || k < 1) {
     stop("k should be a positive integer")
   }
 
+  # store the number of rows and columns in input matrix X
+  # (d being the dimension and n being number of points in X)
   d <- ncol(X)
   n <- nrow(X)
+  # find the Euclidean distances from k nearest neighbors using input matrix X, the query matrix
+  # Grid, a maximum number n of nearest neighbors to search, and the nearest neighbor searching
+  # algorithm ("kd_tree")
   r.k <- apply(FNN::knnx.dist(X, Grid, k = k, algorithm = "kd_tree"), 1, max)
+  # volume of the Euclidean d dimensional unit ball
   v.d <- pi^(d/2) /gamma(d/2+1)
+  # function in its final form, dividing smoothing parameter k by the number of points in X multiplied 
+  # by the volume of unit ball and the Euclidean distances from k nearest neighbors to the power of d dimensions
   out <- k / (n * v.d * r.k ^ d)  
   return(out)
-}
+}