improve docs of k arg of nn2

* closes #36

Author: jefferis

DESCRIPTION

@@ -30,4 +30,4 @@ BugReports: https://github.com/jefferislab/RANN/issues
 Suggests:
     testthat
 Encoding: UTF-8
-RoxygenNote: 7.3.2
+RoxygenNote: 7.3.3

R/nn.R

@@ -6,52 +6,54 @@
 
 #'Nearest Neighbour Search
 #'
-#'Uses a kd-tree to find the p number of near neighbours for each point in an 
-#'input/output dataset. The advantage of the kd-tree is that it runs in O(M log 
+#'Uses a kd-tree to find the p number of near neighbours for each point in an
+#'input/output dataset. The advantage of the kd-tree is that it runs in O(M log
 #'M) time.
 #'
-#'The \code{RANN} package utilizes the Approximate Near Neighbor (ANN) C++ 
-#'library, which can give the exact near neighbours or (as the name suggests) 
-#'approximate near neighbours to within a specified error bound.  For more 
-#'information on the ANN library please visit 
+#'The \code{RANN} package utilizes the Approximate Near Neighbor (ANN) C++
+#'library, which can give the exact near neighbours or (as the name suggests)
+#'approximate near neighbours to within a specified error bound.  For more
+#'information on the ANN library please visit
 #'\url{https://www.cs.umd.edu/~mount/ANN/}.
 #'
-#'Search types: \code{priority} visits cells in increasing order of distance 
-#'from the query point, and hence, should converge more rapidly on the true 
-#'nearest neighbour, but standard is usually faster for exact searches. 
-#'\code{radius} only searches for neighbours within a specified radius of the 
-#'point.  If there are no neighbours then nn.idx will contain 0 and nn.dists 
+#'Search types: \code{priority} visits cells in increasing order of distance
+#'from the query point, and hence, should converge more rapidly on the true
+#'nearest neighbour, but standard is usually faster for exact searches.
+#'\code{radius} only searches for neighbours within a specified radius of the
+#'point.  If there are no neighbours then nn.idx will contain 0 and nn.dists
 #'will contain 1.340781e+154 for that point.
 #'
-#'@param data An \bold{M} x \bold{d} data.frame or matrix, where each of the 
+#'@param data An \bold{M} x \bold{d} data.frame or matrix, where each of the
 #'  \bold{M} rows is a point or a (column) vector (where \bold{d=1}).
-#'@param query A set of \bold{N} x \bold{d} points that will be queried against 
-#'  \code{data}. \bold{d}, the number of columns, must be the same as 
+#'@param query A set of \bold{N} x \bold{d} points that will be queried against
+#'  \code{data}. \bold{d}, the number of columns, must be the same as
 #'  \code{data}. If missing, defaults to \code{data}.
-#'@param k The maximum number of nearest neighbours to compute. The default 
-#'  value is set to the smaller of the number of columnns in data
+#'@param k The maximum number of nearest neighbours to compute. The default
+#'  value is set to the smaller of the number of data points (rows) and 10. Note
+#'  that when data and query are the same then the nearest neighbour will be the
+#'  query point itself.
 #'@param treetype Character vector specifying the standard \code{'kd'} tree or a
-#'  \code{'bd'} (box-decomposition, AMNSW98) tree which may perform better for 
+#'  \code{'bd'} (box-decomposition, AMNSW98) tree which may perform better for
 #'  larger point sets
 #'@param searchtype See details
 #'@param radius Radius of search for searchtype='radius'
 #'@param eps Error bound: default of 0.0 implies exact nearest neighbour search
 #'@return A \code{list} of length 2 with elements:
-#'  
+#'
 #'  \item{nn.idx}{A \bold{N} x \bold{k} integer \code{matrix} returning the near
 #'  neighbour indices.}
-#'  
-#'  \item{nn.dists}{A \bold{N} x \bold{k} \code{matrix} returning the near 
+#'
+#'  \item{nn.dists}{A \bold{N} x \bold{k} \code{matrix} returning the near
 #'  neighbour Euclidean distances.}
-#'@author Gregory Jefferis based on earlier code by Samuel E. Kemp (knnFinder 
+#'@author Gregory Jefferis based on earlier code by Samuel E. Kemp (knnFinder
 #'  package)
-#'@references Bentley J. L. (1975), Multidimensional binary search trees used 
+#'@references Bentley J. L. (1975), Multidimensional binary search trees used
 #'  for associative search. Communication ACM, 18:309-517.
-#'  
-#'  Arya S. and Mount D. M. (1993), Approximate nearest neighbor searching, 
+#'
+#'  Arya S. and Mount D. M. (1993), Approximate nearest neighbor searching,
 #'  Proc. 4th Ann. ACM-SIAM Symposium on Discrete Algorithms (SODA'93), 271-280.
-#'  
-#'  Arya S., Mount D. M., Netanyahu N. S., Silverman R. and Wu A. Y (1998), An 
+#'
+#'  Arya S., Mount D. M., Netanyahu N. S., Silverman R. and Wu A. Y (1998), An
 #'  optimal algorithm for approximate nearest neighbor searching, Journal of the
 #'  ACM, 45, 891-923.
 #'@keywords nonparametric