knnDE {TDA} | R Documentation |
k Nearest Neighbors Density Estimator over a Grid of Points
Description
Given a point cloud X
(n
points), The function knnDE
computes the k Nearest Neighbors Density Estimator over a grid of points. For each x \in R^d
, the knn Density Estimator is defined by
p_X(x)=\frac{k}{n \; v_d \; r_k^d(x)},
where v_n
is the volume of the Euclidean d
dimensional unit ball and r_k^d(x)
is the Euclidean distance from point x to its k
'th closest neighbor.
Usage
knnDE(X, Grid, k)
Arguments
X |
an |
Grid |
an |
k |
number: the smoothing paramter of the k Nearest Neighbors Density Estimator. |
Value
The function knnDE
returns a vector of length m
(the number of points in the grid) containing the value of the knn Density Estimator for each point in the grid.
Author(s)
Fabrizio Lecci
See Also
kde
, kernelDist
, distFct
, dtm
Examples
## Generate Data from the unit circle
n <- 300
X <- circleUnif(n)
## Construct a grid of points over which we evaluate the function
by <- 0.065
Xseq <- seq(-1.6, 1.6, by = by)
Yseq <- seq(-1.7, 1.7, by = by)
Grid <- expand.grid(Xseq, Yseq)
## kernel density estimator
k <- 50
KNN <- knnDE(X, Grid, k)