findNeighbors: Find all neighbors
In kmknn: k-Means for k-Nearest Neighbors

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/findNeighbors.R

Find all neighboring data points within a certain distance with the KMKNN algorithm.

1 2	findNeighbors(X, threshold, get.index=TRUE, get.distance=TRUE, BPPARAM=SerialParam(), precomputed=NULL, subset=NULL, raw.index=FALSE)

`X`	A numeric matrix where rows correspond to data points and columns correspond to variables (i.e., dimensions).
`threshold`	A positive numeric scalar specifying the maximum distance at which a point is considered a neighbor.
`get.index`	A logical scalar indicating whether the indices of the neighbors should be recorded.
`get.distance`	A logical scalar indicating whether distances to the neighbors should be recorded.
`BPPARAM`	A BiocParallelParam object indicating how the search should be parallelized.
`precomputed`	The precomputed output of `precluster` on `X`.
`subset`	A vector indicating the rows of `X` for which the neighbors should be identified.
`raw.index`	A logial scalar indicating whether raw column indices to `precomputed$data` should be returned.

This function uses the same algorithm described in findKNN to identify all points in X that within threshold of each point in X. For Euclidean distances, this is equivalent to identifying all points in a hypersphere centered around the point of interest.

By default, a search is performed for each data point in X, but it can be limited to a specified subset of points with subset. This yields the same result as (but is more efficient than) subsetting the output matrices after running findNeighbors with subset=NULL.

Turning off get.index or get.distance may provide a slight speed boost when these returned values are not of interest. Using BPPARAM will also split the search by query points, which usually provides a linear increase in speed.

If multiple queries are to be performed to the same X, it may be beneficial to use precluster directly and pass the result to precomputed. In such cases, it is also possible to set raw.index=TRUE to obtain the same result as calling findNeighbors on t(precomputed$data), though this will change both the nature of the output index and the interpretation of subset - see ?findKNN for details.

A list is returned containing:

index, if get.index=TRUE. This is a list of integer vectors where each entry corresponds to a point (denoted here as i) in X. The vector for i contains the set of row indices of all points in X that lie within threshold of point i. Points in each vector are not ordered, and i will always be included in its own set.
distance, if get.distance=TRUE. This is a list of numeric vectors where each entry corresponds to a point (as above) and contains the distances of the neighbors from i. Elements of each vector in distance match to elements of the corresponding vector in index.