rangeFind methods {BiocNeighbors} | R Documentation

## Find all neighbors in range

### Description

Find all neighboring data points within a certain distance of each point.

### Usage

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

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

### Arguments

`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. Alternatively, a vector containing a different distance threshold for each point.
`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` | A BiocNeighborIndex object of the appropriate class, generated from `X`. For `rangeFindKmknn`, this should be a KmknnIndex from `rangeFindKmknn`. For `rangeFindVptree`, this should be a VptreeIndex from `rangeFindVptree`.
`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 should be returned, see `?"BiocNeighbors-raw-index"`.
`...` | Further arguments to pass to the respective `build*` function for each algorithm.

### Details

This function identifies 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. The exact implementation can either use the KMKNNN approach or a VP tree.

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`.

If `threshold` is a vector, each entry is assumed to specify a (possibly different) threshold for each point in `X`. If `subset` is also specified, each entry is assumed to specify a threshold for each point in `subset`. An error will be raised if `threshold` is a vector of incorrect length.

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 build the index from `X` (e.g., with `buildKmknn`). The resulting BiocNeighborIndex object can be supplied as `precomputed` to multiple function calls, avoiding the need to repeat index construction in each call. Note that when `precomputed` is supplied, the value of `X` is ignored.

### Value

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`.

If `subset` is not `NULL`, each row of the above matrices refers to a point in the subset, in the same order as supplied in `subset`.

See `?"BiocNeighbors-raw-index"` for an explanation of the output when `raw.index=TRUE`.

### Author(s)

Aaron Lun

### See Also

`buildKmknn` or `buildVptree` to build an index ahead of time.

See `?"BiocNeighbors-algorithms"` for an overview of the available algorithms.

### Examples

    Y <- matrix(runif(100000), ncol=20)
    out <- rangeFindKmknn(Y, threshold=1)
    out2 <- rangeFindVptree(Y, threshold=1)

[Package BiocNeighbors version 1.2.0 Index]