nndist-methods {pRoloc} | R Documentation |
Methods computing the nearest neighbour indices and distances for
matrix
and MSnSet
instances.
signature(object = "matrix", k = "numeric", dist =
"character", ...)
Calculates indices and distances to the
k
(default is 3) nearest neighbours of each feature (row)
in the input matrix object
. The distance dist
can
be either of "euclidean"
or
"mahalanobis"
. Additional parameters can be passed to the
internal function FNN::get.knn
. Output is a matrix with
2 * k
columns and nrow(object)
rows.
signature(object = "MSnSet", k = "numeric", dist =
"character", ...)
As above, but for an MSnSet
input. The indices and distances to the k
nearest
neighbours are added to the object's feature metadata.
signature(object = "matrix", query = "matrix", k =
"numeric", ...)
If two matrix
instances are provided as
input, the k
(default is 3) indices and distances of the
nearest neighbours of query
in object
are returned
as a matrix of dimensions 2 * k
by
nrow(query)
. Additional parameters are passed to
FNN::get.knnx
. Only euclidean distance is available.
library("pRolocdata") data(dunkley2006) ## Using a matrix as input m <- exprs(dunkley2006) m[1:4, 1:3] head(nndist(m, k = 5)) tail(nndist(m[1:100, ], k = 2, dist = "mahalanobis")) ## Same as above for MSnSet d <- nndist(dunkley2006, k = 5) head(fData(d)) d <- nndist(dunkley2006[1:100, ], k = 2, dist = "mahalanobis") tail(fData(d)) ## Using a query nndist(m[1:100, ], m[101:110, ], k = 2)