bw.nn: nearest-neighbor adaptive bandwidth selection
In grady/kdetrees: Nonparametric method for identifying discordant phylogenetic trees
Description
Usage
Arguments
Value
Author(s)
Examples
Description
For each row in pairwise distance matrix find the distance to the
closest prop fraction of trees.
Usage
1
bw.nn(x, prop = 0.2, tol = 1e-06)
Arguments
x
pairwise distance matrix
prop
fraction of data to define the local neighborhood
tol
tolerance for zero-bandwidth check
Value
a vector of bandwidths for each tree (row) in x
Author(s)
Grady Weyenberg
Examples
1
2
dm <- as.matrix(dist.diss(apicomplexa[1:20]))
bw.nn(dm)
grady/kdetrees documentation built on May 17, 2019, 8:01 a.m.
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Description Usage Arguments Value Author(s) Examples
Description
For each row in pairwise distance matrix find the distance to the closest prop fraction of trees.
Usage
1 | bw.nn(x, prop = 0.2, tol = 1e-06)
|
Arguments
x |
pairwise distance matrix |
prop |
fraction of data to define the local neighborhood |
tol |
tolerance for zero-bandwidth check |
Value
a vector of bandwidths for each tree (row) in x
Author(s)
Grady Weyenberg
Examples
1 2 | dm <- as.matrix(dist.diss(apicomplexa[1:20]))
bw.nn(dm)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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