discr.validator {mgc} | R Documentation |
Discriminability Utility Validator
Description
A script that validates that data inputs are correct, and returns a distance matrix and a ids vector.
Usage
discr.validator(
X,
Y,
is.dist = FALSE,
dist.xfm = mgc.distance,
dist.params = list(method = "euclidean"),
dist.return = NULL,
remove.isolates = TRUE
)
Arguments
X |
is interpreted as:
- a
[n x d] data matrix X is a data matrix with n samples in d dimensions, if flag is.dist=FALSE .
- a
[n x n] distance matrix X is a distance matrix. Use flag is.dist=TRUE .
|
Y |
is interpreted as:
- a
[n x d] data matrix Y is a data matrix with n samples in d dimensions, if flag is.dist=FALSE .
- a
[n x n] distance matrix Y is a distance matrix. Use flag is.dist=TRUE .
|
is.dist |
a boolean indicating whether your X input is a distance matrix or not. Defaults to FALSE .
|
dist.xfm |
if is.dist == FALSE , a distance function to transform X . If a distance function is passed,
it should accept an [n x d] matrix of n samples in d dimensions and return a [n x n] distance matrix
as the $D return argument. See mgc.distance for details.
|
dist.params |
a list of trailing arguments to pass to the distance function specified in dist.xfm .
Defaults to list(method='euclidean') .
|
dist.return |
the return argument for the specified dist.xfm containing the distance matrix. Defaults to FALSE .
is.null(dist.return) use the return argument directly from dist.xfm as the distance matrix. Should be a [n x n] matrix.
is.character(dist.return) | is.integer(dist.return) use dist.xfm[[dist.return]] as the distance matrix. Should be a [n x n] matrix.
|
remove.isolates |
whether to remove isolated samples, or samples with only a single instance in the Y vector.
|
Value
A list containing the following:
DX |
The X distance matrix, as a [n x n] matrix.
|
Y |
The sample ids, with isolates removed.
|
[Package
mgc version 2.0.2
Index]