mgc.validator: MGC Utility Validator

Description

A script that validates that data inputs are correct, and returns a X distance and Y distance matrix for MGC.

Usage

mgc.validator(
  X,
  Y,
  is.dist.X = FALSE,
  dist.xfm.X = mgc.distance,
  dist.params.X = list(method = "euclidean"),
  dist.return.X = NULL,
  is.dist.Y = FALSE,
  dist.xfm.Y = mgc.distance,
  dist.params.Y = list(method = "euclidean"),
  dist.return.Y = NULL
)

Arguments

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.

[n] a vector containing the sample ids for our n samples.

is.dist.X

a boolean indicating whether your X input is a distance matrix or not. Defaults to FALSE.

dist.xfm.X

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.X

a list of trailing arguments to pass to the distance function specified in dist.xfm.X. Defaults to list(method='euclidean').

dist.return.X

the return argument for the specified dist.xfm.X 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.X[[dist.return]] as the distance matrix. Should be a [n x n] matrix.

is.dist.Y

a boolean indicating whether your Y input is a distance matrix or not. Defaults to FALSE.

dist.xfm.Y

if is.dist == FALSE, a distance function to transform Y. 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 dist.return.Y return argument. See mgc.distance for details.

dist.params.Y

a list of trailing arguments to pass to the distance function specified in dist.xfm.Y. Defaults to list(method='euclidean').

dist.return.Y

the return argument for the specified dist.xfm.Y containing the distance matrix. Defaults to FALSE.

is.null(dist.return): use the return argument directly from dist.xfm.Y(Y) as the distance matrix. Should be a [n x n] matrix.
is.character(dist.return) | is.integer(dist.return): use dist.xfm.Y(Y)[[dist.return]] as the distance matrix. Should be a [n x n] matrix.

Value

A list containing the following:

D

The distance matrix, as a [n x n] matrix.

Y

the sample ids, as a [n] vector.