A script that validates that data inputs are correct, and returns a X distance and Y distance matrix for MGC.
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 .
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Y |
[n] a vector containing the sample ids for our n samples.
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is.dist.X |
a boolean indicating whether your X input is a distance matrix or not. Defaults to FALSE .
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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.
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dist.params.X |
a list of trailing arguments to pass to the distance function specified in dist.xfm.X .
Defaults to list(method='euclidean') .
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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.
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is.dist.Y |
a boolean indicating whether your Y input is a distance matrix or not. Defaults to FALSE .
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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.
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dist.params.Y |
a list of trailing arguments to pass to the distance function specified in dist.xfm.Y .
Defaults to list(method='euclidean') .
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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.
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