apply.ldf: Applies a canonical correlation transformation to the data
In CNVtools: A package to test genetic association with CNV data

Applies a canonical correlation transformation to the combination of the raw signal intensities with an initial set of posterior probabilities.

1	apply.ldf(full.signal, posterior)

`full.signal`	A matrix with the raw signal intensity. One row per data point or sample in the data, and one column for the probability of each call. The matrix MUST have row names.
`posterior`	A matrix of posterior distribution for the calls. This matrix must have row names that match the signal intensity. The ordering does not have to be the same as the matrix of signals but each data point in “full.signal” must have a corresponding set of posterior probabilities.

Do not forget to add row names to both matrices.

A one-dimensional vector with the transformed canonical corelation transformed values.

CNVtools documentation built on April 28, 2020, 6:06 p.m.