summarizeFarmsGaussian: Summarization Gaussian approach
In mitterecker/cn.farms: cn.FARMS - factor analysis for copy number estimation

Description Usage Arguments Value Author(s) Examples

This function runs the FARMS algorithm.

1
2
3

summarizeFarmsGaussian(probes, weight = 0.15, mu = 0, cyc = 10,
  tol = 1e-04, weightType = "mean", init = 0.6, correction = 0,
  minNoise = 0.35, centering = "median", refIdx)

`probes`	A matrix with numeric values.
`weight`	Hyperparameter value in the range of [0,1] which determines the influence of the prior.
`mu`	Hyperparameter value which allows to quantify different aspects of potential prior knowledge. Values near zero assumes that most genes do not contain a signal, and introduces a bias for loading matrix elements near zero. Default value is 0.
`cyc`	Number of cycles for the EM algorithm.
`tol`	States the termination tolerance. Default is 0.00001.
`weightType`	Flag, that is used to summarize the loading matrix. The default value is set to mean.
`init`	Parameter for estimation.
`correction`	Value that indicates whether the covariance matrix should be corrected for negative eigenvalues which might emerge from the non-negative correlation constraints or not. Default = O (means that no correction is done), 1 (minimal noise (0.0001) is added to the diagonal elements of the covariance matrix to force positive definiteness), 2 (Maximum Likelihood solution to compute the nearest positive definite matrix under the given non-negative correlation constraints of the covariance matrix)
`minNoise`	States the minimal noise. Default is 0.35.
`centering`	States how the data is centered. Default is median.
`refIdx`	index or indices which are used for computation of the centering