summarizeFarmsVariational {cn.farms} | R Documentation

## Summarization variational Laplacian approach

### Description

This function runs the FARMS algorithm.

### Usage

    summarizeFarmsVariational(probes, weight = 0.15, mu = 0, cyc = 10,
      weightType = "median", init = 0.6, correction = 0, minNoise = 0.35,
      spuriousCorrelation = 0.3, centering = "median")

### Arguments

`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.
`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)
`spuriousCorrelation` | Numeric value for suppression of spurious correlation.
`minNoise` | States the minimal noise. Default is 0.35.
`centering` | States how the data is centered. Default is median.

### Value

A list containing the results of the run.

### Author(s)

Djork-Arne Clevert okko@clevert.de and Andreas Mitterecker mitterecker@bioinf.jku.at

### Examples

    x <- matrix(rnorm(100, 11), 20, 5)
    summarizeFarmsVariational(x)

[Package cn.farms version 1.40.0 Index]