summarizeFarmsVariational: Summarization variational Laplacian approach

Description Usage Arguments Value Author(s) Examples

View source: R/summarizeFarmsLaplaceVar.R

Description

This function runs the FARMS algorithm.

Usage

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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 [email protected] and Andreas Mitterecker [email protected]

Examples

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

Bioconductor-mirror/cn.farms documentation built on June 1, 2017, 6:35 a.m.