summarizeFarmsVariational {cn.farms} | R Documentation |
This function runs the FARMS algorithm.
summarizeFarmsVariational(probes, weight = 0.15, mu = 0, cyc = 10, weightType = "median", init = 0.6, correction = 0, minNoise = 0.35, spuriousCorrelation = 0.3, centering = "median")
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. |
A list containing the results of the run.
Djork-Arne Clevert okko@clevert.de and Andreas Mitterecker mitterecker@bioinf.jku.at
x <- matrix(rnorm(100, 11), 20, 5) summarizeFarmsVariational(x)