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.