Usage
expFarms(object, bgcorrect.method = "none", pmcorrect.method = "pmonly",
normalize.method = "quantiles", weight, mu, weighted.mean, laplacian, robust, correction, centering, spuriousCorrelation, ...)Arguments
weight
Hyperparameter value in the range of [0,1] which determines the influence of the prior. The default value is 0.5
bgcorrect.method
the name of the background adjustment method
pmcorrect.method
the name of the PM adjustment method
normalize.method
the normalization method to use
mu
Hyper-parameter 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
weighted.mean
Boolean flag, that indicates whether a weighted mean or a least square fit is used to summarize the loading matrix. The default value is set to FALSE.
laplacian
Boolean flag, indicates whether a Laplacian prior for the factor is employed or not. Default value is FALSE.
robust
Boolean flag, that ensures non-constant results. Default value is TRUE.
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)
centering
Indicates whether the data is "median" or "mean" centered. Default value is "median".
spuriousCorrelation
Numeric value in the range of [0,1] that quantifies the suppression of spurious correlation when using the Laplacian prior.
Default value is 0 (no suppression). Note, that this parameter is only active when the laplacian parameter is set to TRUE.
...
other arguments to be passed to expresso.