l.farms {farms}R Documentation

l.farms expression measure

Description

This function converts an instance of AffyBatch-class into an instance of exprSet-class using a factor analysis model for which a Bayesian Maximum a Posteriori method optimizes the model parameters under the assumption of Gaussian measurement noise. This function is a wrapper for expresso and uses the function normalize.loess for array normalization.

Usage

          l.farms(object, weight, mu, weighted.mean, robust, ...)
          

Arguments

object An instance of AffyBatch.Rdash.class.
weight Hyperparameter value in the range of [0,1] which determines the influence of the prior. The default value is 0.5
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
weighted.mean Boolean flag, that indicates wether a weighted mean or a least square fit is used to summarize the loading matrix. The default value is set to TRUE .
robust Boolean flag, that ensures non-constant results. Default value is TRUE.
... other arguments to be passed to expresso.

Details

This function is a wrapper for expresso.

Value

exprSet-class

See Also

expresso, exp.farms, q.farms, normalize.loess

Examples

  data(Dilution)
  eset <- l.farms(Dilution,  weight=0.5, weighted.mean=TRUE)

[Package farms version 1.4.0 Index]