l.farms {farms} | R Documentation |
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.
l.farms(object, weight, mu, weighted.mean, robust, ...)
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 . |
This function is a wrapper for expresso
.
expresso
, exp.farms
, q.farms
, normalize.loess
data(Dilution) eset <- l.farms(Dilution, weight=0.5, weighted.mean=TRUE)