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=8, mu=0, scale=1.5, cyc=100, tol=0.00001,...)
object |
An instance of AffyBatch-class{AffyBatch.Rdash.class} . |
weight |
Hyperparameter value 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. |
scale |
Value which compensates for the reduction of variance during preprocessing and factor analysis (some of the data variance is explained by the noise). |
cyc |
Value which determinates the maximum numbers of EM-Steps. |
tol |
Value which determinates the termination tolerance. |
... |
other arguments to be passed to expresso{expresso} . |
This function is a wrapper for expresso
.
expresso
, exp.farms
, q.farms
, normalize.loess
data(affybatch.example) eset <- l.farms(affybatch.example)