exp.farms {farms} | R Documentation |
This function converts an instance of AffyBatch-class{AffyBatch.Rdash.class}
into an instance of exprSet-class{exprSet.Rdash.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.
exp.farms(object, bgcorrect.method = "none", pmcorrect.method = "pmonly", normalize.method = "quantiles", weight, mu, scale, cyc, tol,...)
object |
An instance of AffyBatch-class{AffyBatch.Rdash.class} . |
weight |
Hyperparameter value which determines the influence of the prior. |
bgcorrect.method |
the name of the background adjustment method |
pmcorrect.method |
the name of the PM adjustement method |
normalize.method |
the normalization method to use |
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
.
data(affybatch.example) eset <- exp.farms(affybatch.example, weight=8, scale=1.5)