AffinePlm: The AffinePlm class

Description

Package: aroma.affymetrix
Class AffinePlm

Object
~~|
~~+--ParametersInterface
~~~~~~~|
~~~~~~~+--Model
~~~~~~~~~~~~|
~~~~~~~~~~~~+--UnitModel
~~~~~~~~~~~~~~~~~|
~~~~~~~~~~~~~~~~~+--MultiArrayUnitModel
~~~~~~~~~~~~~~~~~~~~~~|
~~~~~~~~~~~~~~~~~~~~~~+--ProbeLevelModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~|
~~~~~~~~~~~~~~~~~~~~~~~~~~~+--AffinePlm

Directly known subclasses:
AffineCnPlm, AffineSnpPlm

public abstract static class AffinePlm
extends ProbeLevelModel

This class represents affine model in Bengtsson & Hossjer (2006).

Usage

AffinePlm(..., background=TRUE)

Arguments

...: Arguments passed to ProbeLevelModel.
background: If TRUE, background is estimate for each unit group, otherwise not. That is, if FALSE, a linear (proportional) model without offset is fitted, resulting in very similar results as obtained by the MbeiPlm.

Fields and Methods

Methods:

getProbeAffinityFile -

Methods inherited from ProbeLevelModel:
calculateResidualSet, calculateWeights, fit, getAsteriskTags, getCalculateResidualsFunction, getChipEffectSet, getProbeAffinityFile, getResidualSet, getRootPath, getWeightsSet

Methods inherited from MultiArrayUnitModel:
getListOfPriors, setListOfPriors, validate

Methods inherited from UnitModel:
findUnitsTodo, getAsteriskTags, getFitSingleCellUnitFunction, getParameters

Methods inherited from Model:
as.character, fit, getAlias, getAsteriskTags, getDataSet, getFullName, getName, getPath, getRootPath, getTags, setAlias, setTags

Methods inherited from ParametersInterface:
getParameterSets, getParameters, getParametersAsString

Methods inherited from Object:
$, $<-, [[, [[<-, as.character, attach, attachLocally, clearCache, clearLookupCache, clone, detach, equals, extend, finalize, getEnvironment, getFieldModifier, getFieldModifiers, getFields, getInstantiationTime, getStaticInstance, hasField, hashCode, ll, load, names, objectSize, print, save, asThis

Model

For a single unit group, the affine model is:

$$y_{ik} = a + \theta_i \phi_k + \varepsilon_{ik}$$

where $a$ is an offset common to all probe signals, $\theta_i$ are the chip effects for arrays $i=1,...,I$, and $\phi_k$ are the probe affinities for probes $k=1,...,K$. The $\varepsilon_{ik}$ are zero-mean noise with equal variance. The model is constrained such that $\prod_k \phi_k = 1$.

Note that with the additional constraint $a=0$ (see arguments above), the above model is very similar to MbeiPlm. The differences in parameter estimates is due to difference is assumptions about the error structure, which in turn affects how the model is estimated.

Author

Henrik Bengtsson

References

Bengtsson & Hossjer (2006).