| AffinePlm {aroma.affymetrix} | R Documentation |
Package: aroma.affymetrix
Class AffinePlm
Object
~~|
~~+--Model
~~~~~~~|
~~~~~~~+--UnitModel
~~~~~~~~~~~~|
~~~~~~~~~~~~+--MultiArrayUnitModel
~~~~~~~~~~~~~~~~~|
~~~~~~~~~~~~~~~~~+--ProbeLevelModel
~~~~~~~~~~~~~~~~~~~~~~|
~~~~~~~~~~~~~~~~~~~~~~+--AffinePlm
Directly known subclasses:
AffineCnPlm, AffineSnpPlm
public static class AffinePlm
extends ProbeLevelModel
This class represents affine model in Bengtsson & Hössjer (2006).
AffinePlm(..., background=TRUE)
... |
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. |
Methods:
getAsteriskTags | - | |
getProbeAffinityFile | - |
Methods inherited from ProbeLevelModel:
calculateResidualSet, calculateWeights, fit, getAsteriskTags, getCalculateResidualsFunction, getChipEffectSet, getProbeAffinityFile, getResidualSet, getWeightsSet
Methods inherited from MultiArrayUnitModel:
getListOfPriors, setListOfPriors, validate
Methods inherited from UnitModel:
findUnitsTodo, getAsteriskTags, getFitSingleCellUnitFunction
Methods inherited from Model:
fit, getAlias, getAsteriskTags, getDataSet, getFullName, getName, getPath, getRootPath, getTags, setAlias, setTags
Methods inherited from Object:
asThis, $, $<-, [[, [[<-, as.character, attach, attachLocally, clearCache, clone, detach, equals, extend, finalize, gc, getEnvironment, getFields, getInstantiationTime, getStaticInstance, hasField, hashCode, ll, load, objectSize, print, registerFinalizer, save
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.
Henrik Bengtsson (http://www.braju.com/R/)
Bengtsson & Hössjer (2006).