AvgPlm {aroma.affymetrix}R Documentation

The AvgPlm class

Description

Package: aroma.affymetrix
Class AvgPlm

Object
~~|
~~+--Model
~~~~~~~|
~~~~~~~+--UnitModel
~~~~~~~~~~~~|
~~~~~~~~~~~~+--MultiArrayUnitModel
~~~~~~~~~~~~~~~~~|
~~~~~~~~~~~~~~~~~+--ProbeLevelModel
~~~~~~~~~~~~~~~~~~~~~~|
~~~~~~~~~~~~~~~~~~~~~~+--AvgPlm

Directly known subclasses:
AvgCnPlm, AvgSnpPlm

public static class AvgPlm
extends ProbeLevelModel

This class represents a PLM where the probe intensities are averaged assuming identical probe affinities. For instance, one may assume that replicated probes with identical sequences have the same probe affinities, cf. the GenomeWideSNP_6 chip type.

Usage

AvgPlm(..., flavor=c("median", "mean"))

Arguments

... Arguments passed to ProbeLevelModel.
flavor A character string specifying what model fitting algorithm to be used. This makes it possible to get identical estimates as other packages.

Fields and Methods

Methods:
getAsteriskTags -

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

Model

For a single unit group, the averaging PLM of K probes is:

y_{ik} = theta_i + varepsilon_{ik}

where theta_i are the chip effects for arrays i=1,...,I. The varepsilon_{ik} are zero-mean noise with equal variance.

Different flavors of model fitting

The above model can be fitted in two ways, either robustly or non-robustly. Use argument flavor="mean" to fit the model non-robustly, i.e.

hat{theta}_{i} = 1/K sum_k y_{ik}

.

Use argument flavor="median" to fit the model robustly, i.e.

hat{theta}_{i} = median_k y_{ik}

.

Missing values are always excluded.

Author(s)

Henrik Bengtsson (http://www.braju.com/R/)


[Package aroma.affymetrix version 1.2.0 Index]