Fields and Methods
Methods:
rll{
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, getChecksum, $, $<-, [[, [[<-, as.character, attach, attachLocally, clearCache, clearLookupCache, clone, detach, equals, extend, finalize, gc, getEnvironment, getFieldModifier, getFieldModifiers, getFields, getInstantiationTime, getStaticInstance, hasField, hashCode, ll, load, objectSize, print, registerFinalizer, saveModel
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.