buildBasisFunction

buildBasisSplineMatrix

calcArrayEffects

calculate.nulls

checkArguments

edge.fit

edge.glr

edge.qvalue

err.msg

estimateDifferentialExpression

fit.fast.model

fit.model

getSpanningSet

make.ref.model.matrices

make.snm.obj

makeDataObject

remove.adjustment.vars

rm.zero.cols

sim.function.var

sim.intensity.dep

sim.probe.specific

snm.diagnostic.plot


Internal functions for the snm package not called by the user.


internal

SNM is a modeling strategy especially designed for
normalizing high-throughput genomic data. The underlying
premise of our approach is that your data is a function of what
we refer to as study-specific variables. These variables are
either biological variables that represent the target of the
statistical analysis, or adjustment variables that represent
factors arising from the experimental or biological setting the
data is drawn from. The SNM approach aims to simultaneously
model all study-specific variables in order to more accurately
characterize the biological or clinical variables of interest.

John D Storey

Supervised Normalization of Microarrays

buildBasisFunction function

Internal functions for the snm package not called by the user.

Internal snm functions.

buildBasisFunction: Internal snm functions.

Description

Arguments