fitPLM {affyPLM} | R Documentation |
This function converts an AffyBatch
into an
PLMset
by fitting a specified robust linear model to the
probe level data
fitPLM(object,model=PM ~ -1 + probes +samples, variable.type=c(default="factor"), constraint.type=c(default="contr.treatment"), subset=NULL, background=TRUE, normalize=TRUE, background.method = "RMA.2",normalize.method = "quantile",background.param=list(),normalize.param=list(),output.param = verify.output.param(), model.param = verify.model.param(object, model))
object |
an AffyBatch |
model |
A formula describing the model to fit. This is slightly different from the standard method of specifying formulae in R. Read the description below |
variable.type |
a way to specify whether variables in the model are factors or standard variables |
constraint.type |
should factor variables sum to zero or have first variable set to zero (endpoint constraint) |
subset |
a vector with the names of probesets to be used. If NULL then all probesets are used. |
normalize |
logical value. If TRUE normalize data using
quantile normalization |
background |
logical value. If TRUE background correct
using RMA background correction |
background.method |
name of background method to use. |
normalize.method |
name of normalization method to use. |
background.param |
A list of parameters for background routines |
normalize.param |
A list of parameters for normalization routines |
output.param |
A list of parameters controlling optional output from the routine. |
model.param |
A list of parameters controlling model procedure |
This function fits robust Probe Level linear Models to all the probesets in
an AffyBatch
. This is carried out on a probeset by
probeset basis. The user has quite a lot of control over which model
is used and what outputs are stored. For more details please read the vignette.
An PLMset
Ben Bolstad bolstad@stat.berkeley.edu
Bolstad, BM (2004) Low Level Analysis of High-density Oligonucleotide Array Data: Background, Normalization and Summarization. PhD Dissertation. University of California, Berkeley.
data(affybatch.example) Pset <- fitPLM(affybatch.example,model=PM ~ -1 + probes + samples) se(Pset)[1:5,] # A larger example testing weight image function data(Dilution) ## Not run: Pset <- fitPLM(Dilution,model=PM ~ -1 + probes + samples) ## Not run: image(Pset) ## Not run: NUSE(Pset) # NUSE #now lets try a wider class of models ## Not run: Pset <- fitPLM(Dilution,model=PM ~ -1 + probes +liver,normalize=FALSE,background=FALSE) ## Not run: coefs(Pset)[1:10,] ## Not run: Pset <- fitPLM(Dilution,model=PM ~ -1 + probes + liver + scanner,normalize=FALSE,background=FALSE) coefs(Pset)[1:10,] #try liver as a covariate logliver <- log2(c(20,20,10,10)) ## Not run: Pset <- fitPLM(Dilution,model=PM~-1+probes+logliver+scanner,normalize=FALSE,background=FALSE,variable.type=c(logliver="covariate")) coefs(Pset)[1:10,] #try a different se.type ## Not run: Pset <- fitPLM(Dilution,model=PM~-1+probes+scanner,normalize=FALSE,background=FALSE,model.param=list(se.type=2)) se(Pset)[1:10,]