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), verbosity.level=0)
object |
an |
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 |
background |
logical value. If |
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 |
verbosity.level |
An integer specifying how much to print out. Higher values indicate more verbose. A value of 0 will print nothing |
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 bmb@bmbolstad.com
Bolstad, BM (2004) Low Level Analysis of High-density Oligonucleotide Array Data: Background, Normalization and Summarization. PhD Dissertation. University of California, Berkeley.
if (require(affydata)) { data(Dilution) Pset <- fitPLM(Dilution, model=PM ~ -1 + probes + samples) se(Pset)[1:5,] image(Pset) NUSE(Pset) #now lets try a wider class of models ## Not run: Pset <- fitPLM(Dilution,model=PM ~ -1 + probes +liver, normalize=FALSE,background=FALSE) ## End(Not run) ## Not run: coefs(Pset)[1:10,] ## Not run: Pset <- fitPLM(Dilution,model=PM ~ -1 + probes + liver + scanner, normalize=FALSE,background=FALSE) ## End(Not run) 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")) ## End(Not run) coefs(Pset)[1:10,] #try a different se.type ## Not run: Pset <- fitPLM(Dilution, model=PM~-1+probes+scanner, normalize=FALSE,background=FALSE,m odel.param=list(se.type=2)) ## End(Not run) se(Pset)[1:10,] }