fitPLM {affyPLM} | R Documentation

## Fit a Probe Level Model to Affymetrix Genechip Data.

### Description

This function converts an `AffyBatch` into an `PLMset` by fitting a specified robust linear model to the probe level data

### Usage

    fitPLM(object,model=PM ~ -1 + probes +samples,
    variable.type=c(default="factor"),
    constraint.type=c(default="contr.treatment"),
    background=TRUE, normalize=TRUE, background.method = "RMA.2",normalize.method = "quantile",se.type=4,psi.type="Huber",psi.k=NULL,background.param=list(),normalize.param=list())

### Arguments

`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)
`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.
`se.type` | integer value indicating which of the four standard error methods to use
`psi.type` | A name of a weight function to use in M estimation
`psi.k` | A tuning parameter for `psi.type`
`background.param` | A list of parameters for background routines
`normalize.param` | A list of parameters for normalization routines

### Details

This function fits robust Probe Level Linear models to all the probesets in an `AffyBatch`.

### Value

An `PLMset`

### Author(s)

Ben Bolstad bolstad@stat.berkeley.edu

### References

Under Preparation

### See Also

`express`,`expresso`, `rma`, `threestep`

### Examples

    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)
    Pset <- fitPLM(Dilution,model=PM ~ -1 + probes + samples)
    image(Pset)
    boxplot(Pset)

    #now lets try a wider class of models
    Pset <- fitPLM(Dilution,model=PM ~ -1 + probes +liver,normalize=FALSE,background=FALSE)
    coefs(Pset)[1:10,]

    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))
    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
    Pset <- fitPLM(Dilution,model=PM~-1+probes+scanner,normalize=FALSE,background=FALSE,se.type=2)
    se(Pset)[1:10,]
    Pset <- fitPLM(Dilution,model=PM~-1+probes+scanner,normalize=FALSE,background=FALSE,se.type=3)
    se(Pset)[1:10,]

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