fitProbeLevelModel {oligo} | R Documentation

## Tool to fit Probe Level Models.

### Description

Fits robust Probe Level linear Models to all the (meta)probesets in an `FeatureSet`. This is carried out on a (meta)probeset by (meta)probeset basis.

### Usage

    fitProbeLevelModel(object, background=TRUE, normalize=TRUE, target="core", method="plm", verbose=TRUE, S4=TRUE, ...)

### Arguments

`object` | `FeatureSet` object.
`background` | Do background correction?
`normalize` | Do normalization?
`target` | character vector describing the summarization target. Valid values are: 'probeset', 'core' (Gene/Exon), 'full' (Exon), 'extended' (Exon).
`method` | summarization method to be used.
`verbose` | verbosity flag.
`S4` | return final value as an S4 object (`oligoPLM`) if `TRUE`. If `FALSE`, final value is returned as a `list`.
`...` | subset to be passed down to `getProbeInfo` for subsetting. See `subset` for details.

### Value

`fitProbeLevelModel` returns an `oligoPLM` object, if `S4=TRUE`; otherwise, it will return a list.

### Note

This is the initial port of `fitPLM` to oligo. Some features found on the original work by Ben Bolstad (in the affyPLM package) may not be yet available. If you found one of this missing characteristics, please contact Benilton Carvalho.

### Author(s)

This is a simplified port from Ben Bolstad's work implemented in the affyPLM package. Problems with the implementation in oligo should be reported to Benilton Carvalho.

### References

Bolstad, BM (2004) Low Level Analysis of High-density Oligonucleotide Array Data: Background, Normalization and Summarization. PhD Dissertation. University of California, Berkeley.

### See Also

`rma`, `summarizationMethods`, `subset`

### Examples

    if (require(oligoData)){
      data(nimbleExpressionFS)
      fit <- fitProbeLevelModel(nimbleExpressionFS)
      image(fit)
      NUSE(fit)
      RLE(fit)
    }

[Package oligo version 1.50.0 Index]