normalize.loess {affy} | R Documentation

## Scale microarray data

### Description

Normalizes arrays using loess.

### Usage

    normalize.loess(mat, subset=sample(1:(dim(mat)[2]),5000),
                    epsilon=10^-2,maxit=1,log.it=TRUE,verbose=TRUE,span=2/3,
                    family.loess="symmetric")
    normalize.AffyBatch.loess(abatch,type=c("together","pmonly","mmonly","separate"), ...)

### Arguments

`mat` | a matrix with columns containing the values of the chips to normalize.
`abatch` | an `AffyBatch` object.
`subset` | a subset of the data to fit a loess to.
`epsilon` | a tolerance value (supposed to be a small value - used as a stopping criterium).
`maxit` | maximum number of iterations.
`log.it` | logical. If `TRUE` it takes the log2 of `mat`
`verbose` | logical. If `TRUE` displays current pair of chip being worked on.
`span` | parameter to be passed the function `loess`
`family.loess` | parameter to be passed the function `loess`. `"gaussian"` or `"symmetric"` are acceptable values for this parameter.
`type` | A string specifying how the normalization should be applied. See details for more.
`...` | any of the options of normalize.loess you would like to modify (described above).

### Details

While a unified scheme is on its way, please see loess.normalize for a complete descritption...

The type arguement should be one of `"separate","pmonly","mmonly","together"` which indicates whether to normalize only one probe type (PM,MM) or both together or separately.

### See Also

`normalize`

### Examples

         #require(affy)
         #data(Dilution)
         #x <- pm(Dilution[,1:3])
         #mva.pairs(x)
         #x <- normalize.loess(x,subset=1:nrow(x))
         #mva.pairs(x)

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