normalize.loess {affy} | R Documentation

## Scale microarray data

### Description

Normalizes arrays using loess.

### Usage

    normalize.loess(mat, subset = sample(1:(dim(mat)[1]), min(c(5000,
                     nrow(mat)))), 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

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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