mrlm {limma} | R Documentation

## Fit Linear Model to Microrray Data by Robust Regression

### Description

Fit a linear model genewise to expression data from a series of arrays. The fit is by robust M-estimation allowing for a small proportion of outliers. This is a utility function for `lmFit`.

### Usage

    mrlm(M,design=NULL,ndups=1,spacing=1,weights=NULL,...)

### Arguments

`M` | numeric matrix containing log-ratio or log-expression values for a series of microarrays, rows correspond to genes and columns to arrays.
`design` | numeric design matrix defining the linear model, with rows corresponding to arrays and columns to comparisons to be estimated. The number of rows must match the number of columns of `M`. Defaults to the unit vector meaning that the arrays are treated as replicates.
`ndups` | a positive integer giving the number of times each gene is printed on an array. `nrow(M)` must be divisible by `ndups`.
`spacing` | the spacing between the rows of `M` corresponding to duplicate spots, `spacing=1` for consecutive spots.
`weights` | numeric matrix of the same dimension as `M` containing weights. If it is of different dimension to `M`, it will be filled out to the same size. `NULL` is equivalent to equal weights.
`...` | any other arguments are passed to `rlm.default`.

### Details

This is a utility function used by the higher level function `lmFit`. Most users should not use this function directly but should use `lmFit` instead.

This function fits a linear model for each gene by calling the function `rlm` from the MASS library.

Warning: don't use weights with this function unless you understand how `rlm` treats weights. The treatment of weights is somewhat different from that of `lm.series` and `gls.series`.

### Value

A list with components

`coefficients` | numeric matrix containing the estimated coefficients for each linear model. Same number of rows as `M`, same number of columns as `design`.
`stdev.unscaled` | numeric matrix conformal with `coef` containing the unscaled standard deviations for the coefficient estimators. The standard errors are given by `stdev.unscaled * sigma`.
`sigma` | numeric vector containing the residual standard deviation for each gene.
`df.residual` | numeric vector giving the degrees of freedom corresponding to `sigma`.
`qr` | QR decomposition of `design`.

### Author(s)

Gordon Smyth

### See Also

`rlm`.

An overview of linear model functions in limma is given by 06.LinearModels.

[Package limma version 2.14.7 Index]