rlm {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,...)
rlm.series(x,...)

### Arguments

`M` |
numeric matrix containing log-ratio or log-expression values for a series of microarrays, rows correspond to genes and columns to arrays. |

`x` |
same as `M` |

`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 linear models 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`

.

The function `rlm.series`

is equivalent to `mrlm`

but is deprecated and will be removed at some time in the future.

### 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 2.4.7

Index]