mrlm: Fit Linear Model to Microrray Data by Robust Regression
In hdeberg/limma: Linear Models for Microarray Data

Description Usage Arguments Details Value Author(s) See Also

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.

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

`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`.

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.

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`.