lm.series: Fit Linear Model to Microrray Data by Ordinary Least Squares
In hdeberg/limma: Linear Models for Microarray Data

Description Usage Arguments Details Value Author(s) See Also Examples

Fit a linear model genewise to expression data from a series of arrays. This function uses ordinary least squares and is a utility function for lmFit.

1	lm.series(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. The number of rows should agree with the number of columns of M. The number of columns will determine the number of coefficients estimated for each gene.
`ndups`	number of duplicate spots. Each gene is printed ndups times in adjacent spots on each array.
`spacing`	the spacing between the rows of `M` corresponding to duplicate spots, `spacing=1` for consecutive spots
`weights`	an optional numeric matrix of the same dimension as `M` containing weights for each spot. If it is of different dimension to `M`, it will be filled out to the same size.

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.

The linear model is fit for each gene by calling the function lm.fit or lm.wfit from the base library.

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`