lm.series {limma}R Documentation

Fit Linear Model to Microrray Data by Ordinary Least Squares


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.




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


Gordon Smyth

See Also


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

[Package limma version 2.4.7 Index]