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
.
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 |
weights |
an optional numeric matrix of the same dimension as |
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 |
stdev.unscaled |
numeric matrix conformal with |
sigma |
numeric vector containing the residual standard deviation for each gene. |
df.residual |
numeric vector giving the degrees of freedom corresponding to |
qr |
QR-decomposition of |
Gordon Smyth
An overview of linear model functions in limma is given by 06.LinearModels.
# See lmFit for examples
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