lmFit
.
gls.series(M,design=NULL,ndups=2,spacing=1,block=NULL,correlation=NULL,weights=NULL,...)
M
. Defaults to the unit vector meaning that the arrays are treated as replicates.nrow(M)
must be divisible by ndups
.M
corresponding to duplicate spots, spacing=1
for consecutive spotsncol(M)
.M
containing weights for each spot. If it is of different dimension to M
, it will be filled out to the same size.dupcor.series
.M
, same number of columns as design
.coef
containing the unscaled standard deviations for the coefficient estimators. The standard errors are given by stdev.unscaled * sigma
.sigma
design
standardized by the Choleski-root of the correlation matrix defined by correlation
lmFit
.
Most users should not use this function directly but should use lmFit
instead.This function is for fitting gene-wise linear models when some of the expression values are correlated.
The correlated groups may arise from replicate spots on the same array (duplicate spots) or from a biological or technical replicate grouping of the arrays.
This function is normally called by lmFit
and is not normally called directly by users.
Note that the correlation is assumed to be constant across genes.
If correlation=NULL
then a call is made to duplicateCorrelation
to estimated the correlation.
duplicateCorrelation
.An overview of linear model functions in limma is given by 06.LinearModels.