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The MAT background model is fitted per sample to genomic and anti-genomic background probes (GC-bins).

The background model was fit as described in Section 2.3 and the posterior probabilities of each gene being a driver were computed as described in Section 2.2.

Background signals were subtracted and values were quantile-normalized before a linear model was fitted for statistical testing of differential expression.

Univariate linear regression models and a multiple linear regression model were fitted to study the effects of the selected background variables on the overall number of sick leave days the physician prescribed for the 19 patient cases.

The model was fitted to the nuclear scattering data by fixing the background (dΣ/dΩ = 0.01), and iterating other parameters with constraints.

The model was fitted using MCMC methods.

bkg_id None If not None, then the normalization of the model being fit to the given background component is changed.

Models were fitted using the lme471 package.

Linear mixed models were fitted using ASREML.

All models were fitted using STATA 10.0.

Three models were fitted.