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A generalized linear model fit on the inventory data of the two sites suggested that productivity (volume increment over the last 10 years) was primarily influenced by the management factor while the stand volume, stand age and density had a much smaller, though significant, influence (Table 2).
This allow, a) to separate fMRI data sets using the group-ICA spatial maps in a linear model fit against, resulting in matrices (time-course matrices) describing the temporal dynamics for each component and subject, and b) estimate subject-specific spatial maps using these time-course matrices.
The limma algorithm was used to compute a linear model fit [32].
A linear model, fit to each probe-set expression value, was used to impute the gene expression trajectory over the entire follow-up period.
Thus, the variation in AV determined from our general linear model fit explains why DI varies so much at one particular time.
After applying the linear model fit, paired comparisons of experimental groups resulted in the following numbers of significantly up- and downregulated genes with an identifier (Figure 2).
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A random effects analysis was conducted by performing one-sample t-tests over best fitting beta amplitudes produced by linear model fitting.
A linear model fitted to the data indicated the need to change the ranges of the variables.
Analysis of differential expression was performed using a linear model fitting (LIMMA package, [20]).
It avoids linear model fitting and thus is more efficient and less constrained by the sample size.
We evaluated the use of CD4+CD25+ as predictors of anti-TNF alpha therapy using generalized linear model fitting.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com