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Modern repeated measures analysis using mixed models in SPSS (1) Repeated measures analyse an introduction to the Mixed models (random effects) option in SPSS. Demonstrates different Covariance matrix types & how to use the Likelihood ratio test to evaluate different models. Robin Beaumont Full notes MCQ's etc at:

[R-sig-ME] Variance components analysis using a GLMM, how to insert a variance-covariance matrix in the model ?

able in the package nlme. Actually, the GLS directly models the spatial covariance structure in the variance-covariance matrix using parametric functions. But first we'll ignore spatial autocorrelation and re-fit the model we had in the introduction, this time using the gls function (instead of lm). The results will be the same, but we will need this model later when doing model comparisons using AIC (i.e. we can't compare the AICs from the model fit using lm with that fit using gls).

from ScienceDaily

Smaller is better for nanotube analysis: Variance spectroscopy technique advances nanoparticle analysis

A covariance matrix produced with a new technique at Rice University maps fluorescence signals from various species of single-walled carbon nanotubes that are beginning to aggregate in a sample. The matrix allows researchers to know which types of nanotubes (identified by their fluorescence spectra) have aggregated and in what amounts, in this case after four hours in solution.