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...covariance matrix) So, just what do the distribution, volume, shape, and orientation mean in the context of Sigma_k? What do the distribution, volume, shape, and orientation mean for a Sigma_k=sigma^2*I where I is a p by p covariance matrix, sigma^2 is the constant variance and Sigma_1=Sigma_2=…=Sigma_G. What about when a Sigma_k=sigma^2_k*I, or when Sigma_1=Sigma_2=…=Sigma_G in situations where each element of the (constant across class) covariance matrix is different? I would say I have a pretty good understanding of finite mixture modeling, but nothing I've read (expect the works cited in...

Hi! I am using the NLME package for R to modeling glucose-insuline response with Bergman's model, very similar to the example in the documentation for the NLME package. My question concerns the model for the residuals. I use a proportional model , Var(e_{ij})=(sigma_g*G(t))^2 for the glucose response and Var(e_{ij})=(sigma_i * I(t))^2 for the insulin response. Hence I have a varPower model, but I know that the power coefficient is 2, but with different sigma coefficients for the two responses. If I fit a model I get an estimation of the standard deviation of th...