search for: sigma_k

...ley and A. Raftery. My problem is trying to figure out how the (model) identifier (e.g, EII, VII, VVI, etc.) relates to the covariance matrix. The parameterization of the covariance matrix makes use of the method of decomposition in Banfield and Rraftery (1993) and Fraley and Raftery (2002) where Sigma_k = lambda_k*D_k*A_k*D_k^' where Sigma_k is the covariance matrix for the kth (k=1,...,G), lambda_k is the kth groups constant of proportionality, D_k is the orthogonal matrix of eigenvectors for the kth group, and A_k is a diagonal matrix whose elements are proportional to the eigenvalues. The...

Hello, I was wondering if someone knows the formula used by the function lm to compute the t-values. I am trying to implement a linear regression myself. Assuming that I have K variables, and N observations, the formula I am using is: For the k-th variable, t-value= b_k/sigma_k With b_k is the coefficient for the k-th variable, and sigma_k =(t(x) x )^(-1) _kk is its standard deviation. I find sigma_k = sigma * n/(n*Sum x_{k,i}^2 -(sum x_{k,i}^2)) With sigma: the estimated standard deviation of the residuals, Sigma = sqrt(1/(N-K-1)*Sum epsilon_i^2) With: N: n...

Dear R developers, I am a little disappointed that my bug report only made it to the wishlist, with the argument: Well, it does not say it has. Only relevant to prediction intervals. predict.lm does calculate prediction intervals for linear models from weighted regression, so they should be correct, right? As far as I can see they are bound to be wrong in almost all cases, if no weights