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cormatrix
2002 Aug 09
1
LM: Least Squares on Large Datasets OR why lm() is designed the w ay it is
...[Y,X] and the observation count, i.e. length(Y). These include
variance of coefficients, their significance levels, ability to compute
predictions, etc. Moreover, all sub-models, i.e. a regression on any subset
of X columns are also readily computable, as well as ANOVA.
Given this I'd store the covmatrix of [Y,X] and the count on an lm object
and write summary.lm, anova.lm, step, stepAIC functions in terms of these
two members only.
I guess this is the idea behind the LM package.
2. There is whole lot of tests that are designed to check the classical
assumptions of normality of the residuals, dete...