The underlying least squares arithmetic of aov and lm is identical. In R, the QR algorithm is used. The difference between the two is intent of the analysis and the default presentation of the results.
With lm [Linear Model], the focus is on the effect of the individual columns of the predictor matrix. The columns are usually interpreted as values of real-valued observations. The regression coefficients are usually meaningful and interesting. With aov [Analysis Of Variance], the focus is on the effects of factors. These are multi-degree of freedom effects associated with categorical variables. The arithmetic is based on a set of dummy variables constructed from a contrast matrix. The individual regression coefficients themselves are not easily interpretable. You can pursue the details of this summary in any good statistical methods book. Rich ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
