(R-intro.info)Statistical models in R

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11 Statistical models in R
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This section presumes the reader has some familiarity with statistical
methodology, in particular with regression analysis and the analysis of
variance.  Later we make some rather more ambitious presumptions, namely
that something is known about generalized linear models and nonlinear
regression.

   The requirements for fitting statistical models are sufficiently well
defined to make it possible to construct general tools that apply in a
broad spectrum of problems.

   R provides an interlocking suite of facilities that make fitting
statistical models very simple.  As we mention in the introduction, the
basic output is minimal, and one needs to ask for the details by calling
extractor functions.

Formulae for statistical models

Linear models

Generic functions for extracting model information

Analysis of variance and model comparison

Updating fitted models

Generalized linear models

Nonlinear least squares and maximum likelihood models

Some non-standard models