

   FFiittttiinngg LLiinneeaarr MMooddeellss

        lm(formula, data, subset, weights, na.action=na.omit,
           method="qr", model=TRUE, singular.ok = TRUE)

        lm.fit[.null] (x, y,    method = "qr", tol = 1e-7, ...)
        lm.wfit[.null](x, y, w, method = "qr", tol = 1e-7, ...)

   AArrgguummeennttss::

    formula: a symbolic description of the model to be fit.
             The details of model specification are given
             below.

       data: an optional data frame containing the variables in
             the model.  By default the variables are taken
             from the environment which `lm' is called from.

     subset: an optional vector specifying a subset of observa-
             tions to be used in the fitting process.

    weights: an optional vector of weights to be used in the
             fitting process.

   na.action: a function which indicates what should happen
             when the data contain `NA's.  The default action
             (`na.omit') is to omit any incomplete observa-
             tions.  The alternative action `na.fail' causes
             `lm' to print an error message and terminate if
             there are any incomplete observations.

      model: logical.  If `TRUE' (default), the model.frame is
             also returned.

   singular.ok: logical, defaulting to `TRUE'. `FALSE' is not
             yet implemented.

     method: currently, only `method="qr"' is supported.

        tol: tolerance for the `qr' decomposition.  Default is
             1e-7.

        ...: currently disregarded.

   DDeessccrriippttiioonn::

        `lm' is used to fit linear models.  It can be used to
        carry out regression, single stratum analysis of vari-
        ance and analysis of covariance.

        Models for `lm' are specified symbolically.  A typical
        model has the form `response ~ terms' where `response'
        is the (numeric) response vector and `terms' is a
        series of terms which specifies a linear predictor for
        `response'.  A terms specification of the form
        `first+second' indicates all the terms in `first'
        together with all the terms in `second' with duplicates
        removed.  A specification of the form `first:second'
        indicates the the set of terms obtained by taking the
        interactions of all terms in `first' with all terms in
        `second'.  The specification `first*second' indicates
        the cross of `first' and `second'.  This is the same as
        `first+second+first:second'.

   VVaalluuee::

        `lm' returns an object of `class' `"lm"'.

        The functions `summary' and `anova' are used to obtain
        and print a summary and analysis of variance table of
        the results.  The generic accessor functions `coeffi-
        cients', `effects', `fitted.values' and `residuals'
        extract various useful features of the value returned
        by `lm'.

   SSeeee AAllssoo::

        `summary.lm' for summaries and `anova.lm' for the ANOVA
        table.  The generic functions `coefficients',
        `effects', `residuals', `fitted.values'; `lm.influence'
        for regression diagnostics, and `glm' for generalized
        linear models.

   EExxaammpplleess::

        ## Annette Dobson (1990) "An Introduction to Statistical Modelling".
        ## Page 9: Plant Weight Data.
        ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
        trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
        group <- gl(2,10,20,labels=c("Ctl","Trt"))
        weight <- c(ctl,trt)
        anova(lm.D9 <- lm(weight~group))
        summary(lm.D90 <- lm(weight ~ group -1))# omitting intercept
        summary(resid(lm.D9) - resid(lm.D90)) #- residuals almost identical

        plot(lm.D9)# Residuals, Fitted,..

