

   AAcccceessssiinngg LLiinneeaarr MMooddeell FFiittss

        anova(object, ...)
        anovalist(object, ..., test = NULL)
        summary(object, correlation = FALSE)
        coefficients(x) ; coef(x)
        df.residual(x)
        family(x)
        formula(x)
        fitted.values(x)
        residuals(x)
        weights(x)
        plot(x)

        print(summary.lm.obj, digits = max(3, .Options$digits - 3),
              symbolic.cor = p > 4,
              signif.stars= .Options$show.signif.stars, ...)

   AArrgguummeennttss::

   object,x: an object of class `lm', usually, a result of
             `lm(..)'.

   DDeessccrriippttiioonn::

        All these functions are `methods' for class `lm' or
        `summary.lm' and `anova.lm' objects.

   DDeettaaiillss::

        `print.summary.lm' tries to be smart about formatting
        the coefficients, standard errors, etc. and addition-
        ally gives ``significance stars'' if `signif.stars' is
        `TRUE'.

        `anova.lm' produces an analysis of variance (`anova')
        table.

        The generic accessor functions `coefficients',
        `effects', `fitted.values' and `residuals' can be used
        to extract various useful features of the value
        returned by `lm'.

   VVaalluuee::

        The function `summary.lm' computes and returns a list
        of summary statistics of the fitted linear model given
        in `lm.obj', using the slots (list elements) `"call"',
        `"terms"', and `"residuals"' from its argument, plus

   coefficients: a p x 4 matrix with columns for the estimated
             coefficient, its standard error, t-statistic and
             corresponding (two-sided) p-value.

      sigma: the square root of the estimated variance of the
             random error

                       sigma^2 = 1/(n-p) Sum(R[i]^2),

             where R[i] is the i-th residual, `residuals[i]'.

         df: degrees of freedom, a 3-vector (p,n-p,p*) ...

   fstatistic: a 3-vector with the value of the F-statistic
             with its numerator and denominator degrees of
             freedom.

   r.squared: R^2, the ``fraction of variance explained by the
             model'',

                 R^2 = 1 - Sum(R[i]^2) / Sum((y[i]- y*)^2),

             where y* is the mean of y[i] if there is an inter-
             cept and zero otherwise.

   adj.r.squared: the above R^2 statistic ``adjusted'', penal-
             izing for higher p.

   cov.unscaled: a p x p matrix of (unscaled) covariances of
             the coef..[j], j=1,...,p.

   correlation: the correlation matrix corresponding to the
             above `cov.unscaled', if `correlation = TRUE' is
             specified.

   SSeeee AAllssoo::

        The model fitting function `lm'.

        `anova' for the ANOVA table, `coefficients',
        `deviance', `effects', `fitted.values', `glm' for gen-
        eralized linear models, `lm.influence' for regression
        diagnostics, `residuals', `summary'.

   EExxaammpplleess::

        ##-- Continuing the  lm(.) example:
        coef(lm.D90)# the bare coefficients
        sld90 <- summary(lm.D90 <- lm(weight ~ group -1))# omitting intercept
        sld90
        coef(sld90)# much more

        ## The 2 basic regression diagnostic plots:
        plot(resid(lm.D90), fitted(lm.D90))# Tukey-Anscombe's
        abline(h=0, lty=2, col = 'gray')

        qqnorm(residuals(lm.D90))

        ##-- Continuing the  lm(.) example:
        coef(lm.D90)# the bare coefficients
        sld90 <- summary(lm.D90 <- lm(weight ~ group -1))# omitting intercept
        sld90
        coef(sld90)# much more

        ## The 2 basic regression diagnostic plots:
        plot(resid(lm.D90), fitted(lm.D90))# Tukey-Anscombe's
        abline(h=0, lty=2, col = 'gray')

        qqnorm(residuals(lm.D90))

