

   TTrryy AAllll OOnnee--TTeerrmm DDeelleettiioonnss ffrroomm aa MMooddeell

        dropterm(object, scope, , scale = 0, test=c("none", "Chisq", "F"),
                 k = 2, sorted = F, trace = F, ...)

   AArrgguummeennttss::

     object: A object fitted by some model-fitting function.

      scope: a formula giving terms which might be dropped. By
             default, the modle formula. Only terms that can be
             dropped and maintain marginality are actually
             tried.

      scale: used in the definition of the AIC statistic for
             selecting the models, currently only for `lm',
             `aov' and `glm' models. Specifying `scale' asserts
             that the residual standard error or dispersion is
             known.

       test: should the results include a test statistic rela-
             tive to the original model?  The F test is only
             appropriate for `lm' and `aov' models. The Chisq
             test can be an exact test (`lm' models with known
             scale) or a likelihood-ratio test depending on the
             method.

          k: the multiple of the number of degrees of freedom
             used for the penalty.  Only `k=2' gives the gen-
             uine AIC: `k = log(n)' is sometimes referred to as
             BIC or SBC.

     sorted: should the results be sorted on the value of AIC?

      trace: if `TRUE' additional information may be given on
             the fits as they are tried.

   DDeessccrriippttiioonn::

        Try fitting all models that differ from the current
        model by dropping a single term, maintaining marginal-
        ity.

        This function is generic; there exist methods for
        classes `lm' and `glm' and the default method will work
        for many other classes.

   DDeettaaiillss::

        The definition of AIC is only up to an additive con-
        stant: when appropriate (`lm' models with specified
        scale) the constant is taken to be that used in Mal-
        lows' Cp statistic and the results are labelled accord-
        ingly.

   VVaalluuee::

        A table of class `"anova"' containing at least columns
        for the change in degrees of freedom and AIC (or Cp)
        for the models. Some methods will give further informa-
        tion, for example sums of squares, deviances, log-like-
        lihoods and test statistics.

   SSeeee AAllssoo::

        `addterm', `stepAIC'

