

   aallll--ssuubbsseettss rreeggrreessssiioomm

        leaps(x=, y=, wt=rep(1, NROW(x)), int=TRUE, method=c("Cp", "adjr2", "r2"), nbest=10, names=NULL, df=NROW(x), strictly.compatible=T)

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

          x: A matrix of predictors

          y: A response vector

         wt: Optional weight vector

        int: Add an intercept to the model

     method: Calculate Cp, adjusted R-squared or R-squared

      nbest: Number of subsets of each size to report

      names: vector of names for columns of `x'

         df: Total degrees of freedom to use instead of
             `nrow(x)' in calculating Cp and adjusted R-squared

   strictly.compatible: Implement misfeatures of leaps() in S

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

        leaps() performs an exhaustive search for the best sub-
        sets of the variables in x for predicting y in linear
        regression, using an efficient branch-and-bound algo-
        rithm.  `subsets' does the same thing better.

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

        A list with components

      which: logical matrix. Each row can be used to select the
             columns of `x' in the respective model

       size: Number of variables, including intercept if any,
             in the model

         cp: or `adjr2' or `r2' is the value of the chosen
             model selectionstatistic for each model

      label: vector of names for the columns of x

   NNoottee::

        With `strictly.compatible=T' the function will stop
        with an error if `x' is not of full rank or if it has
        more than 31 columns. It will ignore the column names
        of `x' even if `names==NULL' and will replace them with
        "0" to "9", "A" to "Z".

   RReeffeerreenncceess::

        Alan Miller "Subset Selection in Regression" Chapman
        Hall S documentation for `leaps()'

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

        `subsets', `subsets.formula', `subsets.default', `sum-
        mary.leaps'

   EExxaammpplleess::

        x<-matrix(rnorm(100),ncol=4)
        y<-rnorm(25)
        leaps(x,y)

