

   ffuunnccttiioonnss ffoorr ssuubbsseett sseelleeccttiioonn

        subsets(x=, ...)

        subsets.formula(formula=, data=, weights=rep(1, length(y)), nbest=1, nvmax=8, force.in=NULL, force.out=NULL, intercept=T, method=c("exhaustive", "backward", "forward", "seqrep"), really.big=F)

        subsets.default(x=, y=, weights=rep(1, length(y)), nbest=1, nvmax=8, force.in=NULL, force.out=NULL, intercept=T, method=c("exhaustive", "backward", "forward", "seqrep"), really.big=F)

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

    formula: model formula for full model

       data: Optional data frame

          x: design matrix with all predictors

          y: response vector

    weights: weight vector

      nbest: number of subsets of each size to record

      nvmax: maximum size of subsets to examine

   force.in: index to columns of design matrix that should be
             in all models

   force.out: index to columns of design matrix that should be
             in no models

   intercept: Add an intercept?

     method: Use exhaustive search, forward selection, backward
             selection or sequential replacement to search.

   really.big: Must be T to performe exhaustive search on more
             than 50 variables.

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

        Generic function for regression subset selection with
        methods for formula and matrix arguments.

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

        An object of class "leaps" containing no user-service-
        able parts. It is designed to be processed by `sum-
        mary.leaps'. If you want to understand the components
        of this object then read the source.

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

        This function improves on `leaps()' in several ways.
        The design matrix need not be of full rank. The ability
        to restrict `nvmax' speeds up exhaustive searches con-
        siderably. There is no hard-coded limit to the number
        of variables.

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

        `leaps()', `summary.leaps'

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

        data(swiss)
        a<-subsets(as.matrix(swiss[,-1]),swiss[,1])
        b<-summary(a)

