regsubsets               package:leaps               R Documentation

_f_u_n_c_t_i_o_n_s _f_o_r _m_o_d_e_l _s_e_l_e_c_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

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

_U_s_a_g_e:

     regsubsets(x=, ...)

     regsubsets.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)
     regsubsets.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)

     summary(ll,all.best=TRUE,matrix=T,matrix.logical=F,df=NULL)

_A_r_g_u_m_e_n_t_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 perform exhaustive search on more than 50
          variables.

      ll: regsubsets object

all.best: Show all the best subsets or just one of each size

  matrix: Show a matrix of the variables in each model or just summary
          statistics

matrix.logical: With `matrix=TRUE', the matrix is logical
          `TRUE'/`FALSE' or string `"*"'/code{" "}

      df: Specify a number of degrees of freedom for the summary
          statistics. The default is `n-1'.

_V_a_l_u_e:

     An object of class `"regsubsets"' containing no user-serviceable
     parts.  It is designed to be processed by `summary.regsubsets'.

_N_o_t_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 considerably. There is no hard-coded
     limit to the number of variables.

_S_e_e _A_l_s_o:

     `leaps'

_E_x_a_m_p_l_e_s:

     data(swiss)
     a<-regsubsets(as.matrix(swiss[,-1]),swiss[,1])
     summary(a)
     b<-regsubsets(Fertility~.,data=swiss)
     summary(a)

