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=, ...)

     ## S3 method for class 'formula':
     regsubsets(x=, data=, weights=NULL, nbest=1, nvmax=8, force.in=NULL, force.out=NULL, intercept=TRUE, method=c("exhaustive", "backward", "forward", "seqrep"), really.big=FALSE,...)

     ## Default S3 method:
     regsubsets(x=, y=, weights=rep(1, length(y)), nbest=1, nvmax=8,
     force.in=NULL, force.out=NULL, intercept=TRUE, method=c("exhaustive",
     "backward", "forward", "seqrep"), really.big=FALSE,...)

     ## S3 method for class 'regsubsets':
     summary(object,all.best=TRUE,matrix=TRUE,matrix.logical=FALSE,df=NULL,...)

_A_r_g_u_m_e_n_t_s:

       x: design matrix or model formula for full model

    data: Optional data frame

       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 TRUE to perform exhaustive search on more than 50
          variables.

  object: 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'

     ...: Other arguments for future methods

_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)

