leaps                 package:leaps                 R Documentation

_a_l_l-_s_u_b_s_e_t_s _r_e_g_r_e_s_s_i_o_m

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

     leaps() performs an exhaustive search for the best subsets of the
     variables in x for predicting y in linear regression, using an
     efficient branch-and-bound algorithm.  It is a compatibility
     wrapper for 'regsubsets' does the same thing better.

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

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

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

_V_a_l_u_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_o_t_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_e_f_e_r_e_n_c_e_s:

     Alan Miller "Subset Selection in Regression" Chapman & Hall

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

     'regsubsets', 'regsubsets.formula', 'regsubsets.default'

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

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

