

leaps(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 sub-
     sets of the variables in x for predicting y in linear
     regression, using an efficient branch-and-bound algo-
     rithm.  It is a compatibility wrapper for `subsets'
     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=T)

_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_:

     `subsets', `subsets.formula', `subsets.default'

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

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

