tunerf

本程序是基于linux系统下c++代码

tuneRF             package:randomForest             R Documentation

_T_u_n_e _r_a_n_d_o_m_F_o_r_e_s_t _f_o_r _t_h_e _o_p_t_i_m_a_l _m_t_r_y _p_a_r_a_m_e_t_e_r

_D_e_s_c_r_i_p_t_i_o_n:

     Starting with the default value of mtry, search for the optimal
     value (with respect to Out-of-Bag error estimate) of mtry for
     randomForest.

_U_s_a_g_e:

     tuneRF(x, y, mtryStart, ntreeTry=50, stepFactor=2, improve=0.05,
            trace=TRUE, plot=TRUE, doBest=FALSE, ...)

_A_r_g_u_m_e_n_t_s:

       x: matrix or data frame of predictor variables

       y: response vector (factor for classification, numeric for
          regression)

mtryStart: starting value of mtry; default is the same as in
          'randomForest'

ntreeTry: number of trees used at the tuning step

stepFactor: at each iteration, mtry is inflated (or deflated) by this
          value

 improve: the (relative) improvement in OOB error must be by this much
          for the search to continue

   trace: whether to print the progress of the search

    plot: whether to plot the OOB error as function of mtry

  doBest: whether to run a forest using the optimal mtry found

     ...: options to be given to 'randomForest'

_V_a_l_u_e:

     If 'doBest=FALSE' (default), it returns a matrix whose first
     column contains the mtry values searched, and the second column
     the corresponding OOB error.

     If 'doBest=TRUE', it returns the 'randomForest' object produced
     with the optimal 'mtry'.

_S_e_e _A_l_s_o:

     'randomForest'

_E_x_a_m_p_l_e_s:

     data(fgl, package="MASS")
     fgl.res <- tuneRF(fgl[,-10], fgl[,10], stepFactor=1.5)