varImpStruct-class       package:MLInterfaces       R Documentation

_C_l_a_s_s "_v_a_r_I_m_p_S_t_r_u_c_t" - _c_o_l_l_e_c_t _d_a_t_a _o_n _v_a_r_i_a_b_l_e _i_m_p_o_r_t_a_n_c_e
_f_r_o_m _v_a_r_i_o_u_s _m_a_c_h_i_n_e _l_e_a_r_n_i_n_g _m_e_t_h_o_d_s

_D_e_s_c_r_i_p_t_i_o_n:

     collects data on variable importance

_O_b_j_e_c_t_s _f_r_o_m _t_h_e _C_l_a_s_s:

     Objects can be created by calls of the form 'new("varImpStruct",
     ...)'. These are matrices of importance measures with separate 
     slots identifying algorithm generating the measures and variable
     names.

_S_l_o_t_s:

     '._D_a_t_a': Object of class '"matrix"' actual importance measures 

     '_m_e_t_h_o_d': Object of class '"character"' tag 

     '_v_a_r_n_a_m_e_s': Object of class '"character"' conformant vector of
          names of variables 

_E_x_t_e_n_d_s:

     Class '"matrix"', from data part. Class '"structure"', by class
     '"matrix"'. Class '"array"', by class '"matrix"'. Class
     '"vector"', by class "matrix", with explicit coerce. Class
     '"vector"', by class "matrix", with explicit coerce.

_M_e_t_h_o_d_s:

     _p_l_o_t 'signature(x = "varImpStruct")': make a bar plot, you can
          supply an argument resolveenv which will look up variable
          names in a translation environment 

     _s_h_o_w 'signature(object = "varImpStruct")': simple abbreviated
          display 

     _g_e_t_V_a_r_I_m_p 'signature(object = "classifOutput")': extractor of
          variable importance structure

_E_x_a_m_p_l_e_s:

     library(golubEsets)
     data(Golub_Merge)
     library(hu6800)
     smallG <- Golub_Merge[1:60,]
     set.seed(1234)
     opar=par(no.readonly=TRUE)
     par(las=2, mar=c(10,11,5,5))
     rf <- randomForestB( smallG, "ALL.AML", 1:40, importance=TRUE )
     plot( getVarImp( rf ), n=10, resolveenv=hu6800SYMBOL )
     gb <- gbmB( smallG, "ALL.AML", 1:40 , n.minobsinnode=3 , n.trees=6000)
     plot( getVarImp( gb ), n=10, resolveenv=hu6800SYMBOL )
     set.seed(1234)
     rf2 <- MLearn(ALL.AML~., smallG, randomForestI, 1:40, importance=TRUE,
      sampsize=table(smallG$ALL.AML[1:40]), mtry=sqrt(ncol(exprs(smallG))))
     plot( getVarImp( rf2 ), n=10, resolveenv=hu6800SYMBOL ) # translation?
     par(opar)

