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 arguments 'plat' and 'toktype' which will use
          'lookUp(...,plat,toktype)' from the 'annotate' package to
          translate probe names to, e.g., gene symbols.

     _s_h_o_w 'signature(object = "varImpStruct")': simple abbreviated
          display 

     _g_e_t_V_a_r_I_m_p 'signature(object = "classifOutput",
          fixNames="logical")': extractor of variable importance
          structure; fixNames parameter is to remove leading X used to
          make variable names syntactic by randomForest (ca 1/2008). 
          You can set fixNames to false if using hu6800 platform,
          because all featureNames are syntactic as given.

     _r_e_p_o_r_t 'signature(object = "classifOutput", fixNames="logical")':
          extractor of variable importance data, with annotation;
          fixNames parameter is to remove leading X used to make
          variable names syntactic by randomForest (ca 1/2008).  You
          can set fixNames to false if using hu6800 platform, because
          all featureNames are syntactic as given.

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

     library(golubEsets)
     data(Golub_Merge)
     library(hu6800.db)
     smallG <- Golub_Merge[1001:1060,]
     set.seed(1234)
     opar=par(no.readonly=TRUE)
     par(las=2, mar=c(10,11,5,5))
     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, FALSE ), n=10, plat="hu6800", toktype="SYMBOL")
     par(opar)
     report( getVarImp( rf2, FALSE ), n=10, plat="hu6800", toktype="SYMBOL")

