PRIM.real.data.min          package:rfcdmin          R Documentation

_P_R_I_M _o_u_t_p_u_t _f_r_o_m _V_R_C_m_i_n _d_a_t_a _a_n_a_l_y_s_i_s

_D_e_s_c_r_i_p_t_i_o_n:

     The HIV-protein stimulated st.1829 FCS R-object and the
     unstimulated unst.1829 FCS R-object from the VRCmin data were used
     to exemplify the PRIM algorithm on a real dataset.  The three
     steps for the finding of a single rule were saved as output.  The
     out.peel is the 'PRIM.step' class object of the Peeling step.  The
     out.expand is the 'PRIM.step' class object of the Expansion Step. 
     The out.cv is the 'PRIM.crossval.step' class object of the
     cross-validation step.  From these output objects we obtain the
     necessary indices and vectors for making plots that are
     exemplified in PRIM.pdf.

_U_s_a_g_e:

     data(PRIM.real.data.min)

_F_o_r_m_a_t:


     _b_o_x._i_d_x._l_i_s_t Peeling Step: a list of five boxes that were in the
          peeling sequence of 'out.peel' which used the whole data 'X' 

     _b_o_x._s_u_p_p_o_r_t._P_S Peeling Step: The vector of proportions of X that
          lie in the peeled box for each iteration of the expansion
          step from 'out.peel'

     _m_e_a_n_s._P_S Peeling Step: The vector of means(Y) of each peeled box
          for each iteration from 'out.peel'

     _t_y_p_e._P_S Peeling  Step: The vector of peeling types, denoting the
          direction and variable for the addition of new points into
          the box for each iteration from 'out.peel'

     _b_o_x._s_u_p_p_o_r_t._E_S Expansion Step: The vector of proportions of X that
          lie in the expanded box for each iteration of the expansion
          step from 'out.expand'

     _m_e_a_n_s._E_S Expansion Step: The vector of means(Y) of each expanded
          box for each iteration from 'out.expand'

     _t_y_p_e._E_S Expansion  Step: The vector of expansion types, denoting
          the direction and variable for the addition of new points
          into the box for each iteration from 'out.expand'

     _b_e_s_t._b_o_x._X._r_a_n_g_e_s Expansion Step: The X-variable ranges of the
          final rule estimated after the Expansion Step from
          'out.expand'

     _R_u_l_e_1._i_d_x Expansion Step: The index of the final rule obtained
          after the expansion step from out.expand

     _T_D_1 Cross-Validation Step: vector of positional row indices
          corresponding to the first testdata set which is subset from
          the original 'X' data (which is constructed in the example
          below)

     _T_D_2 Cross-Validation Step: vector of positional row indices
          corresponding to the second testdata set which is subset from
          the original 'X' data (which is constructed in the example
          below)

     _b_o_x._i_d_x._l_i_s_t_T_D_1 Cross-Validation Step: a list of five boxes in the
          peeling sequence of the first Testdata set

     _b_o_x._i_d_x._l_i_s_t_T_D_2 Cross-Validation Step: a list of five boxes in the
          peeling sequence of the second Testdata set

_D_e_t_a_i_l_s:

     The class information, extraction, plotting, implementation tools
     for the 'PRIM.step' and the 'PRIM.crossval.step' class objects are
     detailed in the 'rfcprim' package.

_S_o_u_r_c_e:

     See VRCmin

_R_e_f_e_r_e_n_c_e_s:

     See VRCmin and 'rfcprim'.

_E_x_a_m_p_l_e_s:

     data(PRIM.real.data)

     ## the following code was used to generate the output
     if (FALSE){

       data(VRCmin)
       ## the HIV-protein stimulation status
       Y <- c(rep(1, dim.FCS(st.1829)[1]), 
            rep(0, dim.FCS(unst.1829)[1]))

       ## the dataset
       X <- rbind(as(st.1829, "matrix"), as(unst.1829, "matrix"))
       if (require(rfcprim)){
       out.peel <- peel.step(X,
                           Y,
                           min.box.size = 500,
                           alpha=0.10,
                           verbose=TRUE)

       out.expand <- expand.step(X,
                               Y,
                               out.peel@best.box.idx,
                               beta=0.01,
                               verbose=TRUE)

       out.cv <- crossval.step(X,
                             Y,
                             num.testdata=2,
                             prob.testdata=c(0.50, 0.50),
                             alpha=0.10,
                             target.mu.Y=1,
                             min.box.size=500,
                             choose.best.box.decision=c("max.mean.box", "final.box"),
                            
                             beta=0.01,
                             verbose=TRUE)

       box.idx.list <- list(out.peel@best.box.idx,
          out.expand@best.box.idx)

       box.support.PS <- out.peel@box.support.vec

       means.PS <- out.peel@means.vec

       type.PS <- out.peel@type.vec

       box.support.ES <- out.expand@box.support.vec

       means.ES <- out.expand@means.vec

       type.ES <- out.expand@type.vec

       best.box.X.ranges <- out.expand@best.box.X.ranges

       Rule1.idx <- out.expand@best.box.idx

       TD1 <- out.cv[1, "testdata.idx.list"]
       TD2 <- out.cv[2, "testdata.idx.list"]

       box.idx.listTD1 <- list(out.cv[1,"step.set.list"]@peel.step@best.box.idx,
           out.cv[1, "step.set.list"]@expand.step@best.box.idx)

       box.idx.listTD2 <- list(out.cv[2,"step.set.list"]@peel.step@best.box.idx,
           out.cv[2, "step.set.list"]@expand.step@best.box.idx)
       }
      }

