rvm-class              package:kernlab              R Documentation

_C_l_a_s_s "_r_v_m"

_D_e_s_c_r_i_p_t_i_o_n:

     Relevance Vector Machine Class

_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("rvm", ...)'. or
     by calling the 'rvm' function.

_S_l_o_t_s:


     '_t_o_l': Object of class '"numeric"' contains tolerance of
          termination critiria used.

     '_k_e_r_n_e_l_f': Object of class '"kfunction"' contains the kernel
          function used 

     '_k_p_a_r': Object of class '"list"' contains the hyperparameter used

     '_k_c_a_l_l': Object of class '"call"' contains the function call

     '_t_y_p_e': Object of class '"character"' contains type of problem

     '_t_e_r_m_s': Object of class '"ANY"'  containing the terms
          representation of the symbolic model used (when using a
          formula interface)

     '_x_m_a_t_r_i_x': Object of class '"matrix"' contains the data matrix
          used during computation

     '_y_m_a_t_r_i_x': Object of class '"output"' contains the response matrix

     '_f_i_t_t_e_d': Object of class '"output"' with the fitted values,
          (predict on trianing set).

     '_l_e_v': Object of class '"vector"' contains the levels of the
          response (in classification)

     '_n_c_l_a_s_s': Object of class '"numeric"' contains the number of
          classes (in classification)

     '_a_l_p_h_a': Object of class '"listI"' containing the the resulting
          alpha vector

     '_n_v_a_r': Object of class '"numeric"' containing the calculated
          variance (in case of regression)

     '_m_l_i_k_e': Object of class '"numeric"' containing the computed
          maximum likelihood

     '_R_V_i_n_d_e_x': Object of class '"vector"' containing the indexes of
          the resulting relevance vectors 

     '_n_R_V': Object of class '"numeric"' containing the number of
          relevance vectors

     '_c_r_o_s_s': Object of class '"numeric"' containing the relusting
          cross validation error 

     '_e_r_r_o_r': Object of class '"numeric"' containing the training error

     '_n._a_c_t_i_o_n': Object of class '"ANY"' containing the action
          performed on NA

_M_e_t_h_o_d_s:

     _R_V_i_n_d_e_x 'signature(object = "rvm")': returns the index of the
          relevance vectors 

     _a_l_p_h_a 'signature(object = "rvm")': returns the resulting alpha
          vector

     _c_r_o_s_s 'signature(object = "rvm")': returns the resulting cross
          validation error

     _e_r_r_o_r 'signature(object = "rvm")': returns the training error  

     _f_i_t_t_e_d 'signature(object = "vm")': returns the fitted values 

     _k_c_a_l_l 'signature(object = "rvm")': returns the function call 

     _k_e_r_n_e_l_f 'signature(object = "rvm")': returns the used kernel
          function 

     _k_p_a_r 'signature(object = "rvm")': returns the parameters of the
          kernel function

     _l_e_v 'signature(object = "rvm")': returns the levels of the
          response (in classification)

     _m_l_i_k_e 'signature(object = "rvm")': returns the estimated maiximum
          likelihood

     _n_v_a_r 'signature(object = "rvm")': returns the calculated variance
          (in regression)

     _t_y_p_e 'signature(object = "rvm")': returns the type of problem

     _x_m_a_t_r_i_x 'signature(object = "rvm")': returns the data mmatrix used
          during computation

     _y_m_a_t_r_i_x 'signature(object = "rvm")': returns the used response 

_A_u_t_h_o_r(_s):

     Alexandros Karatzoglou
      alexandros.karatzoglou@ci.tuwien.ac.at

_S_e_e _A_l_s_o:

     'rvm',  'ksvm-class'

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

     # create data
     x <- seq(-20,20,0.1)
     y <- sin(x)/x + rnorm(401,sd=0.05)

     # train relevance vector machine
     foo <- rvm(x, y)
     foo

     alpha(foo)
     RVindex(foo)
     fitted(foo)
     kernelf(foo)
     nvar(foo)

     ## show slots
     slotNames(foo)

