ksvm-class              package:kernlab              R Documentation

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

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

     An S4 class containing the output (model) of the 'ksvm' Support
     Vector Machines function

_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("ksvm", ...)' or
     by calls to the 'ksvm' function.

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

     '_t_y_p_e': Object of class '"character"'  containing the support
          vector machine type ("C-svc", "nu-svc", "C-bsvc", "spoc-svc",
          "one-svc", "eps-svr", "nu-svr", "eps-bsvr")

     '_p_a_r_a_m': Object of class '"list"' containing the Support Vector
          Machine parameters (C, nu, epsilon)

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

     '_k_p_a_r': Object of class '"list"' containing the kernel function
          parameters (hyperparameters)

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

     '_s_c_a_l_i_n_g': Object of class '"ANY"' containing the scaling
          information performed on the data

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

     '_x_m_a_t_r_i_x': Object of class '"input"' ('"list"' for multiclass
          problems  or '"matrix"' for binary classification and
          regression problems) containing the support vectors
          caclulated from the data matrix used during computations
          (possibly scaled and whithout NA). In the case of multi-class
          classification each list entry contains the support vectors
          from each binary classification problem from the
          one-against-one method.

     '_y_m_a_t_r_i_x': Object of class '"output"' the response '"matrix"' or
          '"factor"' or '"vector"' or '"logical"'

     '_f_i_t_t_e_d': Object of class '"output"' with the fitted values,
          predictions using the training set.

     '_l_e_v': Object of class '"vector"' with the levels of the response
          (in the case of classifiaction)

     '_p_r_o_b._m_o_d_e_l': Object of class '"list"' with the class prob. model

     '_p_r_i_o_r': Object of class '"list"' with the prior of the training
          set

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

     '_a_l_p_h_a': Object of class '"listI"' containing the resulting alpha
          vector ('"list"' or '"matrix"' in case of multiclass
          classification) (support vectors)

     '_c_o_e_f': Object of class '"ANY"' containing the resulting
          coefficients

     '_a_l_p_h_a_i_n_d_e_x': Object of class '"list"' containing

     '_b': Object of class '"numeric"' containing the resulting offset 

     '_S_V_i_n_d_e_x': Object of class '"vector"' containing the indexes of
          the support vectors

     '_n_S_V': Object of class '"numeric"' containing the number of
          suppport vector machines 

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

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

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

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

     _S_V_i_n_d_e_x 'signature(object = "ksvm")': return the indexes of
          support vectors

     _a_l_p_h_a 'signature(object = "ksvm")': returns the complete 5   
          alpha vector (wit zero values)

     _a_l_p_h_a_i_n_d_e_x 'signature(object = "ksvm")': returns the indexes of
          non-zero alphas (support vectors)

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

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

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

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

     _k_p_a_r 'signature(object = "ksvm")': returns the kernel parameters
          (hyperparameters)

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

     _p_r_o_b._m_o_d_e_l 'signature(object="ksvm")': returns class prob. model
          values

     _p_r_i_o_r 'signature(object="ksvm")': returns  the prior of the
          training set

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

     _s_c_a_l_i_n_g 'signature(object = "ksvm")': returns the scaling values 

     _s_h_o_w 'signature(object = "ksvm")': prints the object information

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

     _x_m_a_t_r_i_x 'signature(object = "ksvm")': returns the data matrix used

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

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

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

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

     'ksvm',  'rvm-class', 'gausspr-class'

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

     ## simple example using the promotergene data set
     data(promotergene)

     ## train a support vector machine
     gene <- ksvm(Class~.,data=promotergene,kernel="rbfdot",kpar=list(sigma=0.015),C=50,cross=4)
     gene

     # the kernel  function
     kernelf(gene)
     # the alpha values
     alpha(gene)
     # the coefficients
     coef(gene)
     # the fitted values
     fitted(gene)
     # the cross validation error
     cross(gene)

