svmCMA                  package:CMA                  R Documentation

_S_u_p_p_o_r_t _V_e_c_t_o_r _M_a_c_h_i_n_e

_D_e_s_c_r_i_p_t_i_o_n:

     Calls the function 'svm' from the package 'e1071' that provides an
     interface to the award-winning LIBSVM routines. For 'S4' method
     information, see svmCMA-methods

_U_s_a_g_e:

     svmCMA(X, y, f, learnind, ...)

_A_r_g_u_m_e_n_t_s:

       X: Gene expression data. Can be one of the following:

             *  A 'matrix'. Rows correspond to observations, columns to
                variables.

             *  A 'data.frame', when 'f' is _not_ missing (s. below).

             *  An object of class 'ExpressionSet'.

       y: Class labels. Can be one of the following:

             *  A 'numeric' vector.

             *  A 'factor'.

             *  A 'character' if 'X' is an 'ExpressionSet' that
                specifies the phenotype variable.

             *  'missing', if 'X' is a 'data.frame' and a proper
                formula 'f' is provided.

          *WARNING*: The class labels will be re-coded to range from
          '0' to 'K-1', where 'K' is the total number of different
          classes in the learning set. 

       f: A two-sided formula, if 'X' is a 'data.frame'. The left part
          correspond to class labels, the right to variables.

learnind: An index vector specifying the observations that belong to
          the learning set. May be 'missing'; in that case, the
          learning set consists of all observations and predictions are
          made on the learning set.

     ...: Further arguments to be passed to 'svm' from the package
          'e1071'

_V_a_l_u_e:

     An object of class 'cloutput'.

_N_o_t_e:

     Contrary to the default settings in 'e1071:::svm', the used kernel
     is a linear kernel which has turned to be out a better default
     setting in the small sample, large number of predictors -
     situation, because additional nonlinearity is mostly not necessary
     there. It additionally avoids the tuning of a further kernel
     parameter 'gamma', s. help of the package 'e1071' for details.

     Nevertheless, hyperparameter tuning concerning the parameter
     'cost' must usually be performed to obtain reasonale results, s.
     'tune'.

_A_u_t_h_o_r(_s):

     Martin Slawski martin.slawski@campus.lmu.de

     Anne-Laure Boulesteix <URL: http://www.slcmsr.net/boulesteix>

_R_e_f_e_r_e_n_c_e_s:

     Boser, B., Guyon, I., Vapnik, V.  (1992)

     A training algorithm for optimal margin classifiers.

     _Proceedings of the fifth annual workshop on Computational
     learning theory, pages 144-152, ACM Press_.

     Chang, Chih-Chung and Lin, Chih-Jen : LIBSVM: a library for
     Support Vector Machines <URL:
     http://www.csie.ntu.edu.tw/~cjlin/libsvm>

     Schoelkopf, B., Smola, A.J. (2002)

     Learning with kernels. _MIT Press, Cambridge, MA._

_S_e_e _A_l_s_o:

     'compBoostCMA', 'dldaCMA', 'ElasticNetCMA', 'fdaCMA', 'flexdaCMA',
     'gbmCMA', 'knnCMA', 'ldaCMA', 'LassoCMA', 'nnetCMA', 'pknnCMA',
     'plrCMA', 'pls_ldaCMA', 'pls_lrCMA', 'pls_rfCMA', 'pnnCMA',
     'qdaCMA', 'rfCMA', 'scdaCMA', 'shrinkldaCMA'

_E_x_a_m_p_l_e_s:

     ### load Golub AML/ALL data
     data(golub)
     ### extract class labels
     golubY <- golub[,1]
     ### extract gene expression
     golubX <- as.matrix(golub[,-1])
     ### select learningset
     ratio <- 2/3
     set.seed(111)
     learnind <- sample(length(golubY), size=floor(ratio*length(golubY)))
     ### run _untuned_linear SVM
     svmresult <- svmCMA(X=golubX, y=golubY, learnind=learnind)
     ### show results
     show(svmresult)
     ftable(svmresult)
     plot(svmresult)

