pnnCMA                  package:CMA                  R Documentation

_P_r_o_b_a_b_i_l_i_s_t_i_c _N_e_u_r_a_l _N_e_t_w_o_r_k_s

_D_e_s_c_r_i_p_t_i_o_n:

     Probabilistic Neural Networks is the term Specht (1990) used for a
     Gaussian kernel estimator for the conditional class densities.

     For 'S4' method information, see pnnCMA-methods.

_U_s_a_g_e:

     pnnCMA(X, y, f, learnind, sigma = 1)

_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'.

                Each variable (gene) will be scaled for unit variance
                and zero mean.

       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. For this method, this must _not_ be
          'missing'.

   sigma: Standard deviation of the Gaussian Kernel used.

          This hyperparameter should be tuned, s. 'tune'. The default
          is '1', but this generally  does not lead to good results.
          Actually, this method reacts very sensitively to the value of
          sigma. Take care if warnings appear related to the particular
          choice.

_V_a_l_u_e:

     An object of class 'cloutput'.

_N_o_t_e:

     There is actually no strong relation of this method to
     Feed-Forward Neural Networks, s. 'nnetCMA'.

_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:

     Specht, D.F. (1990).

     Probabilistic Neural Networks. _Neural Networks, 3, 109-118_.

_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', 'qdaCMA',
     'rfCMA', 'scdaCMA', 'shrinkldaCMA', 'svmCMA'

_E_x_a_m_p_l_e_s:

     ### load Golub AML/ALL data
     data(golub)
     ### extract class labels
     golubY <- golub[,1]
     ### extract gene expression from first 10 genes
     golubX <- as.matrix(golub[,2:11])
     ### select learningset
     ratio <- 2/3
     set.seed(111)
     learnind <- sample(length(golubY), size=floor(ratio*length(golubY)))
     ### run PNN
     pnnresult <- pnnCMA(X=golubX, y=golubY, learnind=learnind, sigma = 3)
     ### show results
     show(pnnresult)
     ftable(pnnresult)
     plot(pnnresult)

