nnetCMA                 package:CMA                 R Documentation

_F_e_e_d-_f_o_r_w_a_r_d _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:

     This method provides access to the function 'nnet' in the package
     of the same name that trains Feed-forward Neural Networks with one
     hidden layer.

     For 'S4' method information, see nnetCMA-methods

_U_s_a_g_e:

     nnetCMA(X, y, f, learnind, eigengenes = FALSE, ...)

_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.

eigengenes: Should the training be performed be in the space of
          eigengenes obtained from a singular value decomposition of
          the Gene expression data matrix ? Default is 'FALSE'; in this
          case, variable selection is necessary to reduce the number of
          weights that have to be optimized.

     ...: Further arguments passed to the function 'nnet' from the
          package of the same name.

          Important parameters are:

             *  '"size"', i.e. the number of units in the hidden layer

             *  '"decay"' for weight decay.

_V_a_l_u_e:

     An object of class 'cloutput'.

_N_o_t_e:


        *  Excessive variable selection is usually necessary if
           'eigengenes = FALSE'

        *  Different runs of this method on the same dataset not
           necessarily produce the same results due to the fact that
           optimization for Feed-Forward Neural Networks is rather
           difficult and depends on the choice of (normally randomly
           chosen) starting values for the network weights.

_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:

     Ripley, B.D. (1996)

     Pattern Recognition and Neural Networks.

     _Cambridge University Press_

_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', '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 nnet (not tuned)
     nnetresult <- nnetCMA(X=golubX, y=golubY, learnind=learnind, size = 3, decay = 0.01)
     ### show results
     show(nnetresult)
     ftable(nnetresult)
     plot(nnetresult)
     ### in the space of eigengenes (not tuned)
     golubXfull <-  as.matrix(golubX[,-1])
     nnetresult <- nnetCMA(X=golubXfull, y=golubY, learnind = learnind, eigengenes = TRUE,
                           size = 3, decay = 0.01)
     ### show results
     show(nnetresult)
     ftable(nnetresult)
     plot(nnetresult)

