scdaCMA                 package:CMA                 R Documentation

_S_h_r_u_n_k_e_n _C_e_n_t_r_o_i_d_s _D_i_s_c_r_i_m_i_n_a_n_t _A_n_a_l_y_s_i_s

_D_e_s_c_r_i_p_t_i_o_n:

     The nearest shrunken centroid classification algorithm is detailly
     described in Tibshirani et al. (2002).

     It is widely known under the name PAM (prediction analysis for
     microarrays), which can also be found in the package 'pamr'.

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

_U_s_a_g_e:

     scdaCMA(X, y, f, learnind, delta = 0.5, ...)

_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.

   delta: The shrinkage intensity for the class centroids - a
          hyperparameter that must be tuned. The default '0.5' not
          necessarily produces good results.

     ...: Currently unused argument.

_V_a_l_u_e:

     An object of class 'cloutput'.

_N_o_t_e:

     The results can differ from those obtained by using the package
     'pamr'.

_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:

     Tibshirani, R., Hastie, T., Narasimhan, B., and Chu, G., (2003).

     Class prediction by nearest shrunken centroids with applications
     to DNA microarrays.

     _Statistical Science, 18, 104-117_

_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', 'shrinkldaCMA', 'svmCMA'

_E_x_a_m_p_l_e_s:

     ### load Khan data
     data(khan)
     ### extract class labels
     khanY <- khan[,1]
     ### extract gene expression
     khanX <- as.matrix(khan[,-1])
     ### select learningset
     set.seed(111)
     learnind <- sample(length(khanY), size=floor(2/3*length(khanY)))
     ### run Shrunken Centroids classfier, without tuning
     scdaresult <- scdaCMA(X=khanX, y=khanY, learnind=learnind)
     ### show results
     show(scdaresult)
     ftable(scdaresult)
     plot(scdaresult)

