qdaCMA                  package:CMA                  R Documentation

_Q_u_a_d_r_a_t_i_c _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:

     Performs a quadratic discriminant analysis under the assumption of
     a multivariate normal distribution in each classes without
     restriction concerning the covariance matrices. The function 'qda'
     from the package 'MASS' is called for computation.

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

_U_s_a_g_e:

     qdaCMA(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 'qda' from the package
          'MASS'

_V_a_l_u_e:

     An object of class 'cloutput'.

_N_o_t_e:

     Excessive variable selection has usually to performed before
     'qdaCMA' can be applied in the 'p > n' setting. Not reducing the
     number of variables can result in an error message.

_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:

     McLachlan, G.J. (1992).

     Discriminant Analysis and Statistical Pattern Recognition.

     _Wiley, New York_

_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', 
     '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 3 genes
     golubX <- as.matrix(golub[,2:4])
     ### select learningset
     ratio <- 2/3
     set.seed(112)
     learnind <- sample(length(golubY), size=floor(ratio*length(golubY)))
     ### run QDA
     qdaresult <- qdaCMA(X=golubX, y=golubY, learnind=learnind)
     ### show results
     show(qdaresult)
     ftable(qdaresult)
     plot(qdaresult)
     ### multiclass example:
     ### load Khan data
     data(khan)
     ### extract class labels
     khanY <- khan[,1]
     ### extract gene expression from first 4 genes
     khanX <- as.matrix(khan[,2:5])
     ### select learningset
     set.seed(111)
     learnind <- sample(length(khanY), size=floor(ratio*length(khanY)))
     ### run QDA
     qdaresult <- qdaCMA(X=khanX, y=khanY, learnind=learnind)
     ### show results
     show(qdaresult)
     ftable(qdaresult)
     plot(qdaresult)

