fdaCMA                  package:CMA                  R Documentation

_F_i_s_h_e_r'_s _L_i_n_e_a_r _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:

     Fisher's Linear Discriminant Analysis constructs a subspace of
     'optimal projections' in which classification is performed. The
     directions of optimal projections are computed by the function
     'cancor' from the package 'stats'. For an exhaustive treatment,
     see e.g. Ripley (1996).

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

_U_s_a_g_e:

     fdaCMA(X, y, f, learnind, comp = 1, plot = 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.

    comp: Number of discriminant coordinates (projections) to compute.
          Default is one, must be smaller than or equal to 'K-1', where
          'K' is the number of classes.

    plot: Should the projections onto the space spanned by the optimal
          projection directions be plotted ? Default is 'FALSE'.

_V_a_l_u_e:

     An object of class 'cloutput'.

_N_o_t_e:

     Excessive variable selection has usually to performed before
     'fdaCMA' 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:

     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 FDA
     fdaresult <- fdaCMA(X=golubX, y=golubY, learnind=learnind, comp = 1, plot = TRUE)
     ### show results
     show(fdaresult)
     ftable(fdaresult)
     plot(fdaresult)
     ### multiclass example:
     ### load Khan data
     data(khan)
     ### extract class labels
     khanY <- khan[,1]
     ### extract gene expression from first 10 genes
     khanX <- as.matrix(khan[,2:11])
     ### select learningset
     set.seed(111)
     learnind <- sample(length(khanY), size=floor(ratio*length(khanY)))
     ### run FDA
     fdaresult <- fdaCMA(X=khanX, y=khanY, learnind=learnind, comp = 2, plot = TRUE)
     ### show results
     show(fdaresult)
     ftable(fdaresult)
     plot(fdaresult)

