flexdaCMA                package:CMA                R Documentation

_F_l_e_x_i_b_l_e _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:

     This method is experimental.

     It is easy to show that, after appropriate scaling of the
     predictor matrix 'X', Fisher's Linear Discriminant Analysis is
     equivalent to Discriminant Analysis in the space of the  fitted
     values from the linear regression of the 'nlearn x K' indicator
     matrix of the class labels on 'X'. This gives rise to 'nonlinear
     discrimant analysis' methods that expand 'X' in a suitable, more
     flexible basis. In order to avoid overfitting, penalization is
     used. In the implemented version, the linear model is replaced by
     a generalized additive one, using the package 'mgcv'.

     For 'S4' method information, s. 'flexdaCMA-methods'.

_U_s_a_g_e:

     flexdaCMA(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'.

     ...: Further arguments passed to the function 'gam' from the
          package 'mgcv'.

_V_a_l_u_e:

     An object of class 'cloutput'.

_N_o_t_e:

     Excessive variable selection has usually to performed before
     'flexdaCMA' can be applied in the 'p > n' setting. Recall that the
     original predictor dimension is even enlarged, therefore, it
     should be applied only with very few variables.

_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', '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 5 genes
     golubX <- as.matrix(golub[,2:6])
     ### select learningset
     ratio <- 2/3
     set.seed(111)
     learnind <- sample(length(golubY), size=floor(ratio*length(golubY)))
     ### run flexible Discriminant Analysis
     result <- flexdaCMA(X=golubX, y=golubY, learnind=learnind, comp = 1)
     ### show results
     show(result)
     ftable(result)
     plot(result)

