pls_ldaCMA                package:CMA                R Documentation

_P_a_r_t_i_a_l _L_e_a_s_t _S_q_u_a_r_e_s _c_o_m_b_i_n_e_d _w_i_t_h _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:

     This method constructs a classifier that extracts Partial Least
     Squares components that are plugged into Linear Discriminant
     Analysis. The Partial Least Squares components are computed by the
     package 'plsgenomics'.

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

_U_s_a_g_e:

     pls_ldaCMA(X, y, f, learnind, comp = 2, 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 Partial Least Squares components to extract.
          Default is 2 which can be suboptimal, depending on the
          particular dataset. Can be optimized using 'tune'.

    plot: If 'comp <= 2', should the classification space of the
          Partial Least Squares components be plotted ? Default is
          'FALSE'.

_V_a_l_u_e:

     An object of class 'cloutput'.

_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:

     Nguyen, D., Rocke, D. M., (2002).

     Tumor classifcation by partial least squares using microarray gene
     expression data.

     _Bioinformatics 18, 39-50_

     Boulesteix, A.L.,  Strimmer, K. (2007).

     Partial least squares: a versatile tool for the analysis of
     high-dimensional genomic data.

     _Briefings in Bioinformatics 7:32-44._

_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 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
     plsresult <- pls_ldaCMA(X=khanX, y=khanY, learnind=learnind, comp = 4)
     ### show results
     show(plsresult)
     ftable(plsresult)
     plot(plsresult)

