pls_lrCMA                package:CMA                R Documentation

_P_a_r_t_i_a_l _L_e_a_s_t _S_q_u_a_r_e_s _f_o_l_l_o_w_e_d _b_y _l_o_g_i_s_t_i_c _r_e_g_r_e_s_s_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     This method constructs a classifier that extracts Partial Least
     Squares components that form the the covariates in a binary
     logistic regression model. The Partial Least Squares components
     are computed by the package 'plsgenomics'.

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

_U_s_a_g_e:

     pls_lrCMA(X, y, f, learnind, comp = 2, lambda = 1e-4, 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'.

  lambda: Parameter controlling the amount of L2 penalization for
          logistic regression, usually taken to be a small value in
          order to stabilize estimation in the case of separable data.

    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'.

_N_o_t_e:

     Up to now, only the two-class case is supported.

_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:

     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_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
     golubX <- as.matrix(golub[,-1])
     ### select learningset
     ratio <- 2/3
     set.seed(111)
     learnind <- sample(length(golubY), size=floor(ratio*length(golubY)))
     ### run PLS, combined with logistic regression
     result <- pls_lrCMA(X=golubX, y=golubY, learnind=learnind)
     ### show results
     show(result)
     ftable(result)
     plot(result)

