pls_rfCMA                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 _r_a_n_d_o_m _f_o_r_e_s_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     This method constructs a classifier that extracts Partial Least
     Squares components used to generate Random Forests, s. 'rfCMA'.

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

_U_s_a_g_e:

     pls_rfCMA(X, y, f, learnind, comp = 2 * nlevels(as.factor(y)), seed = 111, ...)

_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 ist two times the number of different classes.

    seed: Fix Random number generator seed to 'seed'. This is useful to
          guarantee reproducibility of the results, due to the random
          component in the random Forest.

     ...: Further arguments to be passed to 'randomForests' from the
          package of the same name.

_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:

     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', '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 Random Forest
     result <- pls_rfCMA(X=golubX, y=golubY, learnind=learnind)
     ### show results
     show(result)
     ftable(result)
     plot(result)

