knnCMA                  package:CMA                  R Documentation

_N_e_a_r_e_s_t _N_e_i_g_h_b_o_u_r_s

_D_e_s_c_r_i_p_t_i_o_n:

     Ordinary 'k' nearest neighbours algorithm from the very fast
     implementation in the package 'class'.

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

_U_s_a_g_e:

     knnCMA(X, y, f, learnind, ...)

_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. Must not be missing for this method.

     ...: Further arguments to be passed to 'knn' from the package
          'class', in particular the number of nearest neighbours to
          use (argument 'k').

_V_a_l_u_e:

     An object of class 'cloutput'.

_N_o_t_e:

     Class probabilities are _not_ returned. For a probabilistic
     variant of 'knn', s. 'pknnCMA'.

_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', '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[,-1])
     ### select learningset
     ratio <- 2/3
     set.seed(111)
     learnind <- sample(length(golubY), size=floor(ratio*length(golubY)))
     ### run k-nearest neighbours
     result <- knnCMA(X=golubX, y=golubY, learnind=learnind, k = 3)
     ### show results
     show(result)
     ftable(result)
     ### multiclass example:
     ### 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(ratio*length(khanY)))
     ### run knn
     result <- knnCMA(X=khanX, y=khanY, learnind=learnind, k = 5)
     ### show results
     show(result)
     ftable(result)

