ClassifierBuild         package:MCRestimate         R Documentation

_B_u_i_l_d_i_n_g _a _c_l_a_s_s_i_f_i_e_r _a_s _a _c_o_m_b_i_n_a_t_i_o_n _o_f _p_r_e_p_r_o_c_e_s_s_i_n_g _a_n_d
_c_l_a_s_s_i_f_i_c_a_t_i_o_n _m_e_t_h_o_d

_D_e_s_c_r_i_p_t_i_o_n:

_U_s_a_g_e:

     ClassifierBuild(eset,
                     class.column,
                     reference.class=NULL,
                     classification.fun,
                     variableSel.fun ="identity",
                     cluster.fun ="identity",
                     poss.parameters=list(),
                     cross.inner=10,
                     rand=123,
                     information=TRUE,
                     thePreprocessingMethods=c(variableSel.fun,cluster.fun))

_A_r_g_u_m_e_n_t_s:

    eset: an object of class 'exprSet' or 'exprSetRG' 

class.column: a number or a character string which indicated the column
          of the expression set's phenodata containing the class label

reference.class: a character string with the  name of one class - if
          specified the class will form the first class and all the
          other classes will form the second class 

classification.fun: a character string which names the function that
          should be used for the classification

variableSel.fun: character string which names the function that should
          be used for variable selection

cluster.fun: character string which names the function that should be
          used for clustering the variables

thePreprocessingMethods: vector of character with the names of all
          preprocessing functions- can be used instead of
          'variableSel.fun' and 'cluster.fun' - see details

poss.parameters: a list of possible values for the parameter of the
          classification method

cross.inner: integer - the number of nearly equal sized parts the train
          set should be divided into

    rand: integer - the random number generator will be put in a
          reproducible state

information: information - should classificator specific data be
          given(depends on the wrapper for the classification method)

_V_a_l_u_e:

     a 'list' with the following arguments: 

classifier.for.matrix: 

classifier.for.exprSet: 

parameter: a list consisting of the estimated 'best' parameter for each
          cross-validation part

class.method: string which names the function used for the
          classification

thePreprocessingMethods: character string - name of the preprocessing
          functions that have been used

cross.inner: number of blocks for a the inner cross-validation

information: classificator specific data

_A_u_t_h_o_r(_s):

     Markus Ruschhaupt <URL: mailto:m.ruschhaupt@dkfz.de>

_E_x_a_m_p_l_e_s:

     ## please have a look at the vignette of this package

