meclight.default            package:klaR            R Documentation

_M_i_n_i_m_a_l _E_r_r_o_r _C_l_a_s_s_i_f_i_c_a_t_i_o_n

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

     Computer intensive method for linear dimension reduction that
     minimizes the classification error directly.

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

     meclight(x, ...)

     ## Default S3 method:
     meclight(x, grouping, r = 1, fold = 10, ...)
     ## S3 method for class 'formula':
     meclight(formula, data = NULL, ..., subset, na.action = na.fail)
     ## S3 method for class 'data.frame':
     meclight(x, ...)
     ## S3 method for class 'matrix':
     meclight(x, grouping, ..., subset, na.action = na.fail)

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

       x: (required if no formula is given as the principal argument.)
          A matrix or data frame  containing the explanatory variables.

grouping: (required if no formula principal argument is given.) A
          factor specifying the class for each observation.

       r: Dimension of projected subspace.

    fold: Number of Bootstrap samples.

 formula: A formula of the form 'groups ~ x1 + x2 + ...'. That is, the
          response is the grouping factor and  the right hand side
          specifies the (non-factor) discriminators. 

    data: Data frame from which variables specified in formula are
          preferentially to be taken.

  subset: An index vector specifying the cases to be used in the
          training sample.  (NOTE: If given, this argument must be
          named.)

na.action: A function to specify the action to be taken if NAs are
          found.  The default action is for the procedure to fail.  An
          alternative is 'na.omit',  which leads to rejection of cases
          with missing values on any required variable.  (NOTE: If
          given, this argument must be named.) 

     ...: Further arguments passed to 'lda'.

_D_e_t_a_i_l_s:

     Computer intensive method for linear dimension reduction that
     minimizes the classification error in the projected subspace
     directly. Classification is done by 'lda'. In contrast to the
     reference function minimization is done by Nelder-Mead in 'optim'.

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

method.model: An object of class 'lda'.

Proj.matrix: Projection matrix.

 B.error: Estimated bootstrap error rate.

 B.impro: Improvement in 'lda' error rate.

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

     Maria Eveslage, Karsten Luebke

_R_e_f_e_r_e_n_c_e_s:

     Roehl, M.C., Weihs, C., and Theis, W. (2002):  Direct Minimization
     in Multivariate Classification. _Computational Statistics_, 17,
     29-46.

_S_e_e _A_l_s_o:

     'predict.meclight'

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

     data(iris)
     meclight.obj <- meclight(Species ~ ., data = iris)
     meclight.obj

