sknn                  package:klaR                  R Documentation

_S_i_m_p_l_e _k _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:

     Function for simple knn classification.

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

     sknn(x, ...)

     ## Default S3 method:
     sknn(x, grouping, kn = 3, gamma=0, ...)
     ## S3 method for class 'data.frame':
     sknn(x, ...)
     ## S3 method for class 'matrix':
     sknn(x, grouping, ..., subset, na.action = na.fail)
     ## S3 method for class 'formula':
     sknn(formula, data = NULL, ..., subset, na.action = na.fail)

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

       x: matrix or data frame containing the explanatory variables 
          (required, if 'formula' is not given).

grouping: factor specifying the class for each observation  (required,
          if 'formula' is not given).

 formula: 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.

      kn: Number of nearest neighbours to use.

   gamma: gamma parameter for rbf in knn. If 'gamma=0' ordinary knn
          classification is used.

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

na.action: specify the action to be taken if 'NA's 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.) 

     ...: 

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

     If 'gamma>0' an gaussian like density is used to weight the
     classes of the 'kn' nearest neighbors. 
     'weight=exp(-gamma*distance)'. This is similar to an rbf kernel. 
     If the distances are large it may be useful to 'scale' the data
     first.

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

     A list containing the function call.

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

     Karsten Luebke, luebke@statistik.uni-dortmund.de

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

     'predict.sknn', 'knn'

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

     data(iris)
     x <- sknn(Species ~ ., data = iris)
     x <- sknn(Species ~ ., gamma = 4, data = iris)

