knn1                  package:class                  R Documentation

_1-_n_e_a_r_e_s_t _n_e_i_g_h_b_o_u_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:

     Nearest neighbour classification for test set from training set.
     For each row of the test set, the nearest (by Euclidean distance)
     training set vector is found, and its classification used. If
     there is more than one nearest, a majority vote is used with ties
     broken at random.

_U_s_a_g_e:

     knn1(train, test, cl)

_A_r_g_u_m_e_n_t_s:

   train: matrix or data frame of training set cases. 

    test: matrix or data frame of test set cases. A vector will be
          interpreted as a row vector for a single case. 

      cl: factor of true classification of training set. 

_V_a_l_u_e:

     factor of classifications of test set.

_R_e_f_e_r_e_n_c_e_s:

     Ripley, B. D. (1996) _Pattern Recognition and Neural Networks._
     Cambridge.

     Venables, W. N. and Ripley, B. D. (2002) _Modern Applied
     Statistics with S._ Fourth edition.  Springer.

_S_e_e _A_l_s_o:

     'knn'

_E_x_a_m_p_l_e_s:

     data(iris3)
     train <- rbind(iris3[1:25,,1], iris3[1:25,,2], iris3[1:25,,3])
     test <- rbind(iris3[26:50,,1], iris3[26:50,,2], iris3[26:50,,3])
     cl <- factor(c(rep("s",25), rep("c",25), rep("v",25)))
     knn1(train, test, cl)

