

knn(class)                                   R Documentation

_k_-_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_:

     k-nearest neighbour classification for test set from
     training set. For each row of the test set, the k near-
     est (in Euclidean distance) training set vectors are
     found, and the classification is decided by majority
     vote, with ties broken at random. If there are ties for
     the kth nearest vector, all candidates are included in
     the vote.

_U_s_a_g_e_:

     knn(train, test, class, k=1, l=1, prob=FALSE, use.all=TRUE)

_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.

   class: factor of true classifications of training set

       k: number of neighbours considered.

       l: minimum vote for definite decision, otherwise
          `doubt'. (More precisely, less than `k-l' dissent-
          ing votes are allowed, even if `k' is increased by
          ties.)

    prob: If this is true, the proportion of the votes for
          the winning class are returned as attribute
          `prob'.

 use.all: controls handling of ties. If true, all distances
          equal to the `k'th largest are included. If false,
          a random selection of distances equal to the `k'th
          is chosen to use exactly `k' neighbours.

_V_a_l_u_e_:

     factor of classifications of test set. `doubt' will be
     returned as `NA'.

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

     `knn1', `knn.cv'

_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)))
     knn(train, test, cl, k=3, prob=TRUE)
     attributes(.Last.value)

