nnbr                   package:sm                   R Documentation

_n_e_a_r_e_s_t _n_e_i_g_h_b_o_u_r _d_i_s_t_a_n_c_e_s _f_r_o_m _d_a_t_a _i_n _o_n_e _o_r _t_w_o _d_i_m_e_n_s_i_o_n_s

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

     This function calculates the `k' nearest neighbour distance from
     each value in `x' to the remainder of the data.  In two
     dimensions, Euclidean distance is used after standardising the
     data to have unit variance in each component.

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

     nnbr(x, k)

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

       x: the vector, or two-column matrix, of data. 

       k: the required order of nearest neighbour. 

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

     see Section 1.7.1 of the reference below.

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

     the vector of nearest neighbour distances.

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

     Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques
     for Data Analysis: the Kernel Approach with S-Plus Illustrations.
     Oxford University Press, Oxford.

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

     none.

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

     x  <- rnorm(50)
     hw <- nnbr(x, 10)
     hw <- hw/exp(mean(log(hw)))
     sm.density(x, h.weights=hw)

