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)

