convol               package:KernGPLM               R Documentation

_K_e_r_n_e_l _c_o_n_v_o_l_u_t_i_o_n

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

     Calculates the convolution of data with a kernel function.

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

     convol(x, h = 1, grid = NULL, y = 1, w = 1, p = 2, q = 2,
            product = TRUE, sort = TRUE)

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

       x: n x d matrix, data

       h: scalar or 1 x d, bandwidth(s)

    grid: m x d matrix, where to calculate the convolution (default =
          x)

       y: n x c matrix, optional responses

       w: scalar or  n x 1 or 1 x m or n x m, optional weights

       p: integer or text, see 'kernel.function'

       q: integer, see 'kernel.function'

 product: (if d>1) product or spherical kernel

    sort: sort the data, necessary to use the DLL code

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

     The kernel convolution which is calculated is sum_i K_h(x_i -
     grid_{j}),y_i,w_{ij} for i=1,...,n and j=1,...,m.

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

     m x c matrix

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

     Marlene Mueller

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

     'kernel.function', 'kde', 'kreg'

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

       n <- 100
       x <- rnorm(n)
       convol(x,h=0.8,grid=-3:3)/n  ## estimates density of x at points -3:3

