kde                    package:ks                    R Documentation

_K_e_r_n_e_l _d_e_n_s_i_t_y _e_s_t_i_m_a_t_e _f_o_r _b_i_v_a_r_i_a_t_e _d_a_t_a

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

     Kernel density estimate for bivariate data.

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

      kde(x, H, gridsize, supp=3.7, eval.points)

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

       x: matrix of data values

       H: bandwidth matrix

gridsize: vector of number of grid points

    supp: effective support for standard normal is ['-supp, supp']

eval.points: points that density estimate is evaluated at

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

     The kernel density estimate is computed exactly i.e. binning is
     not used.

     If 'gridsize' is not set to a specific value, then it defaults to
     50 grid points in each co-ordinate direction i.e. 'c(50,50)'. Not
     required to be set if specifying 'eval.points'.

     If 'eval.points' is not specified, then the density estimate is
     automatically computed over a grid whose resolution is controlled
     by 'gridsize' (a grid is required for plotting).

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

     Kernel density estimate is an object of class 'kde' which is a
     list with 4 fields 

       x: data points - same as input

eval.points: points that density estimate is evaluated at

estimate: density estimate at 'eval.points'

       H: bandwidth matrix

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

     Wand, M.P. & Jones, M.C. (1995) _Kernel Smoothing_. Chapman &
     Hall. London.

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

     'plot.kde'

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

     data(unicef)
     H.pi <- Hpi(unicef, nstage=1)
     H.pi1 <- invvech(c(797.5755, -106.63338, 19.56761))
     fhat <- kde(unicef, H.pi)

