mse2d                package:splancs                R Documentation

_M_e_a_n _S_q_u_a_r_e _E_r_r_o_r _f_o_r _a _K_e_r_n_e_l _S_m_o_o_t_h_i_n_g

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

     Estimate the Mean Square Error for a Kernel Smoothing.

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

     mse2d(pts,poly,nsmse, range)

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

     pts: A set of points. 

    poly: A polygon containng the points. 

   nsmse: Number of steps of `h' at which to calculate the mean square
          error. 

   range: Maximum value of `h' for calculating the mean square error. 

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

     A list with two components, `$h' and `$mse'. These vectors store
     corresponding values of the mean square error at values of the
     kernel smoothing parameter, `h'.  The value of `h' corresponding
     to the minimum value of `$mse' can be passed to `kernel2d' as the
     optimum smoothing parameter.

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

     Berman M. & Diggle P.J. (1989) Estimating Weighted Integrals of
     the Second-Order Intensity of a Spatial Point Pattern.  J. R.
     Statist Soc B 51 81-92; Rowlingson, B. and Diggle, P. 1993
     Splancs: spatial point pattern analysis code in S-Plus.  Computers
     and Geosciences, 19, 627-655; the original sources can be accessed
     at: <URL: http://www.maths.lancs.ac.uk/~rowlings/Splancs/>. See
     also Bivand, R. and Gebhardt, A. 2000 Implementing functions for
     spatial statistical analysis using the R language. Journal of
     Geographical Systems, 2, 307-317.

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

     `kernel2d'

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

     data(bodmin)
     Mse2d <- mse2d(as.points(bodmin), bodmin$poly, nsmse=50, range=8)
     plot(Mse2d$h[5:50],Mse2d$mse[5:50], type="l")

