genie              package:approximator              R Documentation

_G_e_n_i_e _d_a_t_a_s_e_t_s _f_o_r _a_p_p_r_o_x_i_m_a_t_o_r _p_a_c_k_a_g_e

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

     Genie datasets that illustrate the package.

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

       data(genie)
       D1.genie
       hpa.genie
       z.genie
       subsets.genie
       basis.genie(x)
       hpa.fun.genie(x)
       hpa.genie.start
       hpa.genie.optimal

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

       x: A 4-element vector (for 'basis.genie()'); a  19-element
          vector (for 'hpa.fun.genie()')

_F_o_r_m_a_t:

     The genie example is a case with three levels.

     The 'D1.genie' matrix is 36 rows of code run points, corresponding
     to the observations of the level 1 code.  It has four columns, one
     per parameter.

     'hpa.genie' is a hyperparameter object.  

     'subsets.genie' is a list of three elements.  Element i
     corresponds to the rows of 'D1.genie' at which level i has been
     observed.

     'z.genie' is a three element list.  Each element is a vector;
     element i corresponds to observations of level i.  The lengths
     will match those of 'subsets.genie'.

     Function 'basis.genie()' is a suitable basis function.

     Function 'hpa.fun.genie()' creates a hyperparameter object in a
     form suitable for passing to the other functions in the library.

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

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

     Robin K. S. Hankin

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

     M. C. Kennedy and A. O'Hagan 2000. "Predicting the output from a
     complex computer code when fast approximations are available"
     Biometrika, 87(1): pp1-13

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

     data(genie)
     z.genie

     jj <- list(trace=100,maxit=10)

     hpa.genie.level1 <- opt.1(D=D1.genie, z=z.genie, basis=basis.genie, subsets=subsets.genie, hpa.start=hpa.genie.start,control=jj)
      
     hpa.genie.level2 <- opt.gt.1(level=2, D=D1.genie, z=z.genie, basis=basis.genie, subsets=subsets.genie, hpa.start=hpa.genie.level1,control=jj)

     hpa.genie.level3 <- opt.gt.1(level=3, D=D1.genie, z=z.genie, basis=basis.genie, subsets=subsets.genie, hpa.start=hpa.genie.level2,control=jj) 

