h1.toy              package:calibrator              R Documentation

_B_a_s_i_s _f_u_n_c_t_i_o_n_s

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

     Basis functions for D1 and D2 respectively.

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

     h1.toy(x)
     h2.toy(x)

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

       x: Vector of lat/long or lat/long and theta

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

     Note that 'h1()' operates on a vector: for dataframes, use
     'H1.toy()' which is a wrapper for 'apply(D1, 1, h1)'.

     *NB* If the definition of 'h1.toy()' or 'h2.toy()' is changed,
     then function 'hbar.toy()' must be changed to match. This cannot
     be done automatically, as the form of 'hbar.toy()' depends on the
     distribution of 'X'.  The shibboleth is whether 'E_X()' commutes
     with 'h_1()'; it does in this case but does not in general (for
     example, consider h(x,theta)=c(1,x,x^2) and X~N(m,V).  Then
     E_X(h(x,theta)) will be (1,m,m^2+V,theta); note the V).

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

     Returns basis functions of a vector; in the toy case, just prepend
     a '1'.

_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 2001. "Bayesian calibration of
     computer models".  Journal of the Royal Statistical Society B,
     63(3) pp425-464

     M. C. Kennedy and A. O'Hagan 2001.  "Supplementary details on
     Bayesian calibration of computer models", Internal report,
     University of Sheffield.  Available at <URL:
     http://www.shef.ac.uk/~st1ao/ps/calsup.ps>

     R. K. S. Hankin 2005. "Introducing BACCO, an R bundle for Bayesian
     analysis of computer code output", Journal of Statistical
     Software, 14(16)

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

     'H1.toy'

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

     data(toys)
      h1.toy(D1.toy[1,])

