tee                package:calibrator                R Documentation

_A_u_x_i_l_i_a_r_y _f_u_n_c_t_i_o_n_s _f_o_r _e_q_u_a_t_i_o_n _9 _o_f _t_h_e _s_u_p_p_l_e_m_e_n_t

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

     Returns a vector whose elements are the "distances" from a point
     to the observations and code run points ('tee()'); and basis
     functions for use in 'Ez.eqn9.supp()'

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

     tee(x, theta, D1, D2, phi)
     h.fun(x, theta, H1, H2, phi)

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

       x: Point from which distances are calculated

   theta: Value of parameters

   D1,D2: Design matrices of code run points and field observation
          points respectively ('tee()')

   H1,H2: Basis functions for eta and model inadequacy term
          respectively ('h.fun()')

     phi: Hyperparameters

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

     Equation 9 of the supplement is identical to equation 10 of
     KOH2001.

     Function 'h.fun()' returns the first of the subsidiary equations
     in equation 9 of the supplement and function 'tee()' returns the
     second (NB: do not confuse this with functions 't1bar()' and
     't2bar()' which are internal to 'EK.eqn10.supp()')

_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:

     'Ez.eqn9.supp'

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

     data(toys)
     tee(x=x.toy, theta=theta.toy, D1=D1.toy, D2=D2.toy, phi=phi.toy)

     # Now some vectorized examples:
     jj <- rbind(x.toy , x.toy , x.toy+0.01,x.toy+1,x.toy*10)

     tee(x=jj, theta=theta.toy, D1=D1.toy, D2=D2.toy, phi=phi.toy)
     h.fun(x=jj, theta=theta.toy, H1=H1.toy, H2=H2.toy, phi=phi.toy)

