W2                package:calibrator                R Documentation

_v_a_r_i_a_n_c_e _m_a_t_r_i_x _f_o_r _b_e_t_a_2

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

     Variance matrix for beta2 as per page 4 of the supplement

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

     W2(D2, H2, V, det=FALSE)

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

      D2: matrix of observation points

      H2: regression function

       V: Overall covariance matrix

     det: Boolean, with default 'FALSE' meaning to return the matrix,
          and 'TRUE' meaning to return its determinant only

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

     If only the determinant is required, setting argument 'det' to
     'TRUE' is faster than using 'det(W2(...,det=FALSE))', as the
     former avoids an unnecessary use of 'solve()'

_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:

     'V.fun'

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

     data(toys)
     W2(D2=D2.toy, H2=H2.toy, V=V.toy) 

