W1                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_1_h_a_t

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

     returns the variance-covariance matrix for the estimate of
     beta1hat

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

     W1(D1, H1, det=FALSE, phi)

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

      D1: matrix of code points

      H1: Basis function generator

     phi: Hyperparameters

     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(W1(...,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:

     'beta1hat.fun'

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

     data(toys)
     W1(D1=D1.toy, H1=H1.toy,  phi=phi.toy)

