emulator-package          package:emulator          R Documentation

_E_m_u_l_a_t_i_o_n _o_f _c_o_m_p_u_t_e_r _c_o_d_e _o_u_t_p_u_t

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

     This package allows one to estimate the output of a computer
     program, as a function of the input parameters, without actually
     running it. The computer program is assumed to be a Gaussian
     process, whose parameters are estimated using Bayesian techniqes
     that give a PDF of expected program output.  This PDF is
     conditional on a "training set" of runs, each consisting of a
     point in parameter space and the model output at that point.  The
     emphasis is on complex codes that take weeks or months to run, and
     that have a large number of undetermined input parameters; many
     climate prediction models fall into this class. The emulator
     essentially determines Bayesian a-postiori estimates of the PDF of
     the output of a model, conditioned on results from previous runs
     and a user-specified prior linear model.  A working example is
     given in the help page for function 'interpolant()', which should
     be the users's first point of reference.

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


       Package:  emulator
       Type:     Package
       Version:  1.0
       Date:     2007-05-02
       License:  What license is it under?

     The primary function of the package is 'interpolant()'.

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

     Robin K. S. Hankin

     Maintainer: <r.hankin@noc.soton.ac.uk>

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

        *  J. Oakley 1999. _Bayesian uncertainty analysis for complex
           computer codes_, PhD thesis, University of Sheffield.

        *  J. Oakley and A. O'Hagan, 2002. _Bayesian Inference for the
           Uncertainty Distribution of Computer Model Outputs_,
           Biometrika 89(4), pp769-784

        *  R. K. S. Hankin 2005. _Introducing BACCO, an R bundle for
           Bayesian analysis of computer code output_, Journal of
           Statistical Software, 14(16)

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

     # The following example takes a toy dataframe (toy), which represents an
     #  experimental design.  Variable d contains observations at points in a
     #  six dimensional parameter space specified by the rows of toy.
     #  Function interpolant() is then called to estimate what the
     #  observation would be at a point that has no direct observation.

     data(toy)
     d <- c(11.05, 7.48, 12.94, 14.91, 11.34, 5.0, 11.83, 11.761, 11.62, 6.70)
     fish <- rep(1,6)
     x <- rep(0.5, 6)
     interpolant(x, d, toy, scales=fish,give=TRUE)

