toyapps             package:approximator             R Documentation

_T_o_y _d_a_t_a_s_e_t_s _f_o_r _a_p_p_r_o_x_i_m_a_t_o_r _p_a_c_k_a_g_e

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

     Toy datasets that illustrate the package.

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

       data(toys)
       D1.toy
       hpa.toy
       z.toy
       subsets.toy

_F_o_r_m_a_t:

     The toy example is a case with four levels.

     The 'D1.toy' matrix is 20 rows of code run points, corresponding
     to the observations of the level 1 code.  It has three columns,
     one per parameter.

     'hpa.toy' is a hyperparameter object.  It is a list of three
     elements: 'sigmas', 'B', and 'rhos'.

     'subsets.toy' is a list of four elements.  Element i corresponds
     to the rows of 'D1.toy' at which level i has been observed.

     'z.toy' is a four element list.  Each element is a vector; element
     i corresponds to obsevations of level i.  The lengths will match
     those of 'subsets.toy'.

     *Brief description of toy functions fully documented under their
     own manpage*

     Function 'generate.toy.observations()' creates new toy datasets
     with any number of observations and code runs.

     Function 'basis.toy()' is an example of a basis function

     Function 'hpa.fun.toy()' creates a hyperparameter object such as
     'phi.toy' in a form suitable for passing to the other functions in
     the library.

     *See the helpfiles listed in the "see also" section below*

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

     All toy datasets are documented here.  There are also several toy
     functions that are needed for a toy problem; these are documented
     separately (they are too diverse to document fully in a single
     manpage).  Nevertheless a terse summary  for each toy function is
     provided on this page.  All toy functions in the package are
     listed under "See Also".

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

     Robin K. S. Hankin ('subsets.fun()'); Peter Dalgaard (via R-help)

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

     M. C. Kennedy and A. O'Hagan 2000. "Predicting the output from a
     complex computer code when fast approximations are available"
     Biometrika, 87(1): pp1-13

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

     data(toyapps)

