toys {calibrator} | R Documentation |
Toy datasets that illustrate the package.
data(toys)
D1.toy
D2.toy
d.toy
phi.toy
theta.toy
V.toy
X.dist.toy
The D1.toy
matrix is 8 rows of code run points, with five
columns. The first two columns are the lat and long and the next
three are parameter values.
The D2.toy
matrix is five rows of observations on two
variables, x
and y
which are styled
“latitude and longitude”.
d.toy
is the “data” vector consisting of length 13: elements
1-8 are code runs and elements 9-13 are observations.
theta.toy
is a vector of length three that is a working example
of \theta
. The parameters are designed to work with
computer.model()
.
t.vec.toy
is a matrix of eight rows and three columns. Each
row specifies a value for \theta
. The eight rows
correspond to eight code runs.
x.toy
and x.toy2
are vectors of length two that gives a
sample point at which observations may be made (or the code run).
The gloss of the two elements is latitude and longitude.
x.vec
is a matrix whose rows are reasonable x values but
not those in D2.toy
.
y.toy
is a vector of length eight. Each element corresponds to
the output from a code run at each of the rows of D1.toy
.
z.toy
is a vector of length five. Each element corresponds to
a measurement at each of the rows of D2.toy
.
V.toy
is a five by five variance-covariance matrix for the toy
datasets.
X.dist.toy
is a toy example of a distribution of X
for
use in calibrated uncertainty analysis, section 4.2.
Brief description of toy functions fully documented under their own manpage
Function create.new.toy.datasets()
creates new toy datasets
with any number of observations and code runs.
Function E.theta.toy()
returns expectation of H(D)
with
respect to \theta
; Edash.theta.toy()
returns
expectation with respect to E'
.
Function extractor.toy()
extracts x.star.toy
and t.vec.toy
from D2
; toy example needed because the
extraction differs from case to case.
Function H1.toy()
applies basis functions to rows of D1
and D2
Function phi.fun.toy()
creates a hyperparameter object such as
phi.toy
in a form suitable for passing to the other functions
in the library.
Function phi.change.toy()
modifies the hyperparameter object.
See the helpfiles listed in the “see also” section below
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”.
Robin K. S. Hankin
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 http://www.tonyohagan.co.uk/academic/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)
create.new.toy.datasets
,
E.theta.toy
,
extractor.toy
,
H1.toy
,
phi.fun.toy
,
stage1
data(toys)
D1.toy
extractor.toy(D1.toy)
D2.fun(theta=theta.toy , D2=D2.toy)
D2.fun(theta=theta.toy,D2=D2.toy[1,,drop=FALSE])
library("emulator")
corr.matrix(D1.toy,scales=rep(1,5))
corr.matrix(D1.toy, pos.def.matrix=diag(5))