Package 'approximator' reference manual

Title:	Bayesian Prediction of Complex Computer Codes
Description:	Performs Bayesian prediction of complex computer codes when fast approximations are available. It uses a hierarchical version of the Gaussian process, originally proposed by Kennedy and O'Hagan (2000), Biometrika 87(1):1.
Authors:	Robin K. S. Hankin
Maintainer:	Robin K. S. Hankin <[email protected]>
License:	GPL-2
Version:	1.2-8
Built:	2025-01-15 02:37:24 UTC
Source:	https://github.com/robinhankin/approximator

Bayesian approximation of computer models when fast approximations are available

Description

Implements the ideas of Kennedy and O'Hagan 2000 (see references).

Details

Package:	approximator
Type:	Package
Version:	1.0
Date:	2006-01-10
License:	GPL

This package implements the Bayesian approximation techniques discussed in Kennedy and O'Hagan 2000.

In its simplest form, it takes input from a “slow” but accurate code and a “fast” but inaccurate code, each run at different points in parameter space. The approximator package then uses both sets of model runs to infer what the slow code would produce at a given, untried point in parameter space.

The package includes functionality to work with a hierarchy of codes with increasing accuracy.

Author(s)

Robin K. S. Hankin

Maintainer: <[email protected]>

References

R. K. S. Hankin 2005. “Introducing BACCO, an R bundle for Bayesian analysis of computer code output”, Journal of Statistical Software, 14(16)

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

Examples

data(toyapps)
mdash.fun(x=1:3, D1=D1.toy, subsets=subsets.toy, hpa=hpa.toy, z=z.toy, basis=basis.toy)
data(toyapps)
mdash.fun(x=1:3, D1=D1.toy, subsets=subsets.toy, hpa=hpa.toy, z=z.toy, basis=basis.toy)

Matrix of correlations between two sets of points

Description

Returns the matrix of correlations of code output at each level evaluated at points on the design matrix.

Usage

Afun(level, Di, Dj, hpa)
Afun(level, Di, Dj, hpa)

Arguments

`level`	The level. This enters via the correlation scales
`Di`	First set of points
`Dj`	Second set of points
`hpa`	Hyperparameter object

Details

This is essentially a convenient wrapper for function corr.matrix. It is not really intended for the end user.

Author(s)

Robin K. S. Hankin

References

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

Examples

data(toyapps)
D2 <-  D1.toy[subsets.toy[[2]],]
D3 <-  D1.toy[subsets.toy[[3]],]

Afun(1,D2,D3,hpa.toy)
Afun(2,D2,D3,hpa.toy)

data(toyapps)
D2 <-  D1.toy[subsets.toy[[2]],]
D3 <-  D1.toy[subsets.toy[[3]],]

Afun(1,D2,D3,hpa.toy)
Afun(2,D2,D3,hpa.toy)

Converts a level one design matrix and a subsets object into a list of design matrices, one for each level

Description

Given a level one design matrix, and a subsets object, convert into a list of design matrices, each one of which is the design matrix for its own level

Usage

as.sublist(D1, subsets)
as.sublist(D1, subsets)

Arguments

`D1`	Design matrix for level one code
`subsets`	subsets object

Author(s)

Robin K. S. Hankin

References

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

Examples

data(toyapps)
as.sublist(D1=D1.toy , subsets=subsets.toy)
data(toyapps)
as.sublist(D1=D1.toy , subsets=subsets.toy)

Toy basis functions

Description

A working example of a basis function

Usage

basis.toy(x)
basis.toy(x)

Arguments

`x`	Point in parameter space

Author(s)

Robin K. S. Hankin

References

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

Examples

data(toyapps)
basis.toy(D1.toy)
data(toyapps)
basis.toy(D1.toy)

Estimate for beta

Description

Returns the estimator for beta; equation 5. Function betahat.app() returns the estimate in terms of fundamental variables; betahat.app.H() requires the H matrix.

Usage

betahat.app.H(H, V = NULL, Vinv = NULL, z)
betahat.app(D1, subsets, basis, hpa, z, use.Vinv=TRUE)
betahat.app.H(H, V = NULL, Vinv = NULL, z)
betahat.app(D1, subsets, basis, hpa, z, use.Vinv=TRUE)

Arguments

`H`	In `betahat.app.H()`, the H matrix, eg that returned by `H.fun()`
`V`	Variance matrix
`Vinv`	Inverse of variance matrix. If not supplied, it is calculated
`use.Vinv`	In function `betahat.app()`, a Boolean argument with default `TRUE` meaning to calculate the inverse of the `V` matrix; and `FALSE` meaning to use a method which does not involve calculating the inverse of `V`. The default method seems to be faster; YMMV
`z`	vector of observations
`D1`	Design matrix for level 1 code
`subsets`	Subsets object
`basis`	Basis function
`hpa`	Hyperparameter object

Author(s)

Robin K. S. Hankin

References

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

Examples

data(toyapps)

 betahat.app(D1=D1.toy, subsets=subsets.toy, basis=basis.toy, hpa=hpa.toy, z=z.toy, use.Vinv=TRUE)

 H <- H.fun.app(D1=D1.toy, subsets=subsets.toy, basis=basis.toy,hpa=hpa.toy)
 V <- V.fun.app(D1=D1.toy, subsets=subsets.toy, hpa=hpa.toy)
 betahat.app.H(H=H,V=V,z=z.toy)

data(toyapps)

 betahat.app(D1=D1.toy, subsets=subsets.toy, basis=basis.toy, hpa=hpa.toy, z=z.toy, use.Vinv=TRUE)

 H <- H.fun.app(D1=D1.toy, subsets=subsets.toy, basis=basis.toy,hpa=hpa.toy)
 V <- V.fun.app(D1=D1.toy, subsets=subsets.toy, hpa=hpa.toy)
 betahat.app.H(H=H,V=V,z=z.toy)

Correlations between points in parameter space

Description

Correlation matrices between (sets of) points in parameter space, both prior (c_fun()) and posterior (cdash.fun()).

Usage

c_fun(x, xdash=x, subsets, hpa)
cdash.fun(x, xdash=x, V=NULL, Vinv=NULL, D1, subsets, basis, hpa, method=2)
c_fun(x, xdash=x, subsets, hpa)
cdash.fun(x, xdash=x, V=NULL, Vinv=NULL, D1, subsets, basis, hpa, method=2)

Arguments

`x`, `xdash`	Points in parameter space; or, if a matrix, interpret the rows as points in parameter space. Note that the default value of `xdash` (viz `x`) will return the variance-covariance matrix of a set of points
`D1`	Design matrix
`subsets`	Subset object
`hpa`	hyperparameter object
`basis`	Basis function
`V`, `Vinv`	In function `cdash.fun()`, the data covariance matrix and its inverse. If `NULL`, the matrix will be calculated from scratch. Supplying a precalculated value for `V`, and especially `Vinv`, makes for very much faster execution (edepending on `method`)
`method`	Integer specifying which of several algebraically identical methods to use. See the source code for details, but default option 2 seems to be the best. Bear in mind that option 3 does not require inversion of a matrix, but is not faster in practice

Value

Returns a matrix of covariances

Note

Do not confuse function c_fun(), which computes $c(x,x')$ defined just below equation 7 on page 4 with $c_t(x,x')$ defined in equation 3 on page 3.

Consider the example given for two levels on page 4 just after equation 7: $c(x,x')=c_2(x,x')+\rho_1^2c_1(x,x')$ is a kind of prior covariance matrix. Matrix $c'(x,x')$ is a posterior covariance matrix, conditional on the code observations.

Function Afun() evaluates $c_t(x,x')$ in a nice vectorized way.

Equation 7 of KOH2000 contains a typo.

Author(s)

Robin K. S. Hankin

References

KOH2000

Examples

data(toyapps)

x <- latin.hypercube(4,3)
rownames(x) <- c("ash" , "elm" , "oak", "pine")
xdash <- latin.hypercube(7,3)
rownames(xdash) <- c("cod","bream","skate","sole","eel","crab","squid")

cdash.fun(x=x,xdash=xdash, D1=D1.toy, basis=basis.toy,subsets=subsets.toy, hpa=hpa.toy)

# Now add a point whose top-level value is known:
x <- rbind(x,D1.toy[subsets.toy[[4]][1],])

cdash.fun(x=x,xdash=xdash, D1=D1.toy, basis=basis.toy,subsets=subsets.toy, hpa=hpa.toy)
# Observe how the bottom row is zero (up to rounding error)
data(toyapps)

x <- latin.hypercube(4,3)
rownames(x) <- c("ash" , "elm" , "oak", "pine")
xdash <- latin.hypercube(7,3)
rownames(xdash) <- c("cod","bream","skate","sole","eel","crab","squid")

cdash.fun(x=x,xdash=xdash, D1=D1.toy, basis=basis.toy,subsets=subsets.toy, hpa=hpa.toy)

# Now add a point whose top-level value is known:
x <- rbind(x,D1.toy[subsets.toy[[4]][1],])

cdash.fun(x=x,xdash=xdash, D1=D1.toy, basis=basis.toy,subsets=subsets.toy, hpa=hpa.toy)
# Observe how the bottom row is zero (up to rounding error)

Er, generate toy observations

Description

Generates toy observations on four levels using either internal (unknown) parameters and hyperparameters, or user-supplied versions.

Usage

generate.toy.observations(D1, subsets, basis.fun, hpa = NULL, betas = NULL,
export.truth = FALSE)
generate.toy.observations(D1, subsets, basis.fun, hpa = NULL, betas = NULL,
export.truth = FALSE)

Arguments

`D1`	Design matrix for level 1 code
`subsets`	Subset object
`basis.fun`	Basis function
`hpa`	Hyperparameter object. If `NULL`, use the internal (true but unknown) hyperparameter object
`betas`	Regression coefficients. If `NULL`, use the internal (true but unknown) regression coefficients
`export.truth`	Boolean, with default `FALSE` meaning to return synthetic observations and `TRUE` meaning to return the actual hyperparameters and coefficients.

Author(s)

Robin K. S. Hankin

References

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

Examples

data(toyapps)
generate.toy.observations(D1=D1.toy, subsets=subsets.toy, basis.fun=basis.toy)
data(toyapps)
generate.toy.observations(D1=D1.toy, subsets=subsets.toy, basis.fun=basis.toy)

Genie datasets for approximator package

Description

Genie datasets that illustrate the package.

Format

The genie example is a case with three levels.

The D1.genie matrix is 36 rows of code run points, corresponding to the observations of the level 1 code. It has four columns, one per parameter.

hpa.genie is a hyperparameter object.

subsets.genie is a list of three elements. Element $i$ corresponds to the rows of D1.genie at which level $i$ has been observed.

z.genie is a three element list. Each element is a vector; element $i$ corresponds to observations of level $i$ . The lengths will match those of subsets.genie.

Function basis.genie() is a suitable basis function.

Function hpa.fun.genie() creates a hyperparameter object in a form suitable for passing to the other functions in the library.

Author(s)

Robin K. S. Hankin

References

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

Examples

data(genie)
z.genie


jj <- list(trace=100,maxit=10)

hpa.genie.level1 <- opt.1(D=D1.genie, z=z.genie,
                          basis=basis.genie, subsets=subsets.genie,
                          hpa.start=hpa.genie.start,control=jj)
 
hpa.genie.level2 <- opt.gt.1(level=2, D=D1.genie, z=z.genie,
                          basis=basis.genie, subsets=subsets.genie,
                          hpa.start=hpa.genie.level1,control=jj)

hpa.genie.level3 <- opt.gt.1(level=3, D=D1.genie, z=z.genie,
                          basis=basis.genie, subsets=subsets.genie,
                          hpa.start=hpa.genie.level2,control=jj) 


data(genie)
z.genie


jj <- list(trace=100,maxit=10)

hpa.genie.level1 <- opt.1(D=D1.genie, z=z.genie,
                          basis=basis.genie, subsets=subsets.genie,
                          hpa.start=hpa.genie.start,control=jj)
 
hpa.genie.level2 <- opt.gt.1(level=2, D=D1.genie, z=z.genie,
                          basis=basis.genie, subsets=subsets.genie,
                          hpa.start=hpa.genie.level1,control=jj)

hpa.genie.level3 <- opt.gt.1(level=3, D=D1.genie, z=z.genie,
                          basis=basis.genie, subsets=subsets.genie,
                          hpa.start=hpa.genie.level2,control=jj)

The H matrix

Description

Returns the matrix of bases H. The “app” of the function name means “approximator”, to distinguish it from function H.fun() of the calibrator package.

Usage

H.fun.app(D1, subsets, basis, hpa)
H.fun.app(D1, subsets, basis, hpa)

Arguments

`D1`	Design matrix for level 1 code
`subsets`	Subsets object
`basis`	Basis function
`hpa`	Hyperparameter object

Author(s)

Robin K. S. Hankin

References

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

Examples

data(toyapps)
H.fun.app(D1.toy , subsets=subsets.toy , basis=basis.toy , hpa=hpa.toy)
data(toyapps)
H.fun.app(D1.toy , subsets=subsets.toy , basis=basis.toy , hpa=hpa.toy)

Hdash

Description

Returns the thing at the top of page 6

Usage

hdash.fun(x, hpa, basis)
hdash.fun(x, hpa, basis)

Arguments

`x`	Point in question
`hpa`	Hyperparameter object
`basis`	Basis functions

Author(s)

Robin K. S. Hankin

References

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

Examples

data(toyapps)
hdash.fun(x=1:3 , hpa=hpa.toy,basis=basis.toy)

uu <- rbind(1:3,1,1:3,3:1)
rownames(uu) <- paste("uu",1:4,sep="_")
hdash.fun(x=uu, hpa=hpa.toy,basis=basis.toy)

data(toyapps)
hdash.fun(x=1:3 , hpa=hpa.toy,basis=basis.toy)

uu <- rbind(1:3,1,1:3,3:1)
rownames(uu) <- paste("uu",1:4,sep="_")
hdash.fun(x=uu, hpa=hpa.toy,basis=basis.toy)

Toy example of a hyperparameter object creation function

Description

Creates a hyperparameter object from a vector of length 19. Intended as a toy example to be modified for real-world cases.

Usage

hpa.fun.toy(x)
hpa.fun.toy(x)

Arguments

`x`	Vector of length 19 that specifies the correlation scales

Details

Elements 1-4 of x specify the sigmas for each of the four levels in the toy example. Elements 5-7 specify the correlation scales for level 1, elements 8-10 the scales for level 2, and so on.

Internal function pdm.maker() shows how the B matrix is obtained from the various elements of input argument x. Note how, in this simple example, the B matrices are diagonal, but generalizing to non-diagonal matrices should be straightforward (if you can guarantee that they remain positive definite).

Value

`sigmas`	The four sigmas corresponding to the four levels
`B`	The four B matrices corresponding to the four levels
`rhos`	The three (sic) matrices corresponding to levels 1-3

Author(s)

Robin K. S. Hankin

References

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

Examples

hpa.fun.toy(1:19)
hpa.fun.toy(1:19)

Checks observational data for consistency with a subsets object

Description

Checks observational data for consistency with a subsets object: the length of the vectors should match

Usage

is.consistent(subsets, z)
is.consistent(subsets, z)

Arguments

`subsets`	A subsets object
`z`	Data

Value

Returns TRUE or FALSE depending on whether z is consistent with subsets.

Author(s)

Robin K. S. Hankin

References

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

Examples

data(toyapps)
stopifnot(is.consistent(subsets.toy,z.toy))

z.toy[[4]] <- 1:6
is.consistent(subsets.toy,z.toy)
data(toyapps)
stopifnot(is.consistent(subsets.toy,z.toy))

z.toy[[4]] <- 1:6
is.consistent(subsets.toy,z.toy)

Mean of Gaussian process

Description

Returns the mean of the Gaussian process conditional on the observations and the hyperparameters

Usage

mdash.fun(x, D1, subsets, hpa, Vinv = NULL, use.Vinv = TRUE, z, basis)
mdash.fun(x, D1, subsets, hpa, Vinv = NULL, use.Vinv = TRUE, z, basis)

Arguments

`x`	Point at which mean is desired
`D1`	Code design matrix for level 1 code
`subsets`	subsets object
`hpa`	Hyperparameter object
`Vinv`	Inverse of the variance matrix; if `NULL`, the function will calculate it
`use.Vinv`	Boolean, with default `TRUE` meaning to use the inverse of V and `FALSE` meaning to use a method that does not involve inverting V
`z`	observations
`basis`	Basis functions

Author(s)

Robin K. S. Hankin

References

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

Examples

data(toyapps)
mdash.fun(x=1:3,D1=D1.toy,subsets=subsets.toy,hpa=hpa.toy,z=z.toy,basis=basis.toy)

uu <- rbind(1:3,1,3:1,1:3)
rownames(uu) <- c("first","second","third","fourth")

mdash.fun(x=uu,D1=D1.toy,subsets=subsets.toy,hpa=hpa.toy,z=z.toy,basis=basis.toy)

data(toyapps)
mdash.fun(x=1:3,D1=D1.toy,subsets=subsets.toy,hpa=hpa.toy,z=z.toy,basis=basis.toy)

uu <- rbind(1:3,1,3:1,1:3)
rownames(uu) <- c("first","second","third","fourth")

mdash.fun(x=uu,D1=D1.toy,subsets=subsets.toy,hpa=hpa.toy,z=z.toy,basis=basis.toy)

Optimization of posterior likelihood of hyperparameters

Description

Returns the likelihood of a set of hyperparameters given the data. Functions opt1() and opt.gt.1() find hyperparameters that maximize the relevant likelihood for level 1 and higher levels respectively. Function object() returns the expression given by equation 9 in KOH2000, which is minimized opt1() and opt.gt.1().

Usage

object(level, D, z, basis, subsets, hpa)
opt.1(D, z, basis, subsets, hpa.start, give.answers=FALSE, ...)
opt.gt.1(level, D, z, basis, subsets, hpa.start, give.answers=FALSE, ...)
object(level, D, z, basis, subsets, hpa)
opt.1(D, z, basis, subsets, hpa.start, give.answers=FALSE, ...)
opt.gt.1(level, D, z, basis, subsets, hpa.start, give.answers=FALSE, ...)

Arguments

`level`	level
`D`	Design matrix for top-level code
`z`	Data
`basis`	Basis function
`subsets`	subsets object
`hpa`	hyperparameter object
`hpa.start`	Starting value for hyperparameter object
`give.answers`	Boolean, with default `FALSE` meaning to return just the point estimate, and `TRUE` meaning to return extra information from the call to `optim()`
`...`	Extra arguments passed to `optim()`. A common one would be `control=list(trace=100)`

Details

This function is the object function used in toy optimizers optimal.hpa().

Author(s)

Robin K. S. Hankin

References

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

Examples

data(toyapps)
object(level=4, D=D1.toy , z=z.toy,basis=basis.toy,
   subsets=subsets.toy, hpa=hpa.fun.toy(1:19))
object(level=4, D=D1.toy , z=z.toy,basis=basis.toy,
   subsets=subsets.toy, hpa=hpa.fun.toy(3+(1:19)))


# Now a little example of finding optimal hyperpameters in the toy case
# (a bigger example is given on the genie help page)
jj <- list(trace=100,maxit=10)

hpa.toy.level1 <- opt.1(D=D1.toy, z=z.toy, basis=basis.toy,
          subsets=subsets.toy, hpa.start=hpa.toy,control=jj)

hpa.toy.level2 <- opt.gt.1(level=2, D=D1.toy, z=z.toy,
           basis=basis.toy, subsets=subsets.toy,
           hpa.start=hpa.toy.level1, control=jj)

hpa.toy.level3 <- opt.gt.1(level=3, D=D1.toy, z=z.toy,
           basis=basis.toy, subsets=subsets.toy,
           hpa.start=hpa.toy.level2, control=jj) 

hpa.toy.level4 <- opt.gt.1(level=4, D=D1.toy, z=z.toy,
           basis=basis.toy, subsets=subsets.toy,
           hpa.start=hpa.toy.level3, control=jj)

data(toyapps)
object(level=4, D=D1.toy , z=z.toy,basis=basis.toy,
   subsets=subsets.toy, hpa=hpa.fun.toy(1:19))
object(level=4, D=D1.toy , z=z.toy,basis=basis.toy,
   subsets=subsets.toy, hpa=hpa.fun.toy(3+(1:19)))


# Now a little example of finding optimal hyperpameters in the toy case
# (a bigger example is given on the genie help page)
jj <- list(trace=100,maxit=10)

hpa.toy.level1 <- opt.1(D=D1.toy, z=z.toy, basis=basis.toy,
          subsets=subsets.toy, hpa.start=hpa.toy,control=jj)

hpa.toy.level2 <- opt.gt.1(level=2, D=D1.toy, z=z.toy,
           basis=basis.toy, subsets=subsets.toy,
           hpa.start=hpa.toy.level1, control=jj)

hpa.toy.level3 <- opt.gt.1(level=3, D=D1.toy, z=z.toy,
           basis=basis.toy, subsets=subsets.toy,
           hpa.start=hpa.toy.level2, control=jj) 

hpa.toy.level4 <- opt.gt.1(level=4, D=D1.toy, z=z.toy,
           basis=basis.toy, subsets=subsets.toy,
           hpa.start=hpa.toy.level3, control=jj)

Kennedy's Pi notation

Description

Evaluates Kennedy's $\prod$ product

Usage

Pi(hpa, i, j)
Pi(hpa, i, j)

Arguments

`hpa`	Hyperparameter object
`i`	subscript
`j`	superscript

Details

This function evaluates Kennedy's $\prod$ product, but with the additional feature that $\prod_i^j=0$ if $i>j+1$ . This seems to work in practice.

Author(s)

Robin K. S. Hankin

References

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

Examples

data(toyapps)
Pi(hpa.toy,1,2)
Pi(hpa.toy,2,2)
Pi(hpa.toy,3,2)
Pi(hpa.toy,4,2)
data(toyapps)
Pi(hpa.toy,1,2)
Pi(hpa.toy,2,2)
Pi(hpa.toy,3,2)
Pi(hpa.toy,4,2)

Create a simple subset object

Description

Given an integer vector whose $i^\mathrm{th}$ element is the number of runs at level $i$ , return a subset object in echelon form.

Usage

subset_maker(x)
subset_maker(x)

Arguments

`x`	A vector of integers

Details

In this context, x being in “echelon form” means that

x is consistent in the sense of passing is.consistent()
For each $i$ , x[[i]] = 1:n for some $n$ .

Value

A list object suitable for use as a subset object

Author(s)

Robin K. S. Hankin

Examples

subset_maker(c(10,4,3))


is.nested(subset_maker(c(4,9,6))) #should be FALSE
is.nested(subset_maker(c(9,6,4))) #should be TRUE

subset_maker(c(10,4,3))


is.nested(subset_maker(c(4,9,6))) #should be FALSE
is.nested(subset_maker(c(9,6,4))) #should be TRUE

Generate and test subsets

Description

Create a list of subsets (subsets.fun()); or, given a list of subsets, test for correct inclusion (is.nested()), or strict inclusion (is.strict()).

Usage

is.nested(subsets)
is.strict(subsets)
subsets.fun(n, levels = 4, prob = 0.7)
is.nested(subsets)
is.strict(subsets)
subsets.fun(n, levels = 4, prob = 0.7)

Arguments

`subsets`	In `is.nested()`, a list of subsets to be tested
`n`	Number of observations in the lowest level (ie level 1, the fastest code)
`levels`	Number of levels
`prob`	Probability of choosing an observation at level $n+1$ given that there is one at the same place at level $n$

Author(s)

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

References

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

Examples

is.nested(subsets.fun(20))  # Should be TRUE

data(toyapps)
stopifnot(is.nested(subsets.toy))
is.nested(subsets.fun(20))  # Should be TRUE

data(toyapps)
stopifnot(is.nested(subsets.toy))

Returns generalized distances

Description

Returns generalized distances from a point to the design matrix as per equation 10

Usage

tee.fun(x, D1, subsets, hpa)
tee.fun(x, D1, subsets, hpa)

Arguments

`x`	Point in parameter space
`D1`	Design matrix for level 1 code
`subsets`	subsets object
`hpa`	Hyperparameter object

Details

See equation 10

Author(s)

Robin K. S. Hankin

References

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

Examples


data(toyapps)
tee.fun(x=1:3, D1=D1.toy, subsets=subsets.toy, hpa=hpa.toy)
data(toyapps)
tee.fun(x=1:3, D1=D1.toy, subsets=subsets.toy, hpa=hpa.toy)

Toy datasets for approximator package

Description

Toy datasets that illustrate the package.

Usage

  data(toyapps)
data(toyapps)

Format

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.

betas.toy is a matrix of coefficients.

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

Details

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”.

Author(s)

Robin K. S. Hankin

References

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

Examples

data(toyapps)

is.consistent(subsets.toy , z.toy)

generate.toy.observations(D1.toy, subsets.toy, basis.toy, hpa.toy, betas.toy)

data(toyapps)

is.consistent(subsets.toy , z.toy)

generate.toy.observations(D1.toy, subsets.toy, basis.toy, hpa.toy, betas.toy)

Variance matrix

Description

Given a design matrix, a subsets object and a hyperparameter object, return the variance matrix. The “app” of the function name means “approximator”, to distinguish it from function V.fun() of the calibrator package.

Usage

V.fun.app(D1, subsets, hpa)
V.fun.app(D1, subsets, hpa)

Arguments

`D1`	Design matrix for level 1 code
`subsets`	Subsets object
`hpa`	Hyperparameter object

Author(s)

Robin K. S. Hankin

References

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

Examples

data(toyapps)
V.fun.app(D1.toy,subsets.toy,hpa.toy)
data(toyapps)
V.fun.app(D1.toy,subsets.toy,hpa.toy)

Package 'approximator'

Help Index

Bayesian approximation of computer models when fast approximations are available

Description

Details

Author(s)

References

Examples

Matrix of correlations between two sets of points

Description

Usage

Arguments

Details

Author(s)

References

See Also

Examples

Converts a level one design matrix and a subsets object into a list of design matrices, one for each level

Description

Usage

Arguments

Author(s)

References

Examples

Toy basis functions

Description

Usage

Arguments

Author(s)

References

Examples

Estimate for beta

Description

Usage

Arguments

Author(s)

References

Examples

Correlations between points in parameter space

Description

Usage

Arguments

Value

Note

Author(s)

References

See Also

Examples

Er, generate toy observations

Description

Usage

Arguments

Author(s)

References

Examples

Genie datasets for approximator package

Description

Format

Author(s)

References

Examples

The H matrix

Description

Usage

Arguments

Author(s)

References

Examples

Hdash

Description

Usage

Arguments

Author(s)

References

Examples

Toy example of a hyperparameter object creation function

Description

Usage

Arguments

Details