laGP (version 1.5-9)

fcalib: Objective function for performing large scale computer model calibration via optimization

Description

Defines an objective function for performing blackbox optimization towards solving a modularized calibration of large computer model simulation to field data

Usage

fcalib(u, XU, Z, X, Y, da, d, g, uprior = NULL, methods = rep("alc", 2), 
  M = NULL, bias = TRUE, omp.threads = 1, save.global = FALSE, verb = 1)

Value

Returns a scalar measuring the negative log likelihood or posterior density of the calibration parameter u given the other inputs, for the purpose of optimization over u

Arguments

u

a vector of length ncol(XU) - ncol(X) containing a setting of the calibration parameter

XU

a matrix or data.frame containing the full (large) design matrix of input locations to a computer simulator whose final ncol(XU) - ncol(X) columns contain settings of a calibration or tuning parameter like u

Z

a vector of responses/dependent values with length(Z) = ncol(XU) of computer model outputs at XU

X

a matrix or data.frame containing the full (large) design matrix of input locations

Y

a vector of values with length(Y) = ncol(X) containing the response from field data observations at X. A Y-vector with length(Y) = k*ncol(X), for positive integer k, can be supplied in which case the multiple Y-values will be treated as replicates at the X-values

da

for emulating Z at XU: a prior or initial setting for the (single/isotropic) lengthscale parameter in a Gaussian correlation function; a (default) NULL value triggers a sensible regularization (prior) and initial setting to be generated via darg; a scalar specifies an initial value, causing darg to only generate the prior; otherwise, a list or partial list matching the output of darg can be used to specify a custom prior. In the case of a partial list, the only the missing entries will be generated. Note that a default/generated list specifies MLE/MAP inference for this parameter. When specifying initial values, a vector of length nrow(XX) can be provided, giving a different initial value for each predictive location

d

for the discrepancy between emulations Yhat at X, based on Z at XU, and the oputs Y observed at X. Otherwise, same description as da above

g

for the nugget in the GP model for the discrepancy between emulation Yhat at X, based on Z at XU, and the outputs Y observed at X: a prior or initial setting for the nugget parameter; a NULL value causes a sensible regularization (prior) and initial setting to be generated via garg; a scalar (default g = 1/1000) specifies an initial value, causing garg to only generate the prior; otherwise, a list or partial list matching the output of garg can be used to specify a custom prior. In the case of a partial list, only the missing entries will be generated. Note that a default/generated list specifies no inference for this parameter; i.e., it is fixed at its starting value, which may be appropriate for emulating deterministic computer code output. At this time, estimating a nugget for the computer model emulator is not supported by fcalib

uprior

an optional function taking u arguments which returns a log prior density value for the calibration parameter.

methods

a sequence of local search methods to be deployed when emulating Z at XU via aGP; see aGP.seq for more details; provide methods = FALSE to use the computer model M directly

M

a computer model “simulation” function taking two matrices as inputs, to be used in lieu of emulation; see aGP.seq for mode details

bias

a scalar logical indicating whether a GP discrepancy or bias term should be estimated via discrep.est, as opposed to only a Gaussian (zero-mean) variance; see discrep.est for more details

omp.threads

a scalar positive integer indicating the number of threads to use for SMP parallel processing; see aGP for more details

save.global

an environment, e.g., .GlobalEnv if each evaluation of fcalib, say as called by a wrapper or optimization routine, should be saved. The variable used in that environment will be fcalib.save. Otherwise save.global = FALSE will skip saving the information

verb

a non-negative integer specifying the verbosity level; verb = 0 is quiet, whereas a larger value causes each evaluation to be printed to the screen

Author

Robert B. Gramacy

Details

Gramacy, et al. (2015) defined an objective function which, when optimized, returns a setting of calibration parameters under a setup akin to the modularized calibration method of Liu, et al., (2009). The fcalib function returns a log density (likelihood or posterior probability) value obtained by performing emulation at a set of inputs X augmented with a value of the calibration parameter, u. The emulator is trained on XU and Z, presumed to be very large relative to the size of the field data set X and Y, necessitating the use of approximate methods like aGP, via aGP.seq. The emulated values, call them Yhat are fed along with X and Y into the discrep.est function, whose likelihood or posterior calculation serves as a measure of merit for the value u.

The fcalib function is deterministic but, as Gramacy, et al. (2015) described, can result is a rugged objective surface for optimizing, meaning that conventional methods, like those in optim are unlikely to work well. They instead recommend using a blackbox derivative-free method, like NOMAD (Le Digabel, 2011). In our example below we use the implementation in the crs package, which provides an R wrapper around the underlying C library.

Note that while fcalib automates a call first to aGP.seq and then to discrep.est, it does not return enough information to complete, say, an out-of-sample prediction exercise like the one demonstrated in the discrep.est documentation. Therefore, after fcalib is used in an optimization to find the best setting of the calibration parameter, u, those functions must then be used in post-processing to complete a prediction exercise. See demo("calib") or vignette("laGP") for more details

References

Gramacy, R. B. (2020) Surrogates: Gaussian Process Modeling, Design and Optimization for the Applied Sciences. Boca Raton, Florida: Chapman Hall/CRC. (See Chapter 8.) https://bobby.gramacy.com/surrogates/

R.B. Gramacy (2016). laGP: Large-Scale Spatial Modeling via Local Approximate Gaussian Processes in R., Journal of Statistical Software, 72(1), 1-46; tools:::Rd_expr_doi("10.18637/jss.v072.i01") or see vignette("laGP")

R.B. Gramacy, D. Bingham, JP. Holloway, M.J. Grosskopf, C.C. Kuranz, E. Rutter, M. Trantham, and P.R. Drake (2015). Calibrating a large computer experiment simulating radiative shock hydrodynamics. Annals of Applied Statistics, 9(3) 1141-1168; preprint on arXiv:1410.3293 https://arxiv.org/abs/1410.3293

F. Liu, M. Bayarri, and J. Berger (2009). Modularization in Bayesian analysis, with emphasis on analysis of computer models. Bayesian Analysis, 4(1) 119-150.

S. Le Digabel (2011). Algorithm 909: NOMAD: Nonlinear Optimization with the MADS algorithm. ACM Transactions on Mathematical Software, 37, 4, 44:1-44:15.

J.S. Racine, Z. and Nie (2012). crs: Categorical regression splines. R package version 0.15-18.

See Also

vignette("laGP"), jmleGP, newGP, aGP.seq, discrep.est, snomadr

Examples

## the example here illustrates how fcalib combines aGP.seq and 
## discrep.est functions, duplicating the example in the discrep.est
## documentation file.  It is comprised of snippets from demo("calib"), 
## which contains code from the Calibration Section of vignette("laGP")

## Here we generate calibration data using a true calibration
## parameter, u, and then evaluate log posterior probabilities; 
## the discrep.est documentation repeats this with by first calling
## aGP.seq and then discrep.est.  The answers should be identical, however
## note that a call first to example("fcalib") and then 
## example("discrep.est") will generate two random data sets, causing
## the results not to match

## begin data-generation code identical to aGP.seq, discrep.est, fcalib
## example sections and demo("calib")

## M: computer model test function used in Goh et al, 2013 (Technometrics)
## an elaboration of one from Bastos and O'Hagan, 2009 (Technometrics) 
M <- function(x,u) 
  {
    x <- as.matrix(x)
    u <- as.matrix(u)
    out <- (1-exp(-1/(2*x[,2]))) 
    out <- out * (1000*u[,1]*x[,1]^3+1900*x[,1]^2+2092*x[,1]+60) 
    out <- out / (100*u[,2]*x[,1]^3+500*x[,1]^2+4*x[,1]+20)  
    return(out)
  }
  
## bias: discrepancy function from Goh et al, 2013 
bias <- function(x) 
  {
    x<-as.matrix(x)   
    out<- 2*(10*x[,1]^2+4*x[,2]^2) / (50*x[,1]*x[,2]+10)
    return(out)
  }

## beta.prior: marginal beta prior for u, 
## defaults to a mode at 1/2 in hypercube
beta.prior <- function(u, a=2, b=2, log=TRUE)
{
  if(length(a) == 1) a <- rep(a, length(u))
  else if(length(a) != length(u)) stop("length(a) must be 1 or length(u)")
  if(length(b) == 1) b <- rep(b, length(u))
  else if(length(b) != length(u)) stop("length(b) must be 1 or length(u)")
  if(log) return(sum(dbeta(u, a, b, log=TRUE)))
  else return(prod(dbeta(u, a, b, log=FALSE)))
}

## tgp for LHS sampling
library(tgp)
rect <- matrix(rep(0:1, 4), ncol=2, byrow=2)

## training inputs
ny <- 50; 
X <- lhs(ny, rect[1:2,])    ## computer model train

## true (but unknown) setting of the calibration parameter
## for the computer model
u <- c(0.2, 0.1)
Zu <- M(X, matrix(u, nrow=1)) 

## field data response, biased and replicated
sd <- 0.5
## Y <- computer output + bias + noise
reps <- 2 ## example from paper uses reps <- 10
Y <- rep(Zu,reps) + rep(bias(X),reps) + rnorm(reps*length(Zu), sd=sd) 
## variations: remove the bias or change 2 to 1 to remove replicates

## computer model design
nz <- 10000
XU <- lhs(nz, rect)
nth <- 1 ## number of threads to use in emulation, demo uses 8

## augment with physical model design points 
## with various u settings
XU2 <- matrix(NA, nrow=10*ny, ncol=4)
for(i in 1:10) {
  I <- ((i-1)*ny+1):(ny*i)
  XU2[I,1:2] <- X
}
XU2[,3:4] <- lhs(10*ny, rect[3:4,])
XU <- rbind(XU, XU2)

## evaluate the computer model
Z <- M(XU[,1:2], XU[,3:4])

## flag indicating if estimating bias/discrepancy or not
bias.est <- TRUE
## two passes of ALC with MLE calculations for aGP.seq
methods <- rep("alcray", 2) ## demo uses rep("alc", 2)

## set up priors
da <- d <- darg(NULL, XU)
g <- garg(list(mle=TRUE), Y) 

## end identical data generation code

## now calculate log posterior for true calibration parameter 
## value (u).  You could repeat this for an estimate value 
## from demo("calib"), for example u.hat <- c(0.8236673, 0.1406989)

fcalib(u, XU, Z, X, Y, da, d, g, beta.prior, methods, M, bias.est, nth)