Package 'maximin'

spatial locations of 1535 weather stations

Description

The dataset contains spatial locations of 1535 weather stations for measuring solar irradiance across the continental United States.

Usage

data(lola_kn)

Format

A data frame containing 1535 observations and 2 variables

Source

https://onlinelibrary.wiley.com/doi/abs/10.1002/sam.11414

References

F. Sun, R.B. Gramacy, B. Haaland, S.Y. Lu, and Y. Hwang (2019) Synthesizing Simulation and Field Data of Solar Irradiance, Statistical Analysis and Data Mining, 12(4), 311-324; preprint on arXiv:1806.05131.

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Space-filling design under the criterion of maximin distance

Description

Generates a space-filling design under the criterion of maximum-minimum distance; both discrete and continuous searches are provided.

Usage

maximin.cand(n, Xcand, Tmax, Xorig=NULL, init=NULL, verb=FALSE, tempfile=NULL) 
  maximin(n, p, T, Xorig=NULL, Xinit=NULL, verb=FALSE, plot=FALSE, boundary=FALSE)

Arguments

n	the number of space-filling locations
Xcand	the candidate set, from which each space-filling location is selected
Tmax	the number of iterations; Tmax <= nrow(Xcand); to be safe, set Tmax = nrow(Xcand).
Xorig	the existing design; ncol(Xorig) = ncol(Xcand)
init	the initial indices of X; it can be randomly selected from Xcand or introduced from a previous experiment.
verb	progress indicator — every tenth iteration is printed out; by default verb = FALSE.
tempfile	the name of a temporary file given the progress is saved with each iteration; by default tempfile = NULL
p	the dimensionality of input space
T	the number of iterations; $T > n$; setting $T = 10 * n$ is a good starting point.
Xinit	the (initial) design introduced from a previous experiment
plot	if plot = TRUE, then the search space and the "start location –> new location" with each iteration is plotted; if $p > 2$, then TWO input coordinates are RANDOMLY chosen for plotting; it is worth noticing that the search space only VISUALLY makes sense when $p = 2$.
boundary	if boundary = TRUE, then for each iteration, the "to-be-swapped-in" location will be away from the boundary in addition to being away from other X locations and Xorig; how far is it? $min(d, 4*d.bound)$, where d is the Euclidean distance between the "to-be-swapped-in" location and other X locations as well as Xorig, while d.bound is the minimum Euclidean distance between the "to-be-swapped-in" location and the boundaries.

Details

Constructing a space-filling design under the criterion of maximum-minimum distance is quite useful in computer experiments and related fields. Previously, researchers would construct such a design in a random accept-reject way, i.e., randomly propose a location within the study region to replace a randomly selected row from the initial design. If such a proposal increases the minimum pairwise Euclidean distance, then accept the replacement; otherwise keep the original design location. By repeatedly proposing (and accept-rejecting) in this way one is able to construct an (approximately) space-filling design. However the algorithm is inefficient computationally. The reason is that the proposals are not optimized in any way.

In this package, we provide an alternative to build up a well-defined space-filling design more efficiently. There are two versions, one is with discrete search, while the other is with continuous search. For the former, each iteration proposes to swap out a row from the initial design with the minimum distance, and swap in one location from a candidate set to increase the minimum distance. For the latter, the core idea is the same, but instead of working with a candidate set, optim is used to maximize the distance between the "to-be-swapped-in" location and other design locations as well as to any existing design, Xorig. Several heuristics are deployed for situations where the search becomes stuck in a local mode. One involves moving to a location with non-minimum distance, and the other is to jump to a location which has the maximum minimum distance.

For a visualization of applying maximin.cand in a real-life problem on solar irradiance, see Sun et al. (2019).

maximin.cand returns the indices of Xcand, which makes the final space-filling design, and the minimum pairwise Euclidean distance with each iteration

maximin returns the combined existing design and the space-filling design, together with the minimum pairwise Euclidean distance with each iteration

Value

maximin.cand returns

inds	the indices of Xcand, which makes the final space-filling design
mis	the minimum distance with each iteration; length(mis) = Tmax + 1

maximin returns

Xf	dim(Xf) = (nrow(Xorig) + n) * p
mi	the minimum distance with each iteration; length(mi) = T + 1

Author(s)

Furong Sun [email protected] and Robert B. Gramacy [email protected]

References

F. Sun, R.B. Gramacy, B. Haaland, S.Y. Lu, and Y. Hwang (2019) Synthesizing Simulation and Field Data of Solar Irradiance, Statistical Analysis and Data Mining, 12(4), 311-324; preprint on arXiv:1806.05131.

M.H.Y. Tan (2013) Minimax Designs for Finite Design Regions, Technometrics, 55(3), 346-358.

M.E. Johnson, L.M. Moore, and D. Yivisaker (1990) Minimax and Maximin Distance Designs, Journal of Statistical Planning and Inference, 26(2), 131-148.

Examples

## Not run: 
   ## maximin.cand
   # generate the design
   library("lhs")
   n <- 100
   p <- 2
   Xorig <- randomLHS(10, p)
   x1 <- seq(0, 1, length.out=n)
   Xcand <- expand.grid(replicate(p, x1, simplify=FALSE))
   names(Xcand) <- paste0("x", 1:2)
   T <- nrow(Xcand)
   Xsparse <- maximin.cand(n=n, Xcand=Xcand, Tmax=T, Xorig=Xorig, 
                           init=NULL, verb=FALSE, tempfile=NULL)
   
   maxmd <- as.numeric(format(round(max(na.omit(Xsparse$mis)), 5), nsmall=5))
   
   # visualization
   par(mfrow=c(1, 2))
   X <- Xcand[Xsparse$inds,]
   plot(X$x1, X$x2, xlab=expression(x[1]), ylab=expression(x[2]), 
        xlim=c(0, 1), ylim=c(0, 1), 
        main=paste0("n=", n, "_p=", p, "_maximin=", maxmd))
   points(Xorig, col=2, pch=20)
   abline(h=c(0, 1), v=c(0, 1), lty=2, col=2)
   if(!is.null(Xorig))
   {
     legend("topright", "Xorig", xpd=TRUE, horiz=TRUE, 
            inset=c(-0.03, -0.05), pch=20, col=2, bty="n")
   }
   plot(log(na.omit(Xsparse$mis)), type="b", 
        xlab="iteration", ylab="log(minimum distance)", 
        main="progress on minimum distance")
   abline(v=n, lty=2)
   mtext(paste0("design size=", n), at=n, cex=0.6)
   
  
## End(Not run)
  
  ## maximin
  # generate the design
  library("lhs")
  n <- 10
  p <- 2
  T <- 10*n
  Xorig <- randomLHS(10, p)
  Xsparse <- maximin(n=n, p=p, T=T, Xorig=Xorig, Xinit=NULL, 
                     verb=FALSE, plot=FALSE, boundary=FALSE)
  maxmd <- as.numeric(format(round(Xsparse$mi[T+1], 5), nsmall=5))
  
  # visualization
  par(mfrow=c(1,2))
  plot(Xsparse$Xf[,1], Xsparse$Xf[,2], xlab=expression(x[1]), ylab=expression(x[2]), 
       xlim=c(0, 1), ylim=c(0, 1), 
       main=paste0("n=", n, " p=", p, " T=", T, " maximin=", maxmd))
  points(Xorig, col=2, pch=20)
  abline(h=c(0,1), v=c(0,1), lty=2, col=2)
  if(!is.null(Xorig)) legend("topright", "Xorig", xpd=TRUE, horiz=TRUE, 
     inset=c(-0.03, -0.05), pch=20, col=2, bty="n")
  plot(log(Xsparse$mi), type="b", xlab="iteration", ylab="log(minimum distance)", 
       main="progress on minimum distance")
  abline(v=n, lty=2)
  mtext(paste0("design size=", n), at=n, cex=0.6)
  abline(v=T, lty=2)
  mtext(paste0("max.md=", maxmd), at=T, cex=0.6)

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