Basic Latin hypercube samples and designs with package lhs"

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

require(lhs)
source("VignetteCommonCode.R")

graph2dLHS <- function(Alhs)
{
  stopifnot(ncol(Alhs) == 2)
  sims <- nrow(Alhs)
  par(mar = c(4,4,2,2))
  plot.default(Alhs[,1], Alhs[,2], type = "n", ylim = c(0,1),
    xlim = c(0,1), xlab = "Parameter 1", ylab = "Parameter 2", xaxs = "i", 
    yaxs = "i", main = "")
  for (i in 1:nrow(Alhs))
  {
    rect(floor(Alhs[i,1]*sims)/sims, floor(Alhs[i,2]*sims)/sims,
      ceiling(Alhs[i,1]*sims)/sims, ceiling(Alhs[i,2]*sims)/sims, col = "grey")
  }
  points(Alhs[,1], Alhs[,2], pch = 19, col = "red")
  abline(v = (0:sims)/sims, h = (0:sims)/sims)
}

# transform is a function of the kind that takes a number
# transform <- function(x){return(qnorm(x,mean=0, std=1))}
graph2dLHSTransform <- function(Alhs, transform1, transform2, min1, max1, min2, max2)
{
  stopifnot(ncol(Alhs) == 2)
  stopifnot(all(Alhs[,1] <= max1 & Alhs[,1] >= min1))
  stopifnot(all(Alhs[,2] <= max2 & Alhs[,2] >= min2))
  sims <- nrow(Alhs)
  breaks <- seq(0,1,length = sims + 1)[2:(sims)]
  breaksTransformed1 <- sapply(breaks, transform1)
  breaksTransformed2 <- sapply(breaks, transform2)

  par(mar = c(4,4,2,2))
  plot.default(Alhs[,1], Alhs[,2], type = "n", 
               ylim = c(min2, max2),
               xlim = c(min1, max1),
               xlab = "Parameter 1", ylab = "Parameter 2", 
               xaxs = "i", yaxs = "i", main = "")
  for (si in 1:sims)
  {
    temp <- Alhs[si,]
    for (i in 1:sims)
    {
      if ((i == 1 && min1 <= temp[1] && breaksTransformed1[i] >= temp[1]) ||
            (i == sims && max1 >= temp[1] && breaksTransformed1[i - 1] <= temp[1]) ||
            (breaksTransformed1[i - 1] <= temp[1] && breaksTransformed1[i] >= temp[1]))
      {
        for (j in 1:sims)
        {
          if ((j == 1 && min2 <= temp[2] && breaksTransformed2[j] >= temp[2]) ||
                (j == sims && max2 >= temp[2] && breaksTransformed2[j - 1] <= temp[2]) ||
                (breaksTransformed2[j - 1] <= temp[2] && breaksTransformed2[j] >= temp[2]))
          {
            if (i == 1)
            {
              xbot <- min1
              xtop <- breaksTransformed1[i]
            } else if (i == sims)
            {
              xbot <- breaksTransformed1[i - 1]
              xtop <- max1
            } else 
            {
              xbot <- breaksTransformed1[i - 1]
              xtop <- breaksTransformed1[i]
            }
            if (j == 1)
            {
              ybot <- min2
              ytop <- breaksTransformed2[j]
            } else if (j == sims)
            {
              ybot <- breaksTransformed2[j - 1]
              ytop <- max2
            } else 
            {
              ybot <- breaksTransformed2[j - 1]
              ytop <- breaksTransformed2[j]
            }
            rect(xbot, ybot, xtop, ytop, col = "grey")
          }

        }
      }
    }
  }
  points(Alhs[,1], Alhs[,2], pch = 19, col = "red")
  abline(v = breaksTransformed1, h = breaksTransformed2)
}

#set.seed(1111)
#A <- randomLHS(5,4)
#f <- function(x){qnorm(x)}
#g <- function(x){qlnorm(x, meanlog=0.5, sdlog=1)}
#B <- A
#B[,1] <- f(A[,1])
#B[,2] <- g(A[,2])
#graph2dLHSTransform(B[,1:2], f, g, -4, 4, 0, 8)
#f <- function(x){qunif(x, 3, 5)}
#B <- apply(A, 2, f)
#graph2dLHSTransform(B[,1:2], f)

Theory of Latin Hypercube Sampling

For the technical basis of Latin Hypercube Sampling (LHS) and Latin Hypercube Designs (LHD) please see: Stein, Michael. Large Sample Properties of Simulations Using Latin Hypercube Sampling Technometrics, Vol 28, No 2, 1987. McKay, MD, et.al. A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code Technometrics, Vol 21, No 2, 1979.

This package was created to bring these designs to R and to implement many of the articles that followed on optimized sampling methods.

Create a Simple LHS

Basic LHS's are created using randomLHS.

# set the seed for reproducibility
set.seed(1111)
# a design with 5 samples from 4 parameters
A <- randomLHS(5, 4) 
A

In general, the LHS is uniform on the margins until transformed (r registerFigure("X")):

r addFigureCaption("X", "Two dimensions of a Uniform random LHS with 5 samples", register=FALSE)

graph2dLHS(A[,1:2])

It is common to transform the margins of the design (the columns) into other distributions (r registerFigure("Y"))

B <- matrix(nrow = nrow(A), ncol = ncol(A))
B[,1] <- qnorm(A[,1], mean = 0, sd = 1)
B[,2] <- qlnorm(A[,2], meanlog = 0.5, sdlog = 1)
B[,3] <- A[,3]
B[,4] <- qunif(A[,4], min = 7, max = 10)
B

r addFigureCaption("Y", "Two dimensions of a transformed random LHS with 5 samples", register=FALSE)

f <- function(x){qnorm(x)}
g <- function(x){qlnorm(x, meanlog = 0.5, sdlog = 1)}
graph2dLHSTransform(B[,1:2], f, g, -4, 4, 0, 8)

Optimizing the Design

The LHS can be optimized using a number of methods in the lhs package. Each method attempts to improve on the random design by ensuring that the selected points are as uncorrelated and space filling as possible. r registerTable("tab1") shows some results. r registerFigure("Z"), r registerFigure("W"), and r registerFigure("G") show corresponding plots.

set.seed(101)
A <- randomLHS(30, 10)
A1 <- optimumLHS(30, 10, maxSweeps = 4, eps = 0.01)
A2 <- maximinLHS(30, 10, dup = 5)
A3 <- improvedLHS(30, 10, dup = 5)
A4 <- geneticLHS(30, 10, pop = 1000, gen = 8, pMut = 0.1, criterium = "S")
A5 <- geneticLHS(30, 10, pop = 1000, gen = 8, pMut = 0.1, criterium = "Maximin")

r addTableCaption("tab1", "Sample results and metrics of various LHS algorithms", register=FALSE)

Method | Min Distance btwn pts | Mean Distance btwn pts | Max Correlation btwn pts :-----|:-----:|:-----:|:-----: randomLHS | r min(dist(A)) | r mean(dist(A)) | r max(abs(cor(A)-diag(10))) optimumLHS | r min(dist(A1)) | r mean(dist(A1)) | r max(abs(cor(A1)-diag(10))) maximinLHS | r min(dist(A2)) | r mean(dist(A2)) | r max(abs(cor(A2)-diag(10))) improvedLHS | r min(dist(A3)) | r mean(dist(A3)) | r max(abs(cor(A3)-diag(10))) geneticLHS (S) | r min(dist(A4)) | r mean(dist(A4)) | r max(abs(cor(A4)-diag(10))) geneticLHS (Maximin) | r min(dist(A5)) | r mean(dist(A5)) | r max(abs(cor(A5)-diag(10)))


r addFigureCaption("Z", "Pairwise margins of a randomLHS", register=FALSE)

pairs(A, pch = 19, col = "blue", cex = 0.5)

r addFigureCaption("W", "Pairwise margins of a optimumLHS", register=FALSE)

pairs(A1, pch = 19, col = "blue", cex = 0.5)

r addFigureCaption("G", "Pairwise margins of a maximinLHS", register=FALSE)

pairs(A2, pch = 19, col = "blue", cex = 0.5)


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lhs documentation built on March 22, 2022, 1:05 a.m.