sim.Krig: Conditional simulation of a spatial process

Description

Generates exact (or approximate) random draws from the conditional distribution of a spatial process given specific observations. This is a useful way to characterize the uncertainty in the predicted process from data. This is known as conditional simulation in geostatistics or generating an ensemble prediction in the geosciences. sim.Krig.grid can generate a conditional sample for a large regular grid but is restricted to stationary correlation functions.

Usage

sim.Krig(object, xp, M = 1, verbose = FALSE, ...)
sim.Krig.approx(object, grid.list = NULL, M = 1, nx = 40, ny = 40, verbose = FALSE, extrap = FALSE,...)
sim.mKrig.approx(mKrigObject, predictionPoints = NULL, predictionPointsList = NULL, simulationGridList = NULL, gridRefinement = 5, gridExpansion = 1 + 1e-07, M  = 1, nx = 40, ny = 40, nxSimulation = NULL, nySimulation = NULL, delta = NULL, verbose = FALSE,...) 
sim.fastTps.approx(fastTpsObject,  predictionPointsList, simulationGridList = NULL, gridRefinement = 5, gridExpansion = 1 + 1e-07, M  = 1,  delta = NULL, verbose=FALSE,...)

Arguments

delta

If the covariance has compact support the simulation method can take advantage of this. This is the amount of buffer added for the simulation domain in the circulant embedding method. A minimum size would be theta for the Wendland but a multiple of this maybe needed to obtain a positive definite circulant covariance function.

extrap

If FALSE conditional process is not evaluated outside the convex hull of observations.

fastTpsObject

The output object returned by fastTps

grid.list

Grid information for evaluating the conditional surface as a grid.list.

gridRefinement

Amount to increase the number of grid points for the simulation grid.

gridExpansion

Amount to increase the size of teh simulation grid. This is used to increase the simulation domain so that the circulant embedding algorithm works.

mKrigObject

An mKrig Object

Number of draws from conditional distribution.

Number of grid points in prediction locations for x coordinate.

nxSimulation

Number of grid points in the circulant embedding simulation x coordinate.

nySimulation

Number of grid points in the circulant embedding simulation x coordinate.

object

A Krig object.

predictionPoints

A matrix of locations defining the points for evaluating the predictions.

predictionPointsList

A grid.list defining the rectangular grid for evaluating the predictions.

simulationGridList

A gridlist describing grid for simulation. If missing this is created from the range of the locations, nx, ny, gridRefinement, and gridExpansion or from the range and and nxSimulation, nySimulation.

Same as predictionPoints above.

...

Any other arguments to be passed to the predict function. Usually this is the Z or drop.Z argument when there are additional covariates in the fixed part of the model. (See example below.)

verbose

If true prints out intermediate information.

Value

sim.Krig a matrix with rowss indexed by the locations in xp and columns being the M independent draws.sim.Krig.approx a list with components x, y and z. x and y define the grid for the simulated field and z is a three dimensional array with dimensions c(nx, ny, M) where the first two dimensions index the field and the last dimension indexes the draws.sim.mKrig.approx a list with predictionPoints being the locations where the field has been simulated.If these have been created from a grid list that information is stored in the attributes of predictionPoints. Ensemble is a matrix where rows index the simulated values of the field and columns are the different draws, call is the calling sequence. Not that if predictionPoints has been omitted in the call or is created beforehand using make.surface.grid it is easy to reformat the results into an image format for ploting using as.surface. e.g. if simOut is the output object then to plot the 3rd draw:

     imageObject<- as.surface(simOut$PredictionGrid, simOut$Ensemble[,3] )
     image.plot( imageObject)

sim.fastTps.approx is a wrapper function that calls sim.mKrig.approx.

Details

These functions generate samples from a conditional multivariate distribution, or an approximate one, that describes the uncertainty in the estimated spatial process under Gaussian assumptions. An important assumption throughout these functions is that all covariance parameters are fixed at their estimated or prescribed values from the passed object.

Given a spatial process h(x)= P(x) + g(x) observed at

Y.k = Z(x.k)d + P(x.k) + g(x.k) + e.k

where P(x) is a low order, fixed polynomial and g(x) a Gaussian spatial process and Z(x.k) is a vector of covariates that are also indexed by space (such as elevation). Z(x.k)d is a linear combination of the the covariates with the parameter vector d being a component of the fixed part of the model and estimated in the usual way by generalized least squares.

With Y= Y.1, ..., Y.N, the goal is to sample the conditional distribution of the process. [h(x) | Y ] or the full prediction Z(x)d + h(x)

For fixed a covariance this is just a multivariate normal sampling problem. sim.Krig.standard samples this conditional process at the points xp and is exact for fixed covariance parameters. sim.Krig.grid also assumes fixed covariance parameters and does approximate sampling on a grid.

The outline of the algorithm is

0) Find the spatial prediction at the unobserved locations based on the actual data. Call this h.hat(x) and this is the conditional mean.

1) Generate an unconditional spatial process and from this process simluate synthetic observations. At this point the approximation is introduced where the field at the observation locations is approximated using interpolation from the nearest grid points.

2) Use the spatial prediction model ( using the true covariance) to estimate the spatial process at unobserved locations.

3) Find the difference between the simulated process and its prediction based on synthetic observations. Call this e(x).

4) h.hat(x) + e(x) is a draw from [h(x) | Y ].

sim.Krig follows this algorithm exactly. Note the inclusion of drop.Z=TRUE or FALSE will determine whether the conditional simulation includes the covariates Z or not. (See example below.)

sim.Krig.approx and sim.mKrig.approx evaluate the conditional surface on grid and simulates the values of h(x) off the grid using bilinear interpolation of the four nearest grid points. Because of this approximation it is important to choose the grid to be fine relative to the spacing of the observations. The advantage of this approximation is that one can consider conditional simulation for large grids -- beyond the size possible with exact methods. Here the method for simulation is circulant embedding and so is restricted to stationary fields. The circulant embedding method is known to fail if the domain is small relative to the correlation range. The argument gridExpansion can be used to increase the size of the domain to make the algorithm work.

sim.fastTps.approx Is optimized for the approximate thin plate spline estimator in two dimensions and k=2. For efficiency the ensemble prediction locations must be on a grid.

Examples

Run this code

data( ozone2)

set.seed( 399)

# fit to day 16 from Midwest ozone data set.
  out<- Krig( ozone2$lon.lat, ozone2$y[16,], Covariance="Matern", 
            theta=1.0,smoothness=1.0, na.rm=TRUE)

# NOTE theta =1.0 is not the best choice but 
# allows the sim.rf circulant embedding algorithm to 
# work without increasing the domain.

#six missing data locations
 xp<-  ozone2$lon.lat[ is.na(ozone2$y[16,]),]

# 5 draws from process at xp given the data 
# this is an exact calculation
 sim.Krig( out,xp, M=5)-> sim.out

# Compare: stats(sim.out)[3,] to  Exact: predictSE( out, xp)

# simulations on a grid
# NOTE this is approximate due to the bilinear interpolation
# for simulating the unconditional random field. 
# also more  grids points ( nx and  ny) should be used  

sim.Krig.approx(out,M=5, nx=20,ny=20)-> sim.out

# take a look at the ensemble members. 

predictSurface( out, grid= list( x=sim.out$x, y=sim.out$y))-> look

zr<- c( 40, 200)

set.panel( 3,2)
image.plot( look, zlim=zr)
title("mean surface")
for ( k in 1:5){
image( sim.out$x, sim.out$y, sim.out$z[,,k], col=tim.colors(), zlim =zr)
}

## Not run: 
# # conditional simulation with covariates
# # colorado climate example
#   data(COmonthlyMet)
#   fit1E<- spatialProcess(CO.loc,CO.tmin.MAM.climate, Z=CO.elev   )
# # conditional simulation at missing data
#   good<- !is.na(CO.tmin.MAM.climate ) 
#   infill<- sim.Krig( fit1E, xp=CO.loc[!good,],  Z= CO.elev[!good], M= 100)
# # get an elevation grid  ... NGRID<- 50 gives a nicer image but takes longer 
#  NGRID <- 25  
#  # get elevations on a grid  
#    COGrid<- list( x=seq( -109.5, -101, ,NGRID), y= seq(39, 41.5,,NGRID) )
#    COGridPoints<- make.surface.grid( COGrid)
#  # elevations are a bilinear interpolation from the 4km
#  # Rocky Mountain elevation fields data set.   
#    data( RMelevation)
#    COElevGrid<- interp.surface( RMelevation, COGridPoints )
# # NOTE call to sim.Krig treats the grid points as just a matrix
# # of locations the plot has to "reshape" these into a grid 
# # to use with image.plot 
#    SEout<- sim.Krig( fit1E, xp=COGridPoints,  Z= COElevGrid, M= 30)
# # for just the smooth surface in lon/lat
# #  SEout<- sim.Krig( fit1E, xp=COGridPoints,  drop.Z=TRUE, M= 30)
# # in practice M should be larger to reduce Monte Carlo error.      
#    surSE<- apply( SEout, 2, sd )
#    image.plot( as.surface( COGridPoints, surSE)) 
#    points( fit1E$x, col="magenta", pch=16) 
# ## End(Not run)
## Not run: 
# data( ozone2)
# y<- ozone2$y[16,]
# good<- !is.na( y)
# y<-y[good]
# x<- ozone2$lon.lat[good,]
# O3.fit<- mKrig( x,y, Covariance="Matern", theta=.5,smoothness=1.0, lambda= .01 )
# set.seed(122)
# O3.sim<- sim.mKrig.approx( O3.fit, nx=100, ny=100, gridRefinement=3, M=5 )
# set.panel(3,2)
# surface( O3.fit)
# for ( k in 1:5){
# image.plot( as.surface( O3.sim$predictionPoints, O3.sim$Ensemble[,k]) )
# }
# # conditional simulation at missing data
# xMissing<- ozone2$lon.lat[!good,]
# O3.sim2<- sim.mKrig.approx( O3.fit, xMissing, nx=80, ny=80, gridRefinement=3, M=4, verbose=TRUE )
# #check of Wendland
# O3.fit.Wendland<- mKrig( x,y, cov.function="wendland.cov", theta=.5, lambda= .01 )
# O3.sim3<- sim.mKrig.approx( O3.fit.Wendland, xMissing, nx=80, ny=80, gridRefinement=3, M=400,
#                                verbose=TRUE )
# O3.sim4<- sim.mKrig.approx( O3.fit.Wendland, xMissing, nx=80, ny=80, gridRefinement=3, M=400,
#                                  verbose=TRUE, delta=O3.fit.Wendland$args$theta )
# ## End(Not run)
## Not run: 
# #An example for fastTps:
#   data(ozone2)
#   y<- ozone2$y[16,]
#   good<- !is.na( y)
#   y<-y[good]
#   x<- ozone2$lon.lat[good,]
#   O3FitMLE<- fastTps.MLE( x,y, theta=1.5 )
#   O3Obj<- fastTps( x,y, theta=1.5, lambda=O3FitMLE$lambda.MLE)
# # creating a quick grid list based on ranges of locations
#   grid.list<- fields.x.to.grid( O3Obj$x, nx=100, ny=100)
#   O3Sim<- sim.fastTps.approx( O3Obj,predictionPointsList=grid.list,M=5)
# # controlling the grids
#   xR<- range( x[,1], na.rm=TRUE)
#   yR<- range( x[,2], na.rm=TRUE)
#   simulationGridList<- list( x= seq(xR[1],xR[2],,400), y= seq( yR[1],yR[2], ,400))
# # very fine localized prediction grid
#     O3GridList<- list( x= seq( -90.5,-88.5,,200), y= seq( 38,40,,200))
#     O3Sim<- sim.fastTps.approx( O3Obj, M=5, predictionPointsList=O3GridList,
#                   simulationGridList = simulationGridList, verbose=TRUE)
# # check 
#  plot( O3Obj$x)
#  US( add=TRUE)
#  image.plot( as.surface( O3GridList,O3Sim$Ensemble[,1] ), add=TRUE)
#  points( O3Obj$x, pch=16, col="magenta")
# ## End(Not run)