| sim.spatialProcess {fields} | R Documentation |
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.
sim.spatialProcess(object, xp, M = 1, verbose = FALSE, ...)
simSpatialData(object, M = 1, verbose = FALSE)
simLocal.mKrig(mKrigObject,
predictionGridList = NULL,
simulationGridList = NULL,
gridRefinement = 1,
np = 2,
M = 1,
nx = 80,
ny = 80,
verbose = FALSE,
delta = NULL, giveWarnings = TRUE,
...)
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.Krig(object, xp, M = 1, verbose = FALSE, ...)
sim.Krig.approx(object, grid.list = NULL, M = 1, nx = 40, ny = 40,
verbose = FALSE, extrap = FALSE,...)
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 |
extrap |
If FALSE conditional process is not evaluated outside the convex hull of observations. |
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. |
giveWarnings |
If true will warn when more than one observation is in a grid box. This is instead of giving an error and stopping. |
mKrigObject |
An mKrig Object (or spatialProcess object) |
M |
Number of draws from conditional distribution. |
np |
Degree of nearest neighbors to use. Default |
nx |
Number of grid points in prediction locations for x coordinate. |
ny |
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. |
predictionGridList |
A grid list specifying the grid locations for the conditional samples. |
predictionPoints |
A matrix of locations defining the points for evaluating the predictions. |
predictionPointsList |
A |
simulationGridList |
A |
xp |
Same as predictionPoints above. |
... |
Any other arguments to be passed to the predict function.
Usually this is the |
verbose |
If true prints out intermediate information. |
These functions generate samples from an unconditional or conditional multivariate (spatial)
distribution, or an approximate one. The unconditional simulation function,
simSpatialData,
is a handy way to generate synthetic observations from a fitted model.
Typically one would use these for a parametric bootstrap.
The functions that simulate conditional distributions are much more involved
in their coding. They are useful
for describing the uncertainty in predictions using 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. Although these functions might
be coded up easily by the users these
versions have the advantage that they take the mKrig, spatialProcess or
Krig objects as a way to specify the
model in an unambiguous way.
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.
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 ].
The approximations come int step 1). Here the field at the observation locations is approximated using interpolation from the nearest grid points.
sim.spatialProcess Follows this algorithm exactly. For the case of an
addtional covariate this of course needs to be included. For a model
with covariates use drop.Z=TRUE for the function to ignore prediction
using the covariate and generate conditional samples for just the spatial
process and any low order polynomial. Finally, it should be noted that this
function will also work with an mKrig object because the essential
prediction information in the mKrig and spatialProcess objects are the same.
The naming is through convenience.
sim.Krig Also follows this algorithm exactly but for the older Krig object. Note the inclusion of
drop.Z=TRUE or FALSE will determine whether the conditional simulation
includes the covariates Z or not. (See example below.)
simNN.mKrig
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.Krig and sim.spatialProcess
a matrix with rows 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)
Doug Nychka
sim.rf, Krig, spatialProcess
## 10 member ensemble for the O3 data
## Not run:
data( "ozone2")
mKrigObject<- spatialProcess( ozone2$lon.lat, ozone2$y[16,],
smoothness=.5)
nx<- 65
ny<- 65
xGridList<- fields.x.to.grid( mKrigObject$x, nx=nx, ny=ny)
xGrid<- make.surface.grid( xGridList)
allTime0<- system.time(
look0<- sim.spatialProcess(mKrigObject, xp= xGrid, M=10)
)
print( allTime0)
## Local simulation with extra refinement of the grid for embedding
## and same grid size for prediction
## this runs much faster compared to exact method above
## as nx, ny are increased e.g. nx= 128, ny=128 is dramatic difference
allTime<- system.time(
look<- simLocal.mKrig(mKrigObject, M=10,nx=nx, ny=ny,
gridRefinement = 3,
np=3)
)
print( allTime)
print( look$timing)
## End(Not run)
## Not run:
## A simple example for setting up a bootstrap
## M below should be
## set to much larger sample size ( e.g. M <- 200) for better
## statistics
data( ozone2)
obj<- spatialProcess( ozone2$lon.lat,ozone2$y[16,] )
aHat<- obj$summary["aRange"]
lambdaHat<- obj$summary["lambda"]
######### boot strap
# create M independent copies of the observation vector
# here we just grab the model information from the
# spatialProcess object above.
#
# However, one could just create the list
# obj<- list( x= ozone2$lon.lat,
# cov.function.name="stationary.cov",
# summary= c( tau= 9.47, sigma2= 499.79, aRange= .700),
# cov.args= list( Covariance="Matern", smoothness=1.0),
# weights= rep( 1, nrow(ozone2$lon.lat) )
# )
# Here summary component has the parameters
# tau, sigma2 and aRange
# and cov.args component has the remaining ones.
set.seed(223)
M<- 25
ySynthetic<- simSpatialData( obj, M)
bootSummary<- NULL
for( k in 1:M){
cat( k, " ")
# here the MLEs are found using the easy top level level wrapper
# see mKrigMLEJoint for a more efficient strategy
newSummary<- spatialProcess(obj$x,ySynthetic[,k],
cov.params.start= list(
aRange = aHat,
lambda = lambdaHat)
)$summary
bootSummary<- rbind( bootSummary, newSummary)
}
cat( fill= TRUE)
# the results and 95
stats( bootSummary )
obj$summary
tmpBoot<- bootSummary[,c("lambda", "aRange") ]
confidenceInterval <- apply(tmpBoot, 2,
quantile, probs=c(0.025,0.975) )
# compare to estimates used as the "true" parameters
obj$summary[2:5]
print( t(confidenceInterval) )
# compare to confidence interval using large sample theory
print( obj$CITable)
## End(Not run)
## Not run:
# conditional simulation with covariates
# colorado climate example
data(COmonthlyMet)
fit1E<- spatialProcess(CO.loc,CO.tmin.MAM.climate, Z=CO.elev )
# conditional simulation at missing data
good<- !is.na(CO.tmin.MAM.climate )
infill<- sim.spatialProcess( fit1E, xp=CO.loc[!good,],
Z= CO.elev[!good], M= 10)
# get an elevation grid ... NGRID<- 50 gives a nicer image but takes longer
NGRID <- 25
# get elevations on a grid
COGrid<- list( x=seq( -109.5, -101, ,NGRID), y= seq(39, 41.5,,NGRID) )
COGridPoints<- make.surface.grid( COGrid)
# elevations are a bilinear interpolation from the 4km
# Rocky Mountain elevation fields data set.
data( RMelevation)
COElevGrid<- interp.surface( RMelevation, COGridPoints )
# NOTE call to sim.spatialProcess treats the grid points as just a matrix
# of locations the plot has to "reshape" these into a grid
# to use with image.plot
SEout<- sim.spatialProcess( fit1E, xp=COGridPoints, Z= COElevGrid, M= 30)
# for just the smooth surface in lon/lat
# SEout<- sim.spatialProcess( fit1E, xp=COGridPoints, drop.Z=TRUE, M= 30)
# in practice M should be larger to reduce Monte Carlo error.
surSE<- apply( SEout, 2, sd )
image.plot( as.surface( COGridPoints, surSE))
points( fit1E$x, col="magenta", pch=16)
## End(Not run)
data( ozone2)
set.seed( 399)
# fit to day 16 from Midwest ozone data set.
out<- Krig( ozone2$lon.lat, ozone2$y[16,], Covariance="Matern",
aRange=1.0,smoothness=1.0, na.rm=TRUE)
# NOTE aRange =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$z[,,k], col=tim.colors(), zlim =zr)
}
## Not run:
data( ozone2)
y<- ozone2$y[16,]
good<- !is.na( y)
y<-y[good]
x<- ozone2$lon.lat[good,]
O3.fit<- mKrig( x,y, Covariance="Matern", aRange=.5,smoothness=1.0, lambda= .01 )
set.seed(122)
O3.sim<- sim.mKrig.approx( O3.fit, nx=100, ny=100, gridRefinement=3, M=5 )
set.panel(3,2)
surface( O3.fit)
for ( k in 1:5){
image.plot( as.surface( O3.sim$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 )
## 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,]
O3Obj<- fastTps( x,y, aRange=1.5 )
# creating a quick grid list based on ranges of locations
grid.list<- fields.x.to.grid( O3Obj$x, nx=100, ny=100)
# 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.mKrig.approx( O3Obj, M=5, predictionPointsList=O3GridList,
simulationGridList = simulationGridList)
## End(Not run)