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Given two sets of locations these functions compute the cross covariance matrix for some covariance families. In addition these functions can take advantage of spareness, implement more efficient multiplcation of the cross covariance by a vector or matrix and also return a marginal variance to be consistent with calls by the Krig function.
stationary.cov and Exp.cov have additional arguments for
precomputed distance matrices and for calculating only the upper triangle
and diagonal of the output covariance matrix to save time. Also, they
support using the rdist function with compact=TRUE or input
distance matrices in compact form, where only the upper triangle of the
distance matrix is used to save time.
Note: These functions have been been renamed from the previous fields functions
using 'Exp' in place of 'exp' to avoid conflict with the generic exponential
function (exp(...))in R.
Exp.cov(x1, x2 = NULL, aRange = 1, p = 1, distMat = NA, C =
NA, marginal = FALSE, onlyUpper = FALSE, theta = NULL,
...)
Exp.simple.cov(x1, x2, aRange =1, C=NA,marginal=FALSE, theta=NULL)Rad.cov(x1, x2, p = 1, m=NA, with.log = TRUE, with.constant = TRUE,
C=NA,marginal=FALSE, derivative=0)
cubic.cov(x1, x2, aRange = 1, C=NA, marginal=FALSE, theta=NULL)
Rad.simple.cov(x1, x2, p=1, with.log = TRUE, with.constant = TRUE,
C = NA, marginal=FALSE)
stationary.cov(x1, x2=NULL, Covariance = "Exponential", Distance = "rdist",
Dist.args = NULL, aRange = 1, V = NULL, C = NA, marginal = FALSE,
derivative = 0, distMat = NA, onlyUpper = FALSE, theta=NULL, ...)
stationary.taper.cov(x1, x2, Covariance="Exponential",
Taper="Wendland",
Dist.args=NULL, Taper.args=NULL,
aRange=1.0,V=NULL, C=NA, marginal=FALSE,
spam.format=TRUE,verbose=FALSE, theta=NULL,...)
Tps.cov(x1, x2 = NULL, cardinalX, m=2,
C = NA, aRange=NA,
marginal = FALSE
)
wendland.cov(x1, x2, aRange = 1, V=NULL, k = 2, C = NA,
marginal =FALSE,Dist.args = list(method = "euclidean"),
spam.format = TRUE, derivative = 0, verbose=FALSE, theta=NULL)
Paciorek.cov(x1,
x2 = NULL,
Distance = "rdist",
Dist.args = NULL,
aRangeObj = 1,
rhoObj = NULL,
C = NA,
marginal = FALSE,
smoothness = .5)
If the argument C is NULL the cross covariance matrix is returned. In general if nrow(x1)=m and nrow(x2)=n then the returned matrix will be mXn. Moreover, if x1 is equal to x2 then this is the covariance matrix for this set of locations.
If C is a vector of length n, then returned value is the multiplication of the cross covariance matrix with this vector.
Matrix of first set of locations where each row gives the coordinates of a particular point.
Matrix of second set of locations where each row gives the coordinatesof a particular point. If this is missing x1 is used.
Range (or scale) parameter. This should be a scalar (use
the V argument for other scaling options). Any distance calculated for
a covariance function is divided by aRange before the covariance function
is evaluated. For Tps.cov this argument is ignored.
A fit object that defines the Range (or scale) parameter for arbitray locations using the generic predict function.
Old version of the aRange parameter. If passed will be copied to aRange.
Locations added to the thin plate plate radial function to make it positive definite (See Details below).
A vector or matrix with the same rows as the number of rows of x2. If specified the covariance matrix will be multiplied by this vector/matrix.
Character string that is the name of the covariance
shape function for the distance between locations. Choices in fields
are Exponential, Matern
If nonzero evaluates the partials of the
covariance function at locations x1. This must be used with the "C" option
and is mainly called from within a predict function. The partial
derivative is taken with respect to x1.
Character string that is the name of the distance
function to use. Choices in fields are rdist,
rdist.earth
If the distance matrix between x1 and x2 has already been
computed, it can be passed via this argument so it won't need to be
recomputed.
A list of optional arguments to pass to the Distance function.
The order of the Wendland covariance function. See help on Wendland.
If TRUE returns just the diagonal elements of the
covariance matrix using the x1 locations. In this case this is
just 1.0. The marginal argument will trivial for this function is a
required argument and capability for all covariance functions used
with Krig.
For the radial basis function p = 2m-d, with d being the dimension of the locations, is the exponent applied to the distance between locations. (m is a common way of parametrizing this exponent.) Equivalently in Tps.cov the order of the spline. (See Details section below).
For internal use only, not meant to be set by the user. Automatically
set to TRUE by mKrigMLEJoint or mKrigMLEGrid if
lambda.profile is set to TRUE, but set to FALSE
for the final parameter fit so output is compatible with rest of
fields.
If TRUE, only the upper triangle and diagonal of the covariance
matrix is computed to save time (although if a non-compact distance
matrix is used, the onlyUpper argument is set to FALSE).
If FALSE,
the entire covariance matrix is computed. In general, it should
only be set to TRUE for mKrigMLEJoint and
mKrigMLEGrid,
and the default is set to FALSE so it is compatible with all of
fields.
Exponent in the exponential covariance family. p=1 gives an exponential and p=2 gives a Gaussian. Default is the exponential form. For the radial basis function this is the exponent applied to the distance between locations.
A fit object that defines a component of the marginal variance
(rho) parameter for
arbitray locations using the generic predict function. Note that in fields the complete marginal variance is sigma2*rho where sigma2 can be estimated in spatialProcess.
For the Matern family the smoothnes of the process (aka "nu" in formulas).
If TRUE returns matrix in sparse matrix format implemented in the spam package. If FALSE just returns a full matrix.
Character string that is the name of the taper function to use. Choices in fields are listed in help(taper).
A list of optional arguments to pass to the Taper
function. aRange should always be the name for the range (or
scale) paremeter.
A matrix that describes the inverse linear transformation of
the coordinates before distances are found. In R code this
transformation is: x1 %*% t(solve(V)) Default is NULL, that
is the transformation is just dividing distance by the scalar value
aRange. See Details below. If one has a vector of "aRange's"
that are the scaling for each coordinate then just express this as
V = diag(aRange) in the call to this function.
If TRUE prints out some useful information for debugging.
If TRUE includes complicated constant for radial
basis functions. See the function radbad.constant for more
details. The default is TRUE, include the constant. Without the usual
constant the lambda used here will differ by a constant from spline
estimators ( e.g. cubic smoothing splines) that use the
constant. Also a negative value for the constant may be necessary to
make the radial basis positive definite as opposed to negative
definite.
If TRUE include a log term for even dimensions. This is needed to be a thin plate spline of integer order.
Any other arguments that will be passed to the
covariance function. e.g. smoothness for the Matern.
For purposes of illustration, the function
Exp.cov.simple is provided in fields as a simple example and
implements the R code discussed below. List this function out as a
way to see the standard set of arguments that fields uses to define a
covariance function. It can also serve as a template for creating new
covariance functions for the Krig and mKrig
functions. Also see the higher level function stationary.cov to
mix and match different covariance shapes and distance functions.
A common scaling for stationary covariances: If x1 and
x2 are matrices where nrow(x1)=m and nrow(x2)=n
then this function will return a mXn matrix where the (i,j) element
is the covariance between the locations x1[i,] and
x2[j,]. The exponential covariance function is computed as
exp( -(D.ij)) where D.ij is a distance between x1[i,] and
x2[j,] but having first been scaled by aRange. Specifically if
aRange is a matrix to represent a linear transformation of the
coordinates, then let u= x1%*% t(solve( aRange)) and v=
x2%*% t(solve(aRange)). Form the mXn distance matrix with
elements:
D[i,j] = sqrt( sum( ( u[i,] - v[j,])**2 ) ).
and the cross covariance matrix is found by exp(-D). The
tapered form (ignoring scaling parameters) is a matrix with i,j entry
exp(-D[i,j])*T(D[i,j]). With T being a positive definite
tapering function that is also assumed to be zero beyond 1.
Note that if aRange is a scalar then this defines an isotropic
covariance function and the functional form is essentially
exp(-D/aRange).
Implementation: The function r.dist is a useful FIELDS function
that finds the cross Euclidean distance matrix (D defined above) for
two sets of locations. Thus in compact R code we have
exp(-rdist(u, v))
Note that this function must also support two other kinds of calls:
If marginal is TRUE then just the diagonal elements are returned (in R
code diag( exp(-rdist(u,u)) )).
If C is passed then the returned value is exp(-rdist(u, v))
%*% C.
Some details on particular covariance functions:
stationary.cov:Here the computation is to apply the function Covariance to the distances found by the Distance function. For example
Exp.cov(x1,x2, aRange=MyTheta)
and
stationary.cov( x1,x2, aRange=MyTheta, Distance= "rdist",
Covariance="Exponential")
are the same. This also the same as
stationary.cov( x1,x2, aRange=MyTheta, Distance= "rdist",
Covariance="Matern",smoothness=.5).
Rad.cov:The
functional form is Constant* rdist(u, v)**p for odd dimensions and
Constant* rdist(u,v)**p * log( rdist(u,v) ) For an m th order thin plate
spline in d dimensions p= 2*m-d and must be positive. The constant,
depending on m and d, is coded in the fields function
radbas.constant. This form is only a generalized covariance
function -- it is only positive definite when restricted to linear
subspace. See Rad.simple.cov for a coding of the radial basis
functions in R code.
This covariance can be used in a standard
"Kriging" computation to give a thin-plate spline (TPS). This is
useful because one can use the high
level function spatialProcess and supporting functions for
the returned
object, including conditional simulation.
The standard computation for a TPS uses the radial basis functions
as given
in Rad.cov and uses a QR decomposition based a polynoimial
matrix to
reduce the dimension of the radial basis function and yield a
positive definite
matrix. This reduced matrix is then used in the regular
compuations to find
the spatial estimate. The function Krig and specifically
Tps
implements this algoritm. The interested reader should look at
Krig.engine.default and specifically at the TMatrix
polynomial matrix and resulting reduced positive definite matrix
tempM. The difficulty with this approach is that is not
amenable to taking advantage of sparsity in the covariance matrix.
An alternative that is suggested by Grace Wahba in Spline
models for
observational
data is to augment the radial basis functions with a low rank set
of
functions based on a low order polynomial evaluated at a set of
points. The set of locatios for this modifications are called
cardinal points due the to
property mentioned below. This is
implemented in Tps.cov leading to a full rank
(non-stationary!)
covariance function. If the fixed part of the spatial model also
includes
this same order polynomial then the final result gives a TPS and
is invariant
to the choice of cardinal points. To streamline using this
covariance when it isspecified in the spatialProcess
function the cardinal points will choosen automaitcally based on
the observation locations and the spline order, m using a
space filling design.
A simple example with fixed smoothing parameter, lambda <- .1 may help
data( "ozone2")
s<- ozone2$lon.lat
y<- ozone2$y[16,]
fitTps1<- spatialProcess( s,y, cov.function= "Tps.cov", lambda=.1)
and compare the results to the standard algorithm.
fitTps2<- Tps( s,y, scale.type ="unscaled", lambda=.1)stats( abs(fitTps2$fitted.values - fitTps1$fitted.values))
Here the default choice for the order is 2 and in two dimensions implies a linear polynomial. The arguments filled in by default are shown below
fitTps1$args
$cardinalX
[,1] [,2]
[1,] -85.289 40.981
[2,] -90.160 38.330
[3,] -91.229 43.812
attr(,"scaled:scale")
[1] 1 1
attr(,"scaled:center")
[1] 0 0$aRange
[1] NA
fitTps1$mKrig.args
[[1]]
NULL
$m
[1] 2
$collapseFixedEffect
[1] TRUE
$find.trA
[1] TRUE
cardinalX are the cardinal points chosen using a space
filling
criterion. These are attached to the covariance arguments list so
they are used
in the subsequent computations for this fit (such as predict,
predictSE, sim.spatialProcess).
In general, if d is the dimension of the locations and
m the order of the spline one must have 2*m-d >0.
The polynomial will have choose( m+d-1,d) terms and so
this many cardinal points need to be specified. As mentioned above
these are chosen in a reasonable way if spatialProcess is
used.
stationary.taper.cov
:The
resulting cross covariance is the direct or Shure product of the
tapering function and the covariance. In R code given location
matrices, x1 and x2 and using Euclidean distance.
Covariance(rdist( x1, x2)/aRange)*Taper( rdist( x1,
x2)/Taper.args$aRange)
By convention, the Taper function is assumed to be identically
zero outside the interval [0,1]. Some efficiency is introduced within
the function to search for pairs of locations that are nonzero with
respect to the Taper. This is done by the SPAM function
nearest.dist. This search may find more nonzero pairs than
dimensioned internally and SPAM will try to increase the space. One
can also reset the SPAM options to avoid these warnings. For
spam.format TRUE the multiplication with the C argument is done
with the spam sparse multiplication routines through the "overloading"
of the %*% operator.
This implements the nonstationary model developed by Chris Paciorek and Mark Schervish that allows for a varying range parameter over space and also a varying marginal variance. This is still experimental and spatialProcess has not been generalized to fit the parameter surfaces. It can, however, be used to evaluate the model at fixed parameter surfaces. See the last example below.
This covariance works by specifying a object aRangeObj such that the
generic call predict(aRangeObj, loc) will evaluate the aRange
function at the locations loc. This object can be as simple a
fit to local estimated aRange parameters using a fields function such
as Tps or spatialProcess. More specific applications one can create a
special predict function. For example suppose log aRange follows a
linear model in the spatial coordinates and these coefficients are the
vector Afit. Define a class and a predict function.
aRangeObj<- list(coef=Afit) class(aRangeObj)<- "myclass"
predict.myclass<- function( aRangeObj, x){ aRange<- exp(cbind( 1,x) %*% aRangeObj$coef) return( aRange) }
Now use spatialProcess with this object and make sure
predict.myclass is also loaded.
A similar strategy will also work for a varying marginal variance by creating sigmaObj and if needed a companion predict method.
About the FORTRAN: The actual function Exp.cov and
Rad.cov call FORTRAN to
make the evaluation more efficient this is especially important when the
C argument is supplied. So unfortunately the actual production code in
Exp.cov is not as crisp as the R code sketched above. See
Rad.simple.cov for a R coding of the radial basis functions.
Krig, rdist, rdist.earth, gauss.cov, Exp.image.cov, Exponential, Matern, Wendland.cov, mKrig
# exponential covariance matrix ( marginal variance =1) for the ozone
#locations
out<- Exp.cov( ChicagoO3$x, aRange=100)
# out is a 20X20 matrix
out2<- Exp.cov( ChicagoO3$x[6:20,],ChicagoO3$x[1:2,], aRange=100)
# out2 is 15X2 matrix
# Kriging fit where the nugget variance is found by GCV
# Matern covariance shape with range of 100.
#
fit<- Krig( ChicagoO3$x, ChicagoO3$y, Covariance="Matern", aRange=100,smoothness=2)
data( ozone2)
x<- ozone2$lon.lat
y<- ozone2$y[16,]
# Omit the NAs
good<- !is.na( y)
x<- x[good,]
y<- y[good]
# example of calling the taper version directly
# Note that default covariance is exponential and default taper is
# Wendland (k=2).
stationary.taper.cov( x[1:3,],x[1:10,] , aRange=1.5, Taper.args= list(k=2,aRange=2.0,
dimension=2) )-> temp
# temp is now a tapered 3X10 cross covariance matrix in sparse format.
is.spam( temp) # evaluates to TRUE
# should be identical to
# the direct matrix product
temp2<- Exp.cov( x[1:3,],x[1:10,], aRange=1.5) * Wendland(rdist(x[1:3,],x[1:10,]),
aRange= 2.0, k=2, dimension=2)
test.for.zero( as.matrix(temp), temp2)
# Testing that the "V" option works as advertized ...
x1<- x[1:20,]
x2<- x[1:10,]
V<- matrix( c(2,1,0,4), 2,2)
Vi<- solve( V)
u1<- t(Vi%*% t(x1))
u2<- t(Vi%*% t(x2))
look<- exp(-1*rdist(u1,u2))
look2<- stationary.cov( x1,x2, V= V)
test.for.zero( look, look2)
# Here is an example of how the cross covariance multiply works
# and lots of options on the arguments
Ctest<- rnorm(10)
temp<- stationary.cov( x,x[1:10,], C= Ctest,
Covariance= "Wendland",
k=2, dimension=2, aRange=1.5 )
# do multiply explicitly
temp2<- stationary.cov( x,x[1:10,],
Covariance= "Wendland",
k=2, dimension=2, aRange=1.5 )%*% Ctest
test.for.zero( temp, temp2)
# use the tapered stationary version
# cov.args is part of the argument list passed to stationary.taper.cov
# within Krig.
# This example needs the spam package.
#
if (FALSE) {
Krig(x,y, cov.function = "stationary.taper.cov", aRange=1.5,
cov.args= list(Taper.args= list(k=2, dimension=2,aRange=2.0) )
) -> out2
# NOTE: Wendland is the default taper here.
}
# BTW this is very similar to
if (FALSE) {
Krig(x,y, aRange= 1.5)-> out
}
##################################################
#### nonstationary covariance Paciorek.cov
##################################################
if (FALSE) {
M<- 20
gridList<- list(x=seq( 0,1,length.out=M),
y=seq( 0,1,length.out=M))
sGrid<- make.surface.grid(gridList )
# An aRange surface
aRangeObj<- list(coef= c( 1,4,0))
class(aRangeObj)<- "myclass"
predict.myclass<- function( aRangeObj, x){
aRange<- exp(cbind( 1,x) %*% aRangeObj$coef)
return( aRange)
}
covMatrix<- Paciorek.cov( sGrid, sGrid, aRangeObj=aRangeObj)
# examine correlation surface between selected locations and the full grid.
set.panel( 2,2)
{
imagePlot( as.surface(sGrid, covMatrix[,10]))
imagePlot( as.surface(sGrid, covMatrix[,205]))
imagePlot( as.surface(sGrid, covMatrix[,305]))
imagePlot( as.surface(sGrid, covMatrix[,390]))
}
# simulation of the field
set.seed(222)
n<- nrow( sGrid)
f<- t(chol(covMatrix)) %*% rnorm(M^2)
set.panel()
imagePlot( as.surface(sGrid,f))
y<- f + .05*rnorm(n)
fitP<- spatialProcess( sGrid, y, cov.function="Paciorek.cov",
cov.args= list(aRangeObj = aRangeObj ) )
# check estimated tau and sigma
fitP$summary
# fitted surface
surface( fitP)
}
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