exp.cov: Exponential family, radial basis functions,cubic spline,...

In fields: Tools for Spatial Data

Exponential family, radial basis functions,cubic spline, compactly supported Wendland family, stationary covariances and non-stationary covariances.

Description

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.

Usage

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)             

Arguments

x1	Matrix of first set of locations where each row gives the coordinates of a particular point.
x2	Matrix of second set of locations where each row gives the coordinatesof a particular point. If this is missing x1 is used.
aRange	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.
aRangeObj	A fit object that defines the Range (or scale) parameter for arbitray locations using the generic predict function.
theta	Old version of the aRange parameter. If passed will be copied to aRange.
cardinalX	Locations added to the thin plate plate radial function to make it positive definite (See Details below).
C	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.
Covariance	Character string that is the name of the covariance shape function for the distance between locations. Choices in fields are Exponential, Matern
derivative	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.
Distance	Character string that is the name of the distance function to use. Choices in fields are rdist, rdist.earth
distMat	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.
Dist.args	A list of optional arguments to pass to the Distance function.
k	The order of the Wendland covariance function. See help on Wendland.
marginal	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.
m	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).
onlyUpper	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.
p	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.
rhoObj	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.
smoothness	For the Matern family the smoothnes of the process (aka "nu" in formulas).
spam.format	If TRUE returns matrix in sparse matrix format implemented in the spam package. If FALSE just returns a full matrix.
Taper	Character string that is the name of the taper function to use. Choices in fields are listed in help(taper).
Taper.args	A list of optional arguments to pass to the Taper function. aRange should always be the name for the range (or scale) paremeter.
V	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.
verbose	If TRUE prints out some useful information for debugging.
with.constant	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.
with.log	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.

Details

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 covariance 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).

Radial basis functions (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.

Tps.cov

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 tapered covariance 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.

Nonstationary covariance function, Paciorek.cov

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.

Value

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.

See Also

Krig, rdist, rdist.earth, gauss.cov, Exp.image.cov, Exponential, Matern, Wendland.cov, mKrig

Examples

# 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.
# 

## Not run: 

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. 

## End(Not run)

# BTW  this is very similar to 
## Not run: 
 Krig(x,y, aRange= 1.5)-> out

## End(Not run)

##################################################
#### nonstationary covariance Paciorek.cov
##################################################
## Not run: 
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)


## End(Not run)

fields documentation built on June 28, 2024, 1:06 a.m.