mKrig: "micro Krig" Spatial process estimate of a curve or surface, "kriging" with a known covariance function.

Description

This is a simple version of the Krig function that is optimized for large data sets and a clear exposition of the computations. Lambda, the smoothing parameter must be fixed.

Usage

mKrig(x, y, weights = rep(1, nrow(x)),  Z=NULL,
  lambda = 0, cov.function = "stationary.cov", 
    m = 2, chol.args=NULL,cov.args=NULL, find.trA = TRUE, NtrA = 20,
                 iseed = 123, llambda=NULL,  ...)
## S3 method for class 'mKrig':
predict( object, xnew=NULL,ynew=NULL, derivative=0, Z=NULL, drop.Z=FALSE,just.fixed=FALSE, ...)
## S3 method for class 'mKrig':
summary(object, ...)
## S3 method for class 'mKrig':
print( x, digits=4,... )
 mKrig.coef(object, y)
mKrig.trace( object, iseed, NtrA)

Arguments

Value

dCoefficients of the polynomial fixed part and if present the covariates (Z).To determine which is which the logical vector ind.drift also part of this object is TRUE for the polynomial part.cCoefficients of the nonparametric part.ntDimension of fixed part.npDimension of c.nZNumber of columns of Z covariate matrix (can be zero).ind.driftLogical vector that indicates polynomial coefficients in the d coefficients vector. This is helpful to distguish between polynomial part and the extra covariates coefficients associated with Z.lambda.fixedThe fixed lambda valuexSpatial locations used for fitting.knotsThe same as xcov.function.nameName of covariance function used.argsA list with all the covariance arguments that were specified in the call.mOrder of fixed part polynomial.chol.argsA list with all the cholesky arguments that were specified in the call.callA copy of the call to mKrig.non.zero.entriesNumber of nonzero entries in the covariance matrix for the process at the observation locations.shat.MLEMLE of sigma.rho.MLEMLE or rho.rhohatEstimate for rho adjusted for fixed model degrees of freedom (ala REML).lnProfileLikelog Profile likelihood for lambdalnDetCovLog determinant of the covariance matrix for the observations having factored out rho.OmegaGLS covariance for the estimated parameters in the fixed part of the model (d coefficients0.qr.VT, McQR and cholesky matrix decompositions needed to recompute the estimate for new observation vectors.fitted.values, residualsUsual predictions from fit.eff.dfEstimate of effective degrees of freedom. Either the mean of the Monte Carlo sample or the exact value.trA.infoIf NtrA ids less than np then the individual members of the Monte Carlo sample and sd(trA.info)/ sqrt(NtrA) is an estimate of the standard error. If NtrA is greater than or equal to np then these are the diagonal elements of A(lamdba).GCVEstimated value of the GCV function.GCV.infoMonte Carlo sample of GCV functions

Details

This function is an abridged version of Krig that focuses on the computations in Krig.engine.fixed done for a fixed lambda parameter for unique spatial locations and for data without missing values. These restrictions simplify the code for reading. Note that also little checking is done and the spatial locations are not transformed before the estimation. Because most of the operations are linear algebra this code has been written to handle multiple data sets. Specifically if the spatial model is the same except for different observed values (the y's), one can pass y as a matrix and the computations are done efficiently for each set. Note that this is not a multivariate spatial model just an efficient computation over several data vectors without explicit looping.

predict.mKrig will evaluate the derivatives of the estimated function if derivatives are supported in the covariance function. For example the wendland.cov function supports derivatives.

print.mKrig is a simple summary function for the object.

mKrig.coef finds the "d" and "c" coefficients represent the solution using the previous cholesky decomposition for a new data vector. This is used in computing the prediction standard error in predict.se.mKrig and can also be used to evalute the estimate efficiently at new vectors of observations provided the locations and covariance remain fixed.

Sparse matrix methods are handled through overloading the usual linear algebra functions with sparse versions. But to take advantage of some additional options in the sparse methods the list argument chol.args is a device for changing some default values. The most important of these is nnzR, the number of nonzero elements anticipated in the Cholesky factorization of the postive definite linear system used to solve for the basis coefficients. The sparse of this system is essentially the same as the covariance matrix evalauted at the observed locations. As an example of resetting nzR to 450000 one would use the following argument for chol.args in mKrig:

chol.args=list(pivot=TRUE,memory=list(nnzR= 450000))

mKrig.trace This is an internal function called by mKrig to estimate the effective degrees of freedom. The Kriging surface estimate at the data locations is a linear function of the data and can be represented as A(lambda)y. The trace of A is one useful measure of the effective degrees of freedom used in the surface representation. In particular this figures into the GCV estimate of the smoothing parameter. It is computationally intensive to find the trace explicitly but there is a simple Monte Carlo estimate that is often very useful. If E is a vector of iid N(0,1) random variables then the trace of A is the expected value of t(E)AE. Note that AE is simply predicting a surface at the data location using the synthetic observation vector E. This is done for NtrA independent N(0,1) vectors and the mean and standard deviation are reported in the mKrig summary. Typically as the number of observations is increased this estimate becomse more accurate. If NtrA is as large as the number of observations (np) then the algorithm switches to finding the trace exactly based on applying A to np unit vectors.

Examples

Run this code

#
# Midwest ozone data  'day 16' stripped of missings 
data( ozone2)
y<- ozone2$y[16,]
good<- !is.na( y)
y<-y[good]
x<- ozone2$lon.lat[good,]

# nearly interpolate using defaults (Exponential)
mKrig( x,y, theta = 2.0, lambda=.01)-> out
#
# NOTE this should be identical to 
# Krig( x,y, theta=2.0, lambda=.01) 
# interpolate using tapered version of the exponential, 
# the taper scale is set to 1.5 default taper covariance is the Wendland.
# Tapering will done at a scale of 1.5 relative to the scaling 
# done through the theta  passed to the covariance function.


mKrig( x,y,cov.function="stationary.taper.cov",
       theta = 2.0, lambda=.01,
     Taper="Wendland",  Taper.args=list(theta = 1.5, k=2, dimension=2)
           ) -> out2

predict.surface( out2)-> out.p
surface( out.p)

# Try out GCV on a grid of lambda's.
# For this small data set 
# one should really just use Krig or Tps but this is an example of
# approximate GCV that will work for much larger data sets using sparse 
# covariances and the Monte Carlo trace estimate
#
# a grid of lambdas:
lgrid<- 10**seq(-1,1,,15) 

GCV<- matrix( NA, 15,20)
trA<-  matrix( NA, 15,20)
GCV.est<- rep( NA, 15)
eff.df<- rep( NA, 15)
logPL<- rep( NA, 15) 
# loop over lambda's
for (  k in 1:15){
out<- mKrig( x,y,cov.function="stationary.taper.cov",
       theta = 2.0, lambda=lgrid[k],
     Taper="Wendland",  Taper.args=list(theta = 1.5, k=2, dimension=2)  ) 

GCV[k,]<- out$GCV.info
trA[k,]<- out$trA.info
eff.df[k]<- out$eff.df
GCV.est[k]<- out$GCV
logPL[k]<- out$lnProfileLike
}
#
# plot the  results different curves are for individual estimates  
# the two lines are whether one averages first the traces or the GCV criterion.
#
par( mar=c(5,4,4,6))
matplot( trA, GCV, type="l", col=1, lty=2, xlab="effective degrees of freedom", ylab="GCV")
lines( eff.df, GCV.est, lwd=2, col=2)
lines( eff.df, rowMeans(GCV), lwd=2)

# add exact GCV computed by Krig 
out0<-  Krig( x,y,cov.function="stationary.taper.cov",
       theta = 2.0, 
     Taper="Wendland",  Taper.args=list(theta = 1.5, k=2, dimension=2), spam.format=FALSE)  
lines( out0$gcv.grid[,2:3], lwd=4, col="darkgreen")

# add profile likelihood 
utemp<- par()$usr
utemp[3:4] <- range( -logPL)
par( usr=utemp)
lines( eff.df, -logPL, lwd=2, col="blue", lty=2)
axis( 4)
mtext( side=4,line=3, "-ln profile likelihood", col="blue")
title( "GCV ( green = exact) and  -ln profile likelihood", cex=2)

# an example using a "Z" covariate and the Matern family

data(COmonthlyMet)
y<- CO.tmin.MAM.climate
good<- !is.na( y)
y<-y[good]
x<- CO.loc[good,]
Z<- CO.elev[good]
out<- mKrig( x,y, Z=Z,  cov.function="stationary.cov", Covariance="Matern",
                    theta=4.0, smoothness=1.0, lambda=.1)
set.panel(2,1)
# quilt.plot with elevations
quilt.plot( x, predict(out))
# quilt.plot without elevation linear term included
quilt.plot( x, predict(out, drop.Z=TRUE))
set.panel()

# here is a series of examples with a bigger problem 
# using a compactly supported covariance directly

set.seed( 334)
N<- 1000
x<- matrix( 2*(runif(2*N)-.5),ncol=2)
y<- sin( 1.8*pi*x[,1])*sin( 2.5*pi*x[,2]) + rnorm( 1000)*.1
  
look2<-mKrig( x,y, cov.function="wendland.cov",k=2, theta=.2, 
            lambda=.1)

# take a look at fitted surface
predict.surface(look2)-> out.p
surface( out.p)

# this works because the number of nonzero elements within distance theta
# are less than the default maximum allocated size of the 
# sparse covariance matrix. 
#  see  spam.options() for the default values 

# The following will give a warning for theta=.9 because 
# allocation for the  covariance matirx storage is too small. 
# Here theta controls the support of the covariance and so 
# indirectly the  number of nonzero elements in the sparse matrix

mKrig( x,y, cov.function="wendland.cov",k=2, theta=.9, lambda=.1)-> look2

# The warning resets the memory allocation  for the covariance matirx according the 
# values   'spam.options(nearestdistnnz=c(416052,400))'
# this is inefficient becuase the preliminary pass failed. 

# the following call completes the computation in "one pass"
# without a warning and without having to reallocate more memory. 

spam.options(nearestdistnnz=c(416052,400))
mKrig( x,y, cov.function="wendland.cov",k=2, theta=.9, lambda=1e-2)-> look2

# as a check notice that 
#   print( look2)
# report the number of nonzero elements consistent with the specifc allocation
# increase in spam.options


# new data set of 1500 locations
set.seed( 234)
N<- 1500
x<- matrix( 2*(runif(2*N)-.5),ncol=2)
y<- sin( 1.8*pi*x[,1])*sin( 2.5*pi*x[,2]) + rnorm( N)*.01
  
# the following is an example of where the allocation  (for nnzR) 
# for the cholesky factor is too small. A warning is issued and 
# the allocation is increased by 25% in this example
#
mKrig( x,y, 
            cov.function="wendland.cov",k=2, theta=.1, 
            lambda=1e2  )-> look2
# to avoid the warning 
 mKrig( x,y, 
            cov.function="wendland.cov",k=2, theta=.1, 
            lambda=1e2,
            chol.args=list(pivot=TRUE,memory=list(nnzR= 450000))  )-> look2

# success!

# fiting multiple data sets
#
#\dontrun{ 
  y1<- sin( 1.8*pi*x[,1])*sin( 2.5*pi*x[,2]) + rnorm( N)*.01
  y2<- sin( 1.8*pi*x[,1])*sin( 2.5*pi*x[,2]) + rnorm( N)*.01
  Y<- cbind(y1,y2)

  mKrig( x,Y,cov.function="wendland.cov",k=2, theta=.1, 
            lambda=1e2  )-> look3
# note slight difference in summary because two data sets have been fit.
  print( look3)
#}

##################################################
# finding a good choice for theta as a taper 
##################################################
# Suppose the target is a spatial prediction using roughly 50 nearest neighbors
# (tapering covariances is effective for roughly 20 or more in the situation of 
#  interpolation) see Furrer, Genton and Nychka (2006).

# take a look at a random set of 100 points to get idea of scale

set.seed(223)
 ind<- sample( 1:N,100)
 hold<- rdist( x[ind,], x)
 dd<- (apply( hold, 1, sort))[65,]
 dguess<- max(dd)
# dguess is now a reasonable guess at finding cutoff distance for
# 50 or so neighbors

# full distance matrix excluding distances greater than dguess
# but omit the diagonal elements -- we know these are zero!

 hold<- nearest.dist( x, delta= dguess,upper=TRUE)
# exploit spam format to get quick of number of nonzero elements in each row

 hold2<-  diff( hold@rowpointers)
 #  min( hold2) = 55   which we declare close enough 

# now the following will use no less than 55 nearest neighbors 
# due to the tapering. 

mKrig( x,y, cov.function="wendland.cov",k=2, theta=dguess, 
            lambda=1e2) ->  look2