predict.Krig {fields}  R Documentation 
Provides predictions from the Krig spatial process estimate at arbitrary points, new data (Y) or other values of the smoothing parameter (lambda) including a GCV estimate.
## S3 method for class 'Krig' predict( object, x = NULL, Z = NULL, drop.Z = FALSE, just.fixed = FALSE, lambda = NA, df = NA, model = NA, eval.correlation.model = TRUE, y = NULL, yM = NULL, verbose = FALSE, ...) predictDerivative.Krig(object, x = NULL, verbose = FALSE,...) ## S3 method for class 'Tps' predict(object, ... ) ## S3 method for class 'fastTps' predict(object, xnew = NULL, grid.list = NULL, ynew = NULL, derivative = 0, Z = NULL, drop.Z = FALSE, just.fixed = FALSE, xy = c(1, 2), ...)
derivative 
The degree of the derivative to be evauated. Default is 0 (evaluate the function itself), 1 is supported by some covariance functions, Higher derivatives are not supported in this version and for mKrig. 
df 
Effective degrees of freedom for the predicted surface. This can be used in place of lambda ( see the function Krig.df.to.lambda) 
eval.correlation.model 
If true ( the default) will multiply the predicted function by marginal sd's and add the mean function. This usually what one wants. If false will return predicted surface in the standardized scale. The main use of this option is a call from Krig to find MLE's of rho and sigma2 
grid.list 
A 
just.fixed 
Only fixed part of model is evaluated 
lambda 
Smoothing parameter. If omitted, out\$lambda will be used. (See also df and gcv arguments) 
model 
Generic argument that may be used to pass a different lambda. 
object 
Fit object from the Krig, Tps, mKrig, or fastTps functions. 
verbose 
Print out all kinds of intermediate stuff for debugging 
xy 
The column positions that locate the x and y variables for evaluating on a grid. This is mainly useful if the surface has more than 2 dimensions. 
y 
Evaluate the estimate using the new data vector y (in the same order as the old data). This is equivalent to recomputing the Krig object with this new data but is more efficient because many pieces can be reused. Note that the x values are assumed to be the same. 
x 
Matrix of x values on which to evaluate the kriging surface. If omitted, the data x values, i.e. out\$x will be used. 
xnew 
Same as x above. 
ynew 
Same as y above. 
yM 
If not NULL evaluate the estimate using this vector as the replicate mean data. That is, assume the full data has been collapsed into replicate means in the same order as xM. The replicate weights are assumed to be the same as the original data. (weightsM) 
Z 
Vector/Matrix of additional covariates to be included in fixed part of spatial model 
drop.Z 
If TRUE only spatial fixed part of model is evaluated. i.e. Z covariates are not used. 
... 
Other arguments passed to covariance function. In the case of

The main goal in this function is to reuse the Krig object to rapidly
evaluate different estimates. Thus there is flexibility in changing the
value of lambda and also the independent data without having to
recompute the matrices associated with the Krig object. The reason this
is possible is that most on the calculations depend on the observed
locations not on lambda or the observed data. Note the version for
evaluating partial derivatives does not provide the same flexibility as
predict.Krig
and makes some assumptions about the null model
(as a low order polynomial) and can not handle the correlation model form.
Vector of predicted responses or a matrix of the partial derivatives.
Krig, predictSurface gcv.Krig
Krig(ChicagoO3$x,ChicagoO3$y, theta=50) >fit predict( fit) # gives predicted values at data points should agree with fitted.values # in fit object # predict at the coordinate (5,10) x0< cbind( 5,10) # has to be a 1X2 matrix predict( fit,x= x0) # redoing predictions at data locations: predict( fit, x=ChicagoO3$x) # only the fixed part of the model predict( fit, just.fixed=TRUE) # evaluating estimate at a grid of points grid< make.surface.grid( list( seq( 40,40,,15), seq( 40,40,,15))) look< predict(fit,grid) # evaluate on a grid of points # some useful graphing functions for these gridded predicted values out.p< as.surface( grid, look) # reformat into $x $y $z imagetype object contour( out.p) # see also the functions predictSurface and surface # for functions that combine these steps # refit with 10 degrees of freedom in surface look< predict(fit,grid, df=15) # refit with random data look< predict( fit, grid, y= rnorm( 20)) # finding partial derivatives of the estimate # # find the partial derivatives at observation locations # returned object is a two column matrix. # this does not make sense for the exponential covariance # but can illustrate this with a thin plate spline with # a high enough order ( i.e. need m=3 or greater) # data(ozone2) # the 16th day of this ozone spatial dataset fit0< Tps( ozone2$lon.lat, ozone2$y[16,], m=3) look1< predictDerivative.Krig( fit0) # for extra credit compare this to look2< predictDerivative.Krig( fit0, x=ozone2$lon.lat) # (why are there more values in look2)