predict.Krig: Evaluation of Krig spatial process estimate.

Description

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.

Usage

predict.Krig
(out, x = NULL, lambda = NA, df = NA, model = NA,
        eval.correlation.model = TRUE, y = NULL, verbose = FALSE, gcv = FALSE)

Arguments

out

Fit object from the Krig or Tps function.

Matrix of x values on which to evaluate the kriging surface. If omitted, the data x values, i.e. out$x will be used.

lambda

Smoothing parameter. If omitted, out$lambda will be used. (See also df and gcv arguments)

Effective degrees of freedom for the predicted surface. This can be used in place of lambda ( see the function Krig.df.to.lambda)

model

Generic argument that may be used to pass a different 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

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 t

verbose

Print out all kinds of intermediate stuff for debugging

gcv

Default is false. Find lambda from the y vector using GCV. See the returned values for a side effect of setting equal to true.

Value

Vector of predicted responses if gcv=F ( the default) BUT NOTE: If lambda is found by gcv then the returned values is a list with the first component a vector of predictions and the second the estimated value of lambda.

Details

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.

Examples

Run this code

Krig(ozone$x,ozone$y,exp.cov, theta=50) ->fit
predict( fit) # gives predicted values at data 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
out.p<- as.surface( grid, look) # reformat into $x $y $z image-type object
contour( out.p)  


# refit with 10 degrees of freedom in surface

look<- predict(fit,grid, df=15)

# re fit with random data and lambda found by GCV
look<- predict( fit, grid, y= rnorm( 20), gcv=TRUE)

# NOTE: look is a list now  look$predicted  predicted values and look$lambda
# the value of lambda

out.p<-as.surface( grid, look$predicted) 
contour( out.p)