predict.Krig {fields} | R Documentation

## 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

    ## 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, ...)
    predict.derivative.Krig (object, x = NULL,  verbose = FALSE,...)

### Arguments

`object` | Fit object from the Krig or Tps function.
`x` | Matrix of x values on which to evaluate the kriging surface. If omitted, the data x values, i.e. out$x will be used.
`Z` | Vector/Matrix of additional covariates to be included in fixed part of spatial model
`drop.Z` | If TRUE only spatial fixed part of model is evaluated. i.e. Z covariates are not used.
`just.fixed` | Only fixed part of model is evaluated
`lambda` | Smoothing parameter. If omitted, out$lambda will be used. (See also df and gcv arguments)
`df` | 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 MLE's of rho and sigma2
`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.
`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)
`verbose` | Print out all kinds of intermediate stuff for debugging
`...` | Other arguments passed to predict.

### 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. 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.

### Value

Vector of predicted responses or a matrix of the partial derivatives.

### See Also

Krig, predict.surface gcv.Krig

### Examples

    Krig(ozone$x,ozone$y, theta=50) ->fit
    predict( fit) # gives predicted values at data points

    # only the fixed part of the model

    predict( fit, just.fixed=TRUE)

    # in this case the default is a linear spatial drift  (m=2) and there
    # are no additional covariates

    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)

    # refit with random data

    look<- predict( fit, grid, y= rnorm( 20))

[Package fields version 6.01 Index]