Evaluates a a fitted model on a 2-D grid keeping any other variables constant.
The resulting object is suitable for use with functions for viewing 3-d
surfaces.

spatial

predict.surface

Fields is for curve, surface and function
 fitting with an emphasis on splines, spatial data and spatial
 statistics. The major methods include cubic, and thin plate splines,
 Kriging and compact covariances for large data sets. The splines and
 Kriging methods are supporting by functions that can determine the
 smoothing parameter (nugget and sill variance) by cross validation
 and also by restricted maximum likelihood.  A major feature is that
 any covariance function implemented in R  and following a simple fields
 format can be used for spatial prediction. Some tailored optimization
 functions are supplied for finding the MLEs for the Matern family of
 covariances.  There are also many useful functions for plotting and
 working with spatial data as images. This package also contains an
 implementation of sparse matrix methods for large spatial data sets and
 currently requires the sparse matrix (spam) package.
 But spam is not required for the standard
 spatial functions. Use help(fields) to get started and for an
 overview.  The fields source code is heavily commented and
 provides useful explanations of numerical details in addition to the
 manual pages.

predict.surface: This older surface function has been renamed as predictSurface

Description

Usage

Arguments

See Also