print.Krig


Number of significant digits in printed output. Default is 4. 


digits


Prints the results from a fitting a spatial process estimate (Krig)


spatial

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
supported by functions that can determine the smoothing parameter
(nugget and sill variance) and other covariance parameters by cross
validation and also by restricted maximum likelihood. For Kriging
there is an easy to use function that also estimates the correlation
scale (range).  A major feature is that any covariance function
implemented in R and following a simple fields format can be used for
spatial prediction. 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. Use
help(fields) to get started and for an overview.  The fields source
code is deliberately commented and provides useful explanations of
numerical details in addition to the manual pages. The commented
source code can be viewed by expanding the source code file (ending in tar.gz)
and looking in the R subdirectory. Please cite fields along with its
DOI in your publications!

print.Krig: Print kriging fit results.

Description

Usage

Arguments

Value

See Also

Examples