print.Krig

<p>Number of significant digits in printed output. Default is 4.</p>

digits

&#8230;

<p>Prints the results from a fitting a spatial process estimate (Krig)</p>

spatial

For curve, surface and function fitting with an emphasis
on splines, spatial data, geostatistics,  and spatial statistics. The major methods
include cubic, and thin plate splines, Kriging, and compactly supported
covariance functions 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 function 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 parameter).  A major feature is that any covariance function
implemented in R and following a simple 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 as a companion to the manual pages. The commented
source code can be viewed by expanding the source code version
and looking in the R subdirectory. The reference for fields can be generated
by the citation function in R and has DOI <doi:10.5065/D6W957CT>. Development
of this package was supported in part by the National Science Foundation  Grant
1417857,  the National Center for Atmospheric Research, and Colorado School of Mines.
See the Fields URL
for a vignette on using this package and some background on spatial statistics.

print.Krig: Print kriging fit results.

Description

Usage

Arguments

Value

See Also

Examples