% fields, Tools for spatial data
% Copyright 2004-2007, Institute for Mathematics Applied Geosciences
% University Corporation for Atmospheric Research
% Licensed under the GPL -- www.gpl.org/licenses/gpl.html

\name{fields}
\alias{fields}
\title{
fields - tools for spatial data 
}
\description{
 Fields is a collection of programs for curve and function
 fitting with an emphasis on spatial data and spatial statistics. The
 major methods implemented include cubic and thin plate splines, 
 universal
 Kriging and Kriging for large data sets. One main feature is any
 covariance function implemented in R code can be used for spatial prediction. Another important feature is that fields will take advantage of compactly supported covariance functions in a seamless way through 
the spam package. See \code{library( help=fields)} for a listing of all the
fields contents. To load fields with the comments retained in the source
use \code{ keep.source = TRUE} in the \code{library} command. 

fields stives to have readable and tutorial code. Take a look at the 
source code for \code{Krig} and \code{mKrig} to see how things work 
"under the hood". 
To load fields with the comments retained in the source
use \code{ keep.source = TRUE} in the \code{library} command. 
We also keep the source on-line:
browse the directory 
\url{http://www.image.ucar.edu/~nychka/Fields/Source} for commented source. 
\url{http://www.image.ucar.edu/~nychka/Fields/Help/00Index.html} is a
page for html formatted help files. (If you obtain the source version of the
package (file ends in .gz) the commented source code is the R subdirectory.) 

Some major methods in fields include: 
\itemize{ \item \code{Tps} Thin Plate spline
regression (including GCV) 
\item \code{Krig} Spatial process estimation
(Kriging) including support for conditional simulation. 
}

The Krig function allows you to supply a covariance function that is
written in native R code.  See (\code{stationary.cov}) that includes
several families of covariances and distance metrics including the
Matern and great circle distance. Also check out \code{mKrig} (micro Krig) and
\code{fastTps}
a fast Kriging and spline-like functions, that can take advantage of sparse covariance
functions and thus handle very large numbers of spatial locations. 

Some other noteworthy functions are
\itemize{
\item \code{cover.design}  Gnerates space-filling designs where the distance 
function is expresed in R/S code

\item \code{as.image}, \code{image.plot}, \code{drape.plot}, \code{quilt.plot}
\code{add.image}, \code{crop.image}, \code{half.image}, \code{average.image} 
\code{designer.colors}.
 
convenient functions for working with image data and rationally (well,
maybe reasonably) creating and  placing a color scale on an image plot. See also 
\code{help(grid.list)} for how fields works with grids and \code{US}
and \code{world} for adding a map quickly. 

\item \code{sreg},  \code{qsreg} \code{splint}   Fast 1-D smoothing 
splines and 1-D 
quantile/robust and interpolating cubic splines.

}

There are also generic functions that support 
these methods such as 

\code{plot} - diagnostic plots of fit \cr
\code{summary}- statistical summary of fit \cr
\code{print}- shorter version of summary \cr
\code{surface}- graphical display of fitted surface \cr
\code{predict}- evaluation fit at arbitrary points \cr
\code{predict.se}- prediction standard errors at arbitrary points. \cr
\code{sim.rf}- Simulate a random fields on a 2-d grid.

To get started, try some of the examples from help files for \code{Tps} or 
\code{Krig}. 

Graphics tips:
\code{help( fields.hints)}
 gives some R code tricks for setting up common legends and axes. 
And has little to do with this package!

Testing:
See \code{help(fields.tests)} for testing fields. 

DISCLAIMER:

This is software for statistical research and not for commercial uses. The
authors do not guarantee the correctness of any function or program in
this package. Any changes to the software should not be made without the
authors permission.
}
\examples{

# some air quality data,daily surface ozone for the Midwest:
data(ozone2)
x<-ozone2$lon.lat
y<- ozone2$y[16,] # June 18, 1987

# pixel plot of spatial data
quilt.plot( x,y)
US( add=TRUE) # add US map

fit<- Tps(x,y)
# fits a GCV thin plate smoothing spline surface to ozone measurements.
# Hey, it does not get any easier than this!

summary(fit) #diagnostic summary of the fit 

set.panel(2,2)
plot(fit) # four diagnostic plots of  fit and residuals.


set.panel()
surface(fit) # contour/image plot of the fitted surface
US( add=TRUE, col="magenta", lwd=2) # US map overlaid
title("Daily max 8 hour ozone in PPB,  June 18th, 1987")

}
\keyword{datasets}