fields {fields} | R Documentation |
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 library( help=fields)
for a listing of all the
fields contents. To load fields with the comments retained in the source
use keep.source = TRUE
in the library
command.
fields stives to have readable and tutorial code. Take a look at the
source code for Krig
and mKrig
to see how things work
"under the hood".
To load fields with the comments retained in the source
use keep.source = TRUE
in the library
command.
We also keep the source on-line:
browse the directory
http://www.image.ucar.edu/~nychka/Fields/Source for commented source.
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:
Tps
Thin Plate spline
regression (including GCV)
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 (stationary.cov
) that includes
several families of covariances and distance metrics including the
Matern and great circle distance. Also check out mKrig
(micro Krig) and
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
cover.design
Gnerates space-filling designs where the distance
function is expresed in R/S code
as.image
, image.plot
, drape.plot
, quilt.plot
add.image
, crop.image
, half.image
, average.image
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
help(grid.list)
for how fields works with grids and US
and world
for adding a map quickly.
sreg
, qsreg
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
plot
- diagnostic plots of fit
summary
- statistical summary of fit
print
- shorter version of summary
surface
- graphical display of fitted surface
predict
- evaluation fit at arbitrary points
predict.se
- prediction standard errors at arbitrary points.
sim.rf
- Simulate a random fields on a 2-d grid.
To get started, try some of the examples from help files for Tps
or
Krig
.
Graphics tips:
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 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.
# 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")