| 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 can be used for spatial prediction.
fields stives to have readable and tutorial code. Take a look at the
source code for Krig and Krig.engine.default to see how
things work "under the hood".
Some major methods include:
Tps Thin Plate spline
regression (including GCV)
Krig Spatial process estimation
(Kriging) including support for conditional simulation.
The Krig function allow 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)
a fast Kriging routine, 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.
convenient functions for working with image data and rationally (well, maybe reasonably) placing a color scale on an image plot.
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. See also the manual/tutorial at
http://www.image.ucar.edu/Software/Fields
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")