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,
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 stives to have readable and tutorial code. Take a look at the
source code for
mKrig to see how things work
"under the hood".
To load fields with the comments retained in the source
keep.source = TRUE in the
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.)
Tps Thin Plate spline
regression including GCV and REML estimates for the smoothing parameter.
spatialProcess An easy to use method that fits a spatial process model
( e.g. Kriging) but also estimates the key spatial parameters: nugget variance, sill variance and range by maximum likelihood. Default covariance model is a Matern covariance function.
Krig Spatial process estimation that is the core function of fields.
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.
mKrig (micro Krig) are
fast efficient Universal Kriging and spline-like functions, that can take advantage of sparse covariance
functions and thus handle very large numbers of spatial locations.
QTps A easy to use extension of thin plate splines for quantile and robust surface fitting.
mKrigMLEGrid for maximum likelihood estimates of covariance parameters. This function also
handles replicate fields assumed to be independent realizations at the same locations.
Other noteworthy functions
vgram.matrix find variograms for spatial data (and
with temporal replications.
cover.design Generates space-filling designs where the distance
function is expresed in R code.
convenient functions for working with image data and rationally (well,
maybe reasonably) creating and placing a color scale on an image plot.
grid.list for how fields works with grids and
world for adding a map quickly.
splint Fast 1-D smoothing
splines and interpolating cubic splines.
Generic functions that support the methods
plot - diagnostic plots of fit
summary- statistical summary of fit
surface- graphical display of fitted surface
predict- evaluation fit at arbitrary points
predictSE- prediction standard errors at arbitrary points.
sim.rf- Simulate a random fields on a 2-d grid.
Try some of the examples from help files for
gives some R code tricks for setting up common legends and axes.
And has little to do with this package!
help(fields.tests) for testing fields.
Some fields datasets
CO2 Global satelite CO2 concentrations (simulated field)
RCMexample Regional climate model output
lennon Image of John Lennon
COmonthlyMet Monthly mean temperatures and precip for Colorado
RMelevation Digital elevations for the Rocky Mountain Empire
ozone2 Daily max 8 hour ozone concentrations for the US midwest
for summer 1987.
PRISMelevation Digital elevations for the
continental US at approximately 4km resolution
NorthAmericanRainfall 50+ year average and trend for summer rainfall at
rat.diet Small paired study on rat food intake over time.
WorldBankCO2 Demographic and carbon emission data
for 75 countries and for 1999.
DISCLAIMER: The authors can not guarantee the correctness of any function or program in this package.
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# some air quality data, daily surface ozone measurements 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. # quick plot of predicted surface 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") fit2<- spatialProcess( x,y) # a "Kriging" model. The covariance defaults to a Matern with smoothness 1.0. # the nugget, sill and range parameters are found by maximum likelihood # summary, plot, and surface also work for fit2 !