fields {fields} | R Documentation

## 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 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.

### 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")

[Package fields version 5.02 Index]