fields                package:fields                R Documentation

_f_i_e_l_d_s - _t_o_o_l_s _f_o_r _s_p_a_t_i_a_l _d_a_t_a

_D_e_s_c_r_i_p_t_i_o_n:

     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: 

        *  '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  <URL:
     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.

_E_x_a_m_p_l_e_s:

     # 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")

