gstat                 package:gstat                 R Documentation

_C_r_e_a_t_e _g_s_t_a_t _o_b_j_e_c_t_s, _o_r _s_u_b_s_e_t _i_t

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

     Function that creates gstat objects; objects that hold all the
     information necessary for univariate or multivariate
     geostatistical prediction (simple, ordinary or universal
     (co)kriging), or its conditional or unconditional Gaussian or
     indicator simulation equivalents. Multivariate gstat object can be
     subsetted.

_U_s_a_g_e:

     gstat(g, id, formula, locations, data, model = NULL, beta, nmax = Inf,
             nmin = 0, maxdist = Inf, dummy = FALSE, set, fill.all = FALSE, 
             fill.cross = TRUE, variance = "identity", weights = NULL, merge, 
             degree = 0)
     print.gstat(x, ...)

_A_r_g_u_m_e_n_t_s:

       g: gstat object to append to; if missing, a new gstat object is
          created 

      id: identifier of new variable; if missing, 'varn' is used with
          'n' the number for this variable. If a cross variogram is
          entered, 'id' should be a vector with the two 'id' values ,
          e.g.  'c("zn", "cd")', further only supplying arguments 'g' 
          and 'model'. Note that it is not advisable to use
          expressions, such as 'log(zinc)', as identifiers 

 formula: formula that defines the dependent variable as a linear model
          of independent variables; suppose the dependent variable has
          name 'z', for ordinary and simple kriging use the formula
          'z~1'; for simple kriging also define 'beta' (see below); for
          universal kriging, suppose 'z' is linearly dependent on 'x'
          and 'y', use the formula 'z~x+y'

locations: formula with only independent variables that define the
          spatial data locations (coordinates), e.g. '~x+y'; if 'data'
          is of class 'spatial.data.frame', this argument may be
          ignored, as  it can be derived from the data 

    data: data frame; contains the dependent variable, independent
          variables, and locations. 

   model: variogram model for this 'id'; defined by a call to  vgm; see
          argument 'id' to see how cross variograms are entered 

    beta: only for simple kriging (and simulation based on simple
          kriging); vector with the trend coefficients (including
          intercept); if no independent variables are defined the model
          only contains an intercept and this should be the simple
          kriging mean 

    nmax: for local kriging: the number of nearest observations that
          should be used for a kriging prediction or simulation, where
          nearest is defined in terms of the space of the spatial
          locations 

    nmin: for local kriging: if the number of nearest observations
          within distance 'maxdist' is less than 'nmin', a missing 
          value will be generated; see maxdist 

 maxdist: for local kriging: only observations within a distance of
          'maxdist' from the prediction location are used for
          prediction or simulation; if combined with 'nmax', both
          criteria apply 

   dummy: logical; if TRUE, consider this data as a dummy variable
          (only necessary for unconditional simulation) 

     set: named list with optional parameters to be passed to gstat
          (only 'set' commands of gstat are allowed, and not all of
          them may be relevant; see the manual for gstat stand-alone,
          URL below ) 

       x: gstat object to print 

fill.all: logical; if TRUE, fill all of the direct variogram and,
          depending on the value of 'fill.cross' also all cross
          variogram model slots in 'g' with the given variogram model 

fill.cross: logical; if TRUE, fill all of the cross variograms, if
          FALSE fill only all direct variogram model slots in 'g' with
          the  given variogram model (only if 'fill.all' is used)

variance: character; variance function to transform to non-stationary
          covariances; "identity" does not transform, other options are
          "mu" (Poisson) and "mu(1-mu)" (binomial) 

 weights: numeric vector; if present, covariates are present, and
          variograms are missing weights are passed to OLS prediction
          routines; if variograms are given, weights should be
          1/variance, where variance  specifies location-specific
          measurement error as in Delhomme, J.P. Kriging in the
          hydrosciences.  Advances in Water Resources, 1(5):251-266,
          1978; see also the section Kriging with known measurement
          errors in the gstat user's manual, URL see below.  

   merge: either character vector of length 2, indicating two ids  that
          share a common mean; the more general gstat merging of any
          two coefficients across variables is obtained when a list is
          passed, with each element a character vector of length 4, in
          the form  'c("id1", 1,"id2", 2)'. This merges the first
          parameter  for variable 'id1' to the second of variable
          'id2'.

  degree: order of trend surface in the location, between 0 and 3

     ...: arguments that are passed to the printing of variogram models
          only

_D_e_t_a_i_l_s:

     to print the full contents of the object 'g' returned, use
     'as.list(g)' or 'print.default(g)'

_V_a_l_u_e:

     an object of class 'gstat', which inherits from 'list'. Its
     components are:  

    data: list; each element is a list with the 'formula', 
          'locations', 'data', 'nvars', 'beta', etc., for a  variable

   model: list; each element contains a variogram model; names are
          those of the elements of 'data'; cross variograms have names
          of the pairs of data elements, separated by a '.' (e.g.: 
          'var1.var2'

     set: list; named list, corresponding to set 'name'='value'; gstat
          commands (look up the set command in the gstat manual for a
          full list)

_N_o_t_e:

     The function currently copies the data objects into the gstat
     object, so this may become a large object. I would like to copy
     only the name of the data frame, but could not get this to work.
     Any help is appreciated.  

     Subsetting (see examples) is done using the 'id''s of the
     variables, or using numeric subsets. Subsetted gstat objects only
     contain cross variograms if (i) the original gstat object
     contained them and (ii) the order of the subset indexes increases,
     numerically, or given the order they have in the gstat object.

     The merge item may seem obscure. Still, for colocated cokriging,
     it is needed. See texts by Goovaerts, Wackernagel, Chiles and
     Delfiner, or look for standardised ordinary kriging in the 1992
     Deutsch and Journel or Isaaks and Srivastava. In these cases, two
     variables share a common mean parameter. Gstat generalises this
     case: any two variables may share any of the regression
     coefficients; allowing for instance analysis of covariance models,
     when variograms left out (see e.g. R. Christensen's ``Plane
     answers'' book on linear models. The tests directory of the
     package contains examples in file merge.R.

_A_u_t_h_o_r(_s):

     Edzer J. Pebesma

_R_e_f_e_r_e_n_c_e_s:

     <URL: http://www.gstat.org/> 

     Pebesma, E.J., 2004. Multivariable geostatistics in S: the gstat
     package. Computers & Geosciences, 30: 683-691.

_S_e_e _A_l_s_o:

     predict.gstat, krige

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

     data(meuse)
     # let's do some manual fitting of two direct variograms and a cross variogram
     g <- gstat(id = "ln.zinc", formula = log(zinc)~1, locations = ~x+y, 
             data = meuse)
     g <- gstat(g, id = "ln.lead", formula = log(lead)~1, locations = ~x+y, 
             data = meuse)
     # examine variograms and cross variogram:
     plot(variogram(g))
     # enter direct variograms:
     g <- gstat(g, id = "ln.zinc", model = vgm(.55, "Sph", 900, .05))
     g <- gstat(g, id = "ln.lead", model = vgm(.55, "Sph", 900, .05))
     # enter cross variogram:
     g <- gstat(g, id = c("ln.zinc", "ln.lead"), model = vgm(.47, "Sph", 900, .03))
     # examine fit:
     plot(variogram(g), model = g$model, main = "models fitted by eye")
     # see also demo(cokriging) for a more efficient approach
     g["ln.zinc"]
     g["ln.lead"]
     g[c("ln.zinc", "ln.lead")]
     g[1]
     g[2]

     # Inverse distance interpolation with inverse distance power set to .5:
     # (kriging variants need a variogram model to be specified)
     data(meuse)
     data(meuse.grid)
     meuse.gstat <- gstat(id = "zinc", formula = zinc ~ 1, locations = ~ x + y,
             data = meuse, nmax = 7, set = list(idp = .5))
     meuse.gstat
     z <- predict(meuse.gstat, meuse.grid)
     levelplot(zinc.pred~x+y, z, aspect = mapasp(z))
     # see demo(cokriging) and demo(examples) for further examples, 
     # and the manuals for predict.gstat and image

