Sncf.srf: Nonparametric (Cross-)Covariance Function from stationary...
In ncf: Spatial Covariance Functions

Sncf.srf

R Documentation

Nonparametric (Cross-)Covariance Function from stationary random fields

Description

Sncf.srf is the function to estimate the nonparametric for spatio-temporal data from fully stationary random fields (i.e. marginal expectation and variance identical for all locations; use Sncf otherwise).

Usage

Sncf.srf(
  x,
  y,
  z,
  w = NULL,
  avg = NULL,
  avg2 = NULL,
  corr = TRUE,
  df = NULL,
  type = "boot",
  resamp = 0,
  npoints = 300,
  save = FALSE,
  filter = FALSE,
  fw = 0,
  max.it = 25,
  xmax = FALSE,
  jitter = FALSE,
  quiet = FALSE
)

Arguments

`x`	vector of length n representing the x coordinates (or longitude; see latlon).
`y`	vector of length n representing the y coordinates (or latitude).
`z`	matrix of dimension n x p representing p observation at each location.
`w`	an optional second matrix of dimension n x p for variable 2 (to estimate the spatial cross-correlation function).
`avg`	supplies the marginal expectation of the Markov random field; if TRUE, the sample mean (across the markovian field) is used.
`avg2`	optionally supplies the marginal expectation of the Markov random field for optional variable 2; if TRUE, the sample mean is used.
`corr`	If TRUE, the covariance function is standardized by the marginal variance (across the Markovian field) to return a correlation function (alternatively the covariance function is returned).
`df`	degrees of freedom for the spline. Default is sqrt(n).
`type`	takes the value "boot" (default) to generate a bootstrap distribution or "perm" to generate a null distribution for the estimator
`resamp`	the number of resamples for the bootstrap or the null distribution.
`npoints`	the number of points at which to save the value for the spline function (and confidence envelope / null distribution).
`save`	If TRUE, the whole matrix of output from the resampling is saved (an resamp x npoints dimensional matrix).
`filter`	If TRUE, the Fourier filter method of Hall and coworkers is applied to ensure positive semidefiniteness of the estimator. (more work may be needed on this.)
`fw`	If filter is TRUE, it may be useful to truncate the function at some distance w sets the truncation distance. When set to zero no truncation is done.
`max.it`	the maximum iteration for the Newton method used to estimate the intercepts.
`xmax`	If FALSE, the max observed in the data is used. Otherwise all distances greater than xmax is omitted.
`jitter`	If TRUE, jitters the distance matrix, to avoid problems associated with fitting the function to data on regular grids.
`quiet`	If TRUE, the counter is suppressed during execution.

Details

If corr = F, an object of class "Sncf.cov" is returned. Otherwise the class is "Sncf".

Sncf.srf is a function to estimate the nonparametric (cross-)covariance function (as discussed in Bjornstad and Bascompte 2001) for data from a fully stationary random fields. I have found it useful to estimate the (cross-)covariance functions in synthetic data.

Value

An object of class "Sncf" (or "Sncf.cov") is returned. See Sncf for details.

Author(s)

Ottar N. Bjornstad onb1@psu.edu

References

Bjornstad, O. N., and J. Bascompte. (2001) Synchrony and second order spatial correlation in host-parasitoid systems. Journal of Animal Ecology 70:924-933. <doi:10.1046/j.0021-8790.2001.00560.x>

Examples

# first generate some sample data
x <- expand.grid(1:20, 1:5)[, 1]
y <- expand.grid(1:20, 1:5)[, 2]

# z data from an exponential random field
z <- cbind(
  rmvn.spa(x = x, y = y, p = 2, method = "exp"), 
  rmvn.spa(x = x, y = y, p = 2, method = "exp")
  )

# w data from a gaussian random field
w <- cbind(
  rmvn.spa(x = x, y = y, p = 2, method = "gaus"), 
  rmvn.spa(x = x, y = y, p = 2, method = "gaus")
  )

# multivariate nonparametric covariance function
fit1 <- Sncf.srf(x = x, y = y, z = z, avg = NULL, corr = TRUE, resamp = 0) 
## Not run: plot(fit1) 
summary(fit1)

# multivariate nonparametric cross-covariance function (with known
# marginal expectation of zero for both z and w
fit2 <- Sncf.srf(x = x, y = y, z = z, w = w, avg = 0, avg2 = 0, corr = FALSE, 
                 resamp = 0)
## Not run: plot(fit2) 
summary(fit2)

ncf documentation built on May 7, 2022, 5:05 p.m.