RandomFields (version 3.1.36)

RMcovariate: Model for covariates

Description

The model makes covariates available.

Usage

RMcovariate(c, x, y=NULL, z=NULL, T=NULL, grid, var, scale, Aniso, proj,  raw, norm, addNA, factor)

Arguments

scale, Aniso, proj, var

optional arguments; same meaning for any RMmodel. If not passed, the above covariance function remains unmodified.

c

vector or matrix of data

x,y,z,T,grid

optional. The usual arguments as in RFsimulate to define the locations where the covariates are given

raw

logical. If FALSE then the data are interpolated. This approach is always save, but might be slow.

If TRUE then the data may be accessed when covariance matrices are calculated. No rescaling or anisotropy definition is allowed in combination with the model. The use is dangerous, but fast.

Default: FALSE

norm

optional model that gives the norm between locations

addNA

If addNA is TRUE, then an additional (linear) factor is estimated in an estimation framework. This parameter must be set in particular when RMcovariate passes several covariates.

factor

real value. From users point of view very much the same as setting the argument var

Value

RMcovariate returns an object of class RMmodel

Details

The functions interpolates (nearest neighbour) between the values.

See Also

RMfixcov, RMmodel, RMtrend

Examples

RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
##                   RFoptions(seed=NA) to make them all random again

z <- 0.2 + (1:10)
RFfctn(RMcovariate(z), 1:10)
RFfctn(RMcovariate(z, 1:10), c(2, 2.1, 2.5, 3))