R: Model for covariates

RMcovariate {RandomFields}

R Documentation

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`

Details

The functions interpolates (nearest neighbour) between the values.

Value

RMcovariate returns an object of class RMmodel

Note

c, x, also accept lists of data. However, its use is not in an advanced stage yet.

Author(s)

Martin Schlather, schlather@math.uni-mannheim.de

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))

[Package RandomFields version 3.1.50 Index]