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

### 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))

[Package RandomFields version 3.1.50 Index]