Description Usage Arguments Details Value Note Author(s) References Examples

A stationary tool for simulating a surface (as a matrix of values) with specified spatial autocorrelation parameters. The simulation return *2^level*2^level* real-valued surface (e.g., landscape) based on the FFT algorithm and the spectral (or autocorrelation theorem using first and second order neighbours in N-S, E-W, NW-SE, and NE-SW directions.

1 | ```
CARsimu(level = 5, row1 = 0.2499999, row2 = 0, col1 = 0.2499999, col2 = 0, rc1 = 0, cr1 = 0)
``` |

`level` |
Integer: controls the dimensions of the output surface ( |

`row1` |
First order neighbour East-West autocorrleation parameter |

`row2` |
Second order neighbour East-West autocorrelation parameter |

`col1` |
First order neighbour North-South autocorrelation parameter |

`col2` |
Second order neighbour North-South autocorrelation parameter |

`rc1` |
First order neighbour NW-SE autocorrelation parameter |

`cr1` |
First order neighbour NE-SW autocorrelation parameter |

The sum of all six autocorrelation parameters MUST be less than 0.5. Their effect is highly non-linear, thus there is a marked difference beetween 0.499 and 0.499999. This implementation corresponds to the homogeneous (stationary) conditional autoregressive (CAR) model.

Returns a *2^level*2^level* matrix of real numbers. It is possible to use the image() or imaks() functions to graphically display the surface. Saving the output from this function into a new object is likely the most desireable usage. Ths surface can then be factored to produce binary maps with specified proportions as in Remmel and Csillag (2003).

This function was originally called ujabki() - some earlier versions may be available elsewhere under this name.

Sandor Kabos, Ferenc (Ferko) Csillag, and Tarmo Remmel

Remmel, T.K. and F. Csillag. 2003. When are two landscape pattern indices significantly different? Journal of Geographical Systems 5(4):331-351.

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