PatternClass (version 2.0.1)

CARsimu: This function simulates an image using a FFT implementation of a Conditional Autoregressive (CAR) model.

Description

A CAR landscape simulator for isotropic conditions.

Usage

CARsimu(LEVEL = 6, rho = 0.2499, row2 = 0, col2 = 0, rc1 = 0, cr1 = 0, 
maindi = 1, rajz = TRUE)

Arguments

LEVEL

This is the power (n) with base 2 that defines the image size: 2^n x 2^n.

rho

This is the spatial autocorrelation parameter.

row2

For later implementation - 2nd order neighbour spatial autocorrelation parameter (rows).

col2

For later implementarion - 2nd order neighbour spatial autocorrelation parameter (columns).

rc1

For later implementation - diagonal spatial autocorreltion parameter.

cr1

For later implementation - diagonal spatial autocorrelation parameter.

maindi

For later implementation.

rajz

When rajz = TRUE, the simulated map will be drawn.

Value

The output is a simulated map on a grid (provided as a matrix).

Details

This function can actually be parameterized in many ways, however, for isotropic landscapes, only the rho parameter is to be used. Additional parameterizations, although possible, are not permitted with the use of the other functions provided with the PatternClass package

References

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

Remmel, T.K. and M.-J. Fortin. 2013. Categorical class map patterns: characterization and comparison. Landscape Ecology. DOI: 10.1007/s/10980-013-9905-x.

Remmel, T.K. and M.-J. Fortin. What constitutes a significant difference in landscape pattern? (using R) (Pending 2014). In Gergel, S.E. and M.G. Turner. Learning landscape ecology: concepts and techniques for a sustainable world (2nd ed.). New York: Springer.

See Also

See Also as singlemap

Examples

# NOT RUN {
CARsimu(LEVEL = 6, rho = 0.2499, row2 = 0, col2 = 0, rc1 = 0, cr1 = 0, 
maindi = 1, rajz = TRUE)
# }