wtest.run: Whittle estimation for binary map

In PatternClass: Class-Focused Pattern Metric Comparisons using Simulation

Description

This function is the workhorse for estimating the Whittle correction for a binary map. This function is generally called by a wrapping function to facilitate its use (e.g., singlemap).

Usage

wtest.run(LEVEL=6, REPSIM=20, RHO=0.2499999, CPROP=0.5, RAJZ=T, CIM="CIM", ENV="data")

Arguments

LEVEL	This is a numeric, binary matrix that represents a raster landscape. In future updates, this argumnent may migrate to be a raster object, but for now, it must be a two-valued matrix. There is no implementation for the spatial resolution of each cell as the computed metrics do not require this value. It assumes that the spatial resolution is consistent in both dimensions and across the entire scene. The image is also assumed to be a graphic representation resulting from a stationary spatial process.
REPSIM	This is a numeric matrix that provides the correction factors based on Whittle's estimation. The defalut matrix DIFF50 is provided in the data environment that is supplied with this package. However, if image sizes differ (i.e., not the 64x64 demos that are provided), this matrix needs to be recreated using build.lut and the result provided here.
RHO	This is a numeric argument recording the spatial autocorrelation parameter. Note that this is divided into 4 and thus 0.2499999 approaches the theoretical maximum value of 1.
CPROP	This argument controls the proportion of white to black pixels.
RAJZ	This Boolean argument controls whether outputs of the simulation are drawn on the screen.
CIM	A parameter that can control the naming of outputs.
ENV	A parameter that can controls the name of the environment where temporary objects are stored.

Details

This code is generally not called directly by users.

Value

Not for users.

Note

This is for internal use. Use singlemap instead.

Author(s)

Tarmo K. Remmel

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). 2016. 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 singlemap.

Examples

# No example.

PatternClass documentation built on March 14, 2020, 1:07 a.m.