Description Usage Arguments Details Value Note Author(s) References See Also Examples
View source: R/singleplotter.R
Produce a series of one or more boxplots depicting the empirical (null) distribuitons of class-focused pattern metrics, as computed for a single landscape map. The code has been updated as of October 24 2016 to permit boxplots to be drawn even if landscape metrics have NA in them (those values are simply ignored). As of 21 January 2020, the code now relies on landscapemetrics and raster packages rather than the diminished SDMTools.
1 2 3 4 5 6 7 |
dat |
This is the output object produced by |
img |
This is the binary integer matrix landscape map that corresponds to the |
metrics |
A vector listing the integers, corresponding to specific metrics, that you want to plot. There are 55 unique class-level metrics computed for each of 2 classes; thus, the specific metric values in the specified vector can range from 1-110. |
rows |
This argument controls the number of rows (of plots) on the output graphics device. The total number of graphs that can be displayed simultaneously will be ( |
cols |
This argument controls the number of columns (of plots) on the output graphics device. The total number of graphs that can be displayed simultaneously will be ( |
addactual |
If TRUE, then the landscape's actual class-focused pattern metric will be added to the plot to depict its relative position within the empirical (null) distribution. |
colour |
If TRUE, the actual landscape's class-focused pattern metric, if added, will be drawn in red. If FALSE, the plot will be without colour (hollow circle). |
This function requires as input, both the original image object (img
) and output object from singlemap
(data
) such that the proper metric values can be plotted along with their expectations (distributions). This is the work-horse plotting function for metric values and distributions for any specific landscape object. Currently, implementaiton is for 64x64 pixel subset landscape maps. Future development will expand on this limiatation; however, our work has shown that this extent is sufficient for adequate pattern characterization and comparison purposes.
The output is graphical.
No further notes at this time.
Tarmo K. Remmel
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 singlemap
, and doubleplotter
.
1 2 3 | # EXAMPLE USES PREVIOUSLY PRODUCED RESULTS TO SPEED-UP THE EXAMPLE,
# BUT THE EXAMPLE FROM singlemap() SHOULD BE CALLED FIRST
singleplotter(dat = data$result1, img = data$demoimage1, metrics = c(1, 5, 10))
|
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