evaluate {sperich}

Result grid evaluation

Description

This function evaluates the result grids of species richness estimation.

Usage

evaluate(result.grid.one, result.grid.two, 
		title.one="Histogramm of species richness", 
		title.two="Histogramm of species richness", xmax=400, ymax=1000,
		directory=getwd(), filename="histogramm.png")

Arguments

result.grid.one

A result grid of species richness estimation. If the value is 'NULL', the grid is ignored and only the other grid will be included in the resulting png-file.

result.grid.two

A result grid of species richness estimation. If the value is 'NULL', the grid is ignored and only the other grid will be included in the resulting png-file.

title.one

The title for the histogramm of the first grid ('result.grid.one').

title.two

The title for the histogramm of the second grid ('result.grid.two').

xmax

The maximum value of the abscissa respectively the maximum number of species in the grid.

ymax

The maximum value of the ordinate respectively the maximum frequency shown in the histogramm.

directory

The directory the png-file is created in.

filename

The name of the created png-file.

Details

This routine evaluates the result grids of a species richness estimation (details in Raedig et al. 2010). It creates one or two histogramms (depending on input) of the species occurrences with additional information about the total number of species of the grid and other statistic scores. It is possible to create on png-file for every result grid or to compare two result-grids in one png-file, depending on how many grids are defined as input.

Value

This function creates a png-file with one or two histogramms (depending on input) and additional statistic informations.

Author(s)

Maximilian Lange, Sven Lautenbach

References

Raedig, C., Dorman, C.F., Hildebrandt, A. and Lautenbach, S. (2010). Reassessing Neotropical angiosperm distribution patterns based on monographic data: a geometric interpolation approach. Biodivers Conserv, 19, 1523-1546.

Examples

#load data
data(dataset.all.species)
data(dataset.landwater)

#create grid parameters
dimension <- getDimension(dataset.all.species, resolution=1)
origin <- getOrigin(dataset.all.species)

#create landwatermask
landwatermask.nocoast <- createLandwatermask(dataset.landwater,
dimension, origin, resolution=1)

#estimate species richness
species.richness.weighted <- species.richness(dataset.all.species,
landwatermask.nocoast, distances=1:5, weight=0.5, dimension,
origin, resolution=1, upperbound=3000, all.species=1:2)

#evaluation
## Not run: 
evaluate(species.richness.weighted, NULL, title.one="Histogramm 1")

## End(Not run)