ENiRG (version 1.0.1)

enirg.plot: Ordination plot of enirg results

Description

Performs an ordination diagram for objects of class enirg.

Usage

enirg.plot(enirg.results, mar.col = "grey", spe.col = "black", method = "extended", plot.egvs = TRUE, asp = FALSE, title = NULL)

Arguments

enirg.results

an object of class enirg

mar.col

color for filling available space (habitat)

spe.col

color for filling used space (presences data)

method

a string character. "extended" represents available space by using the convex hull, "simplified" represents available space by a polygon, representing the range.

plot.egvs

logical. If TRUE, ecogeographical variables will be also plotted in the ordination diagram.

asp

logical. If TRUE, marginality and specialization axes will be scaled.

title

string. A title for plot.

Details

enirg.plot displays the factorial map of pixels, by projection of the row coordinates of ENFA analysis. Available and used habitat are displayed as polygon area and clouds of points, respectively. Marginality axis is the X-axis and specialization is the Y-axis. Quantitative environmental predictors used for the analysis are represented using blue arrows and qualitative ones are represented by text labels. White dot shows the centroid of the used habitat.

References

Basille, M., Calenge, C., Marboutin, E., Andersen, R. and Gaillard, J.M. (2008) Assessing habitat selection using multivariate statistics: Some refinements of the ecological-niche factor analysis. Ecological Modelling, 211, 233--240.

See Also

enirg, import.egvs, enfa, scatter.enfa, initGRASS

Examples

## Not run: 
# # starting GRASS session
# initGRASS("/usr/bin/grass-7.0.0", home=tempdir())
# initGRASS("C:/GRASS", home=tempdir())
# 
# data(apis.enirg)
# 
# # presences table
# lina <- apis.enirg$presences
# 
# # loading the environmental information in batch
# predictor.names <- c("tann", "mxtwm", "mntcm", "rfdm", "rfseas")
# predictor.maps <- paste("std_", predictor.names, sep="")
# file.names <- paste(system.file(package = "ENiRG"),
#                     "/ext/", predictor.names, ".asc", sep="")
# 
# import.egvs(file.names, predictor.names)
# 
# # standardization
# stdz.maps(predictor.names, predictor.maps)
# 
# # performing the Ecologigal Niche Factor Analysis (ENFA)
# enirg(presences.table = lina, qtegv.maps = predictor.maps,
#       species.name = "African", nf = 3, scannf = FALSE,
#       method = "normal") -> apis.enfa
# 
# enirg.plot(apis.enfa)
# 
# ## End(Not run)