plot.HOF: Plot Hierarchical Logistic Regression Models

In eHOF: Extended HOF (Huisman-Olff-Fresco) Models

Plot Hierarchical Logistic Regression Models

Description

Plot single or multiple HOF models with or without model parameters.

Usage

  ## S3 method for class 'HOF'
plot(x, marginal = c('bar', 'rug', 'hist', 'points', 'n'), boxp = TRUE, 
  las.h = 1, yl, main, model, test = 'AICc', modeltypes, onlybest = TRUE, penal, para = 
  FALSE, gam.se = FALSE, color, newdata = NULL, lwd=1, leg = TRUE, add=FALSE, xlabel, ...)
  ## S3 method for class 'HOF.list'
plot(x, plottype = c("layout", "lattice", "all") , xlabel = NULL, 
  test = 'AICc', modeltypes, border.top = 0.1, color, yl, leg = FALSE, ...)
  

Arguments

x	an object from HOF(spec, ...).
marginal	type of marginal representation for occurrences/absences.
boxp	plotting of horizontal boxplots
las.h	orientation of axes labels (0 = vertical, 1 = horizontal)
yl	range of y axis, useful for rare species. Must be given as fraction of M (between 0 and 1).
main	optional plot title
model	specific HOF model used, if not selected automatically.
test	test for model selection. Alternatives are "AICc" (default), "F", "Chisq", "AIC", "BIC" and "Dev"iance.
modeltypes	vector of suggested model types
onlybest	plot only the best model according to chosen Information criterion. If set to FALSE all calculated models will be plotted, but the best model with a thicker line.
penal	penalty term for model types, default is the number of model parameter
para	should model parameters (optima, raw.mean, niche,..) be plotted.
gam.se	plotting of two times standard error of predict.gam as confidence interval
color	model line color, vector of length seven
newdata	curves are plotted for original x-values. Otherwise you have to provide a vector with new gradient values.
leg	legend for model type (and parameters)
lwd	line width of model curve(s)
plottype	plottype, see details
add	add to existing plot
xlabel	x axis label
border.top	height of top border for legend
...	further arguments passed to or from other methods.

Details

Plottype layout will give a normal plot for a single species, or if the HOF object contains several species, the graphics display will be divided by autolayout. Multiple species can also be plotted by a lattice xyplot and plotted with plot.HOF for every species. The third option (plottype='all') plots all selected species on the same graph which might be useful to evaluate e.g. the species within one vegetation plot, see examples.

A rug adds a rug representation (1-d plot) of the data to the plot. A rug plot is a compact way of illustrating the marginal distributions of x. Positions of the data points along x and y are denoted by tick marks, reminiscent of the tassels on a rug. Rug marks are overlaid onto the axis. A dit='bar' plot will display the original response values. For binary data this will be identical to rug.

Author(s)

Florian Jansen

References

de la Cruz Rot M (2005) Improving the Presentation of Results of Logistic Regression with R. Bulletin of the Ecological Society of America 86: 41-48

See Also

HOF

Examples

	data(acre)
	sel <- c('MATRREC', 'RUMEACT', 'SILENOC', 'APHAARV', 'MYOSARV', 'DESUSOP', 'ARTE#VU')
	mo <- HOF(acre[match(sel, names(acre))], acre.env$PH_KCL, M=1, bootstrap=NULL)
	par(mar=c(2,2,1,.1))
	plot(mo, para=TRUE)

# An example for plottype='all' to show species responses for the species within 
# the most acidic and the most calcareous vegetation plot.
	## Not run: 
	allSpeciesFromAnAcidicPlot <- acre['57',] > 0
	mods.acidic <- HOF(acre[,allSpeciesFromAnAcidicPlot],acre.env$PH_KCL,M=1,bootstrap=NULL)
	allSpeciesFromAnCalcareousPlot <- acre['87',] > 0
	mods.calc <- HOF(acre[,allSpeciesFromAnCalcareousPlot],acre.env$PH_KCL,M=1,bootstrap=NULL)
	
	autolayout(2)
	plot(mods.acidic, plottype='all', main='Plot with low pH')
	abline(v=acre.env$PH_KCL[acre.env$RELEVE_NR == '57'])
  names(mods.acidic)
	
	plot(mods.calc, plottype='all', main='Plot with high pH')
	abline(v=acre.env$PH_KCL[acre.env$RELEVE_NR == '87'])
  names(mods.calc)
	
## End(Not run)

eHOF documentation built on April 23, 2022, 1:05 a.m.