plotGradient: plotGradient

In Hmsc: Hierarchical Model of Species Communities

plotGradient

Description

Plots an environmental gradient over one of the variables included in XData

Usage

plotGradient(
  hM,
  Gradient,
  predY,
  measure,
  xlabel = NULL,
  ylabel = NULL,
  index = 1,
  q = c(0.025, 0.5, 0.975),
  cicol = rgb(0, 0, 1, alpha = 0.5),
  pointcol = "lightgrey",
  pointsize = 1,
  showData = FALSE,
  jigger = 0,
  yshow = NA,
  showPosteriorSupport = TRUE,
  main,
  ...
)

Arguments

hM	a fitted Hmsc model object
Gradient	an object returned by constructGradient
predY	an object returned by applying the function predict to Gradient
measure	whether to plot species richness ("S"), an individual species ("Y") or community-weighted mean trait values ("T")
xlabel	label for x-axis
ylabel	label for y-axis
index	which species or trait to plot
q	quantiles of the credibility interval plotted
cicol	colour with which the credibility interval is plotted
pointcol	colour with which the data points are plotted
pointsize	size in which the data points are plotted
showData	whether raw data are plotted as well
jigger	the amount by which the raw data are to be jiggered in x-direction (for factors) or y-direction (for continuous covariates)
yshow	scale y-axis so that these values are also visible. This can used to scale y-axis so that it includes 0 and the expected maximum values.
showPosteriorSupport	add margin text on the posterior support of predicted change from gradient minimum to maximum for continuous gradients.
main	main title for the plot.
...	additional arguments for plot

Details

For measure="Y", index selects which species to plot from hM$spNames. For measure="T", index selects which trait to plot from hM$trNames. With measure="S" the row sum of pred is plotted, and thus the interpretation of "species richness" holds only for probit models. For Poisson models "S" shows the total count, whereas for normal models it shows the summed response. For measure="T", in probit model the weighting is over species occurrences, whereas in count models it is over individuals. In normal models, the weights are exp-transformed predictions to avoid negative weights

Value

For the case of a continuous covariate, returns the posterior probability that the plotted variable is greater for the last sampling unit of the gradient than for the first sampling unit of the gradient. For the case of a factor, returns the plot object.

See Also

constructGradient, predict

Examples

# Plot response of species 2 over the gradient of environmental variable x1
Gradient = constructGradient(TD$m, focalVariable="x1")
predY = predict(TD$m, Gradient=Gradient)
plotGradient(TD$m, Gradient, pred=predY, measure="Y", index = 2, showData = TRUE, jigger = 0.05)
# Plot modelled species richness over the gradient of environmental variable x1
Gradient = constructGradient(TD$m, focalVariable="x1")
predY = predict(TD$m, Gradient=Gradient)
plotGradient(TD$m, Gradient, pred=predY, measure="S")

Hmsc documentation built on Aug. 11, 2022, 5:11 p.m.