plotGradient {Hmsc}

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")