Plot.ispline: Plots I-splines for Multi-Site Generalised Dissimilarity...

In zetadiv: Functions to Compute Compositional Turnover Using Zeta Diversity

Plots I-splines for Multi-Site Generalised Dissimilarity Modelling

Description

Plots I-splines computed by Return.ispline, or calls Return.ispline if the outputs from Zeta.msgdm are provided before plotting.

Usage

Plot.ispline(
  isplines = NULL,
  msgdm,
  data.env,
  distance = FALSE,
  biotic = 0,
  pch = NULL,
  lty = NULL,
  legend = TRUE,
  lwd = 1,
  cex = 1,
  num.quantiles = 11
)

Arguments

isplines	Output of function Return.ispline.
msgdm	Output of function Zeta.msgdm computed with reg.type = ispline.
data.env	Site-by-variable data frame used for the computation of msgdm, with sites as rows and environmental variables as columns.
distance	Boolean, indicates is distance was used in the computation of msgdm.
biotic	Boolean, indicates is zeta diversity from another community was used in the computation of msgdm.
pch	Shapes of the points to be used in the plotting. If nothing is provided, pch is a sequence of integers from 1 to the number of variables used for the computation of msgdm.
lty	Line types to be used in the plotting. If nothing is provided, lty is a sequence of integers from 1 to the number of variables used for the computation of msgdm.
legend	Boolean, indicates if the legend must be drawn.
lwd	Line width.
cex	Point size.
num.quantiles	Number of points to plot on the I-splines. Default is 11 to plot a point every 10 percents of the range of values.

Value

Plot.ispline returns a data frame with the same number of rows as dat and ncol(dat) * (order.ispline + kn.ispline) columns.

References

Ramsay, J. O. (1988). Monotone regression splines in action. Statistical Science, 425-441.

Ferrier, S., Manion, G., Elith, J., & Richardson, K. (2007). Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Diversity and Distributions, 13(3), 252-264.

See Also

Zeta.msgdm

Examples

utils::data(Marion.species)
xy.marion <- Marion.species[1:2]
data.spec.marion <- Marion.species[3:33]

utils::data(Marion.env)
data.env.marion <- Marion.env[3]

zeta.ispline <- Zeta.msgdm(data.spec.marion, data.env.marion, xy.marion, sam = 100,
    order = 3, normalize = "Jaccard", reg.type = "ispline")
zeta.ispline
zeta.ispline.r <- Return.ispline(zeta.ispline, data.env.marion, distance = TRUE)
zeta.ispline.r

dev.new()
Plot.ispline(isplines = zeta.ispline.r, distance = TRUE)

dev.new()
Plot.ispline(msgdm = zeta.ispline, data.env = data.env.marion, distance = TRUE)

zetadiv documentation built on June 11, 2022, 1:12 a.m.