Plot.ispline {zetadiv}

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)