VGAM (version 0.8-2)

persp.qrrvglm: Perspective plot for QRR-VGLMs

Description

Produces a perspective plot for a CQO model (QRR-VGLM). It is only applicable for rank-1 or rank-2 models with argument Norrr = ~ 1.

Usage

persp.qrrvglm(x, varlvI = FALSE, reference = NULL, plot.it = TRUE, 
              xlim=NULL, ylim=NULL, zlim=NULL,
              gridlength = if (Rank==1) 301 else c(51,51),
              whichSpecies = NULL,
              xlab = if (Rank==1) "Latent Variable" else "Latent Variable 1",
              ylab = if (Rank==1) "Expected Value" else "Latent Variable 2",
              zlab = "Expected value", labelSpecies = FALSE,
              stretch = 1.05, main="", ticktype = "detailed",
              col = if (Rank==1) par()$col else "white",
              llty=par()$lty, llwd=par()$lwd,
              add1 = FALSE, ...)

Arguments

x

Object of class "qrrvglm", i.e., a constrained quadratic ordination (CQO) object.

varlvI

Logical that is fed into Coef.qrrvglm.

reference

Integer or character that is fed into Coef.qrrvglm.

plot.it

Logical. Plot it?

xlim, ylim

Limits of the x- and y-axis. Both are numeric of length 2. See par.

zlim

Limits of the z-axis. Numeric of length 2. Ignored if rank is 1. See par.

gridlength

Numeric. The fitted values are evaluated on a grid, and this argument regulates the fineness of the grid. If Rank=2 then the argument is recycled to length 2, and the two numbers are the number of grid points on the x- and y-axes resp

whichSpecies

Numeric or character vector. Indicates which species are to be plotted. The default is to plot all of them. If numeric, it should contain values in the set {1,2,...,$S$} where $S$ is the number of species.

xlab, ylab

Character caption for the x-axis and y-axis. By default, a suitable caption is found. See the xlab argument in plot or title.

zlab

Character caption for the z-axis. Used only if Rank=2. By default, a suitable caption is found. See the xlab argument in plot or

labelSpecies

Logical. Whether the species should be labelled with their names. Used for Rank=1 only. The position of the label is just above the species' maximum.

stretch

Numeric. A value slightly more than 1, this argument adjusts the height of the y-axis. Used for Rank=1 only.

main

Character, giving the title of the plot. See the main argument in plot or title.

ticktype

Tick type. Used only if Rank=2. See persp for more information.

col

Color. See persp for more information.

llty

Line type. Rank-1 models only. See the lty argument of par.

llwd

Line width. Rank-1 models only. See the lwd argument of par.

add1

Logical. Add to an existing plot? Used only for rank-1 models.

...

Arguments passed into persp. Useful arguments here include theta and phi, which control the position of the eye.

Value

• For a rank-2 model, a list with the following components.
• fitted
  A $(G_1 \times G_2)$ by $M$ matrix of fitted values on the grid. Here, $G_1$ and $G_2$ are the two values of gridlength.
• lv1grid, lv2grid
  The grid points for the x-axis and y-axis.
• maxfitted
  A $G_1$ by $G_2$ matrix of maximum of the fitted values over all species. These are the values that are plotted on the z-axis.
• For a rank-1 model, the components lv2grid and maxfitted are NULL.

Details

For a rank-1 model, a perspective plot is similar to lvplot.qrrvglm but plots the curves along a fine grid and there is no rugplot to show the site scores.

For a rank-2 model, a perspective plot has the first latent variable as the x-axis, the second latent variable as the y-axis, and the expected value (fitted value) as the z-axis. The result of a CQO is that each species has a response surface with elliptical contours. This function will, at each grid point, work out the maximum fitted value over all the species. The resulting response surface is plotted. Thus rare species will be obscured and abundant species will dominate the plot. To view rare species, use the whichSpecies argument to select a subset of the species.

A perspective plot will be performed if Norrr = ~ 1, and Rank = 1 or 2. Also, all the tolerance matrices of those species to be plotted must be positive-definite.

References

Yee, T. W. (2004) A new technique for maximum-likelihood canonical Gaussian ordination. Ecological Monographs, 74, 685--701.

See Also

persp, cqo, Coef.qrrvglm, lvplot.qrrvglm, par, title.

Examples

hspider[,1:6] = scale(hspider[,1:6]) # Good idea when ITolerances = TRUE
set.seed(111)
r1 = cqo(cbind(Alopacce, Alopcune, Alopfabr, Arctlute, Arctperi,
               Auloalbi, Pardmont, Pardnigr, Pardpull, Trocterr) ~
         WaterCon + BareSand + FallTwig + CoveMoss + CoveHerb + ReflLux,
         poissonff, hspider, trace = FALSE, ITolerances = TRUE)
set.seed(111)  # r2 below is an ill-conditioned model
r2 = cqo(cbind(Alopacce, Alopcune, Alopfabr, Arctlute, Arctperi,
               Auloalbi, Pardmont, Pardnigr, Pardpull, Trocterr) ~
         WaterCon + BareSand + FallTwig + CoveMoss + CoveHerb + ReflLux,
         isdlv = c(2.4,1.0), Muxfactor = 3.0, trace = FALSE,
         poissonff, hspider, Rank = 2, EqualTolerances = TRUE)

sort(r1@misc$deviance.Bestof)  # A history of the fits
sort(r2@misc$deviance.Bestof)  # A history of the fits
if (deviance(r2) > 857) stop("suboptimal fit obtained")

persp(r1, xlim=c(-6,5), col=1:4, label=TRUE)

# Involves all species 
persp(r2, xlim=c(-6,5), ylim=c(-4,5), theta=10, phi=20, zlim=c(0,220))
# Omit the two dominant species to see what's behind them
persp(r2, xlim=c(-6,5), ylim=c(-4,5), theta=10, phi=20, zlim=c(0,220), 
      which=(1:10)[-c(8,10)]) # Use zlim to retain the original z-scale