persp.qrrvglm {VGAM} | R Documentation |
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
.
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", add1 = FALSE, ...)
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 respectively.
|
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 title .
|
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.
|
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.
|
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.
For a rank-2 model, a list with the following components.
fitted |
A (G1*G2) by M matrix
of fitted values on the grid. Here, G1 and G2
are the two values of gridlength .
|
lv1grid, lv2grid |
The grid points for the x-axis and y-axis. |
maxfitted |
A G1 by G2 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
.
Yee (2004) does not refer to perspective plots. Instead, contour plots
via lvplot.qrrvglm
are used.
For rank-1 models, a similar function to this one is
lvplot.qrrvglm
. It plots the fitted values at the actual
site score values rather than on a fine grid here. The result has
the advantage that the user sees the curves as a direct result from a
model fitted to data whereas here, it is easy to think that the smooth
bell-shaped curves are the truth because the data is more of a distance
away.
Thomas W. Yee
Yee, T. W. (2004) A new technique for maximum-likelihood canonical Gaussian ordination. Ecological Monographs, 74, 685–701.
persp
,
cqo
,
Coef.qrrvglm
,
lvplot.qrrvglm
,
par
,
title
.
## Not run: data(hspider) 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 ## End(Not run)