ordisurf fits a smooth surface for given variable and
plots the result on ordination diagram.## S3 method for class 'default':
ordisurf(x, y, choices=c(1, 2), knots=10, family="gaussian", col="red",
thinplate = TRUE, add = FALSE, display = "sites",
w = weights(x), main, nlevels = 10, levels, labcex = 0.6,
bubble = FALSE, cex = 1, select = FALSE, method = "GCV.Cp",
gamma = 1, plot = TRUE, ...)## S3 method for class 'formula':
ordisurf(formula, data, ...)
## S3 method for class 'ordisurf':
calibrate(object, newdata, ...)
## S3 method for class 'ordisurf':
plot(x, what = c("contour","persp","gam"),
add = FALSE, bubble = FALSE, col = "red", cex = 1,
nlevels = 10, levels, labcex = 0.6, ...)
ordisurf an ordination configuration, either a
matrix or a result known by scores. For
plot.ordisurf and object of class "ordisurf" as
returned by gam (one
more than degrees of freedom). If knots = 0 or
knots = 1 the function will fit a linear trend surface, and
if knots = 2gam.gam.scores: typically
"sites" for ordinary site scores or "lc" for linear combination scores.levels for which contours
are drawn, or suggested number of contours in
nlevels if levels are not supplied.bubble is
numeric, its value is used for the maximum symbol size (as in
cex), or if bubble = TRUE<"GCV.Cp" uses GCV for models with
unknown scale parameter and Mallows' Cp/UBRE/AIC for models with
known scale; "GACV.Cp" as for "GCV.Cpgamma = 1.4.ordisurf? Useful if all you want is the fitted response
surface model.x ~ y, or left-hand side is the ordination x and
right-hand side the single fitted continuous variable
y. The variable y must be in the working eordisurf result object."contour"
produces a contour plot of the response surface, see
contour for details. "persp" produces a
perspective plot of the gam, or
to the graphical functions. See Note below for exceptions."ordisurf" that inherits from gam used
internally to fit the surface, but adds an item grid that
contains the data for the grid surface. The item grid has
elements x and y which are vectors of axis coordinates,
and element z that is a matrix of fitted values for
contour. The values outside the convex hull of observed
points are NA in z. The gam
component of the result can be used for further analysis like
predicting new values (see predict.gam).ordisurf fits a smooth surface using thinplate
splines (Wood 2003) in gam, and uses
predict.gam to find fitted values in a regular
grid. The smooth surface can be fitted with an extra penalty that
allows the entire smoother to be penalized back to 0 degrees of
freedom, effectively removing the term from the model (see Marra &
Wood, 2011). The addition of this extra penalty is invoked by
setting argument select to TRUE. The function plots
the fitted contours with convex hull of data points either over an
existing ordination diagram or draws a new plot. If select ==
TRUE and the smooth is effectively penalised out of the model, no
contours will be plotted. gam determines the degree of smoothness for the
fitted response surface during model fitting. Argument method
controls how gam performs this smoothness
selection. See gam for details of the available
options. Using "REML" or "ML" yields p-values for
smooths with the best coverage properties if such things matter to
you.
The function uses scores to extract ordination scores,
and x can be any result object known by that function.
User can supply a vector of prior weights w. If the ordination
object has weights, these will be used. In practise this means that
the row totals are used as weights with
cca or
decorana results. If you do not like this, but want to give
equal weights to all sites, you should set w = NULL. The
behaviour is consistent with envfit. For complete
accordance with constrained cca, you should set
display = "lc" (and possibly scaling = 2).
Function calibrate returns the fitted values of the response
variable. The newdata must be coordinates of points for which
the fitted values are desired. The function is based on
predict.gam and will pass extra arguments to
that function.
Wood, S.N. (2003) Thin plate regression splines. J. R. Statist. Soc. B 65, 95--114.
gam,
and scores. Function
envfit provides a more traditional and compact
alternative.data(varespec)
data(varechem)
vare.dist <- vegdist(varespec)
vare.mds <- monoMDS(vare.dist)
with(varechem, ordisurf(vare.mds, Baresoil, bubble = 5))
## as above but with extra penalties on smooth terms:
with(varechem, ordisurf(vare.mds, Baresoil, bubble = 5, col = "blue",
add = TRUE, select = TRUE))
## Cover of Cladina arbuscula
fit <- with(varespec, ordisurf(vare.mds, Cla.arb, family=quasipoisson))
## Get fitted values
calibrate(fit)
## Plot method
plot(fit, what = "contour")
## Plotting the "gam" object
plot(fit, what = "gam") ## 'col' and 'cex' not passed on
## or via plot.gam directly
plot.gam(fit, cex = 2, pch = 1, col = "blue")
## 'col' effects all objects drawn...Run the code above in your browser using DataLab