The function provides plot.lm
style diagnostic plots
for the results of constrained ordination from cca
,
rda
, dbrda
and
capscale
. Normally you do not need these plots,
because ordination is descriptive and does not make assumptions on
the distribution of the residuals. However, if you permute residuals
in significance tests (anova.cca
), you may be
interested in inspecting that the residuals really are exchangeable
and independent of fitted values.
ordiresids(x, kind = c("residuals", "scale", "qqmath"),
residuals = "working", type = c("p", "smooth", "g"),
formula, ...)
The function returns a Lattice
object that can
displayed as plot.
Ordination result from cca
, rda
,
dbrda
, capscale
.
The type of plot: "residuals"
plot residuals
against fitted values, "scale"
the square root of absolute
residuals against fitted values, and "qqmath"
the residuals
against expected distribution (defaults qnorm
),
unless defined differently in the formula
argument.
The kind of residuals and fitted values, with alternatives
"working"
, "response"
, "standardized"
and
"studentized"
(see Details).
The type of plot. The argument is passed on to lattice functions.
Formula to override the default plot. The formula can
contain items Fitted
, Residuals
, Species
and
Sites
(provided that names of species and sites are available
in the ordination result).
Other arguments passed to lattice functions.
Jari Oksanen
The default plots are similar as in plot.lm
, but they
use Lattice
functions
xyplot
and qqmath
. The
alternatives have default formulae but these can be replaced by the
user. The elements available in formula or in the groups
argument
are Fitted
, Residuals
, Species
and Sites
.
With residuals = "response"
and residuals = "working"
the fitted values and residuals are found with functions
fitted.cca
and residuals.cca
. With
residuals = "standardized"
the residuals are found with
rstandard.cca
, and with residuals = "studentized"
they are found with rstudent.cca
, and in both cases the
fitted values are standardized with sigma.cca
.
data(varespec)
data(varechem)
mod <- cca(varespec ~ Al + P + K, varechem)
ordiresids(mod)
ordiresids(mod, formula = Residuals ~ Fitted | Species, residuals="standard",
cex = 0.5)
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