deviance.cca {vegan} | R Documentation |
The functions extract statistics that resemble deviance and AIC from the
result of constrained correspondence analysis cca
or
redundancy analysis rda
. These functions are rarely
needed directly, but they are called by step
in
automatic model building. Actually, cca
and
rda
do not have AIC
and these functions
are certainly wrong.
## S3 method for class 'cca': deviance(object, ...) ## S3 method for class 'cca': extractAIC(fit, scale = 0, k = 2, ...)
object |
the result of a constrained ordination
(cca or rda ). |
fit |
fitted model from constrained ordination. |
scale |
optional numeric specifying the scale parameter of the model,
see scale in step . |
k |
numeric specifying the "weight" of the equivalent degrees of
freedom (=:edf ) part in the AIC formula. |
... |
further arguments. |
The functions find statistics that
resemble deviance
and AIC
in constrained
ordination. Actually,
constrained ordination methods do not have log-Likelihood, which means
that they cannot have AIC and deviance. Therefore you should not use
these functions, and if you use them, you should not trust them. If
you use these functions, it remains as your responsibility to check
the adequacy of the result.
The deviance of cca
is equal to Chi-square of
the residual data matrix after fitting the constraints. The deviance of
rda
is defined as the residual sum of squares.
The deviance function of rda
is also used for
capscale
.
Function extractAIC
mimics
extractAIC.lm
in translating deviance to AIC.
There is little need to call these functions directly. However, they
are called implicitly in step
function used in automatic
selection of constraining variables. You should check the
resulting model with some other criteria, because the statistics used
here are unfounded. In particular, the penalty k
is not properly
defined, and the default k = 2
is not justified
theoretically. If you have only continuous covariates, the step
function will base the model building on magnitude of eigenvalues, and
the value of k
only influences the stopping point (but
variable with highest eigenvalues is not necessarily the most
significant one in permutation
tests in anova.cca
). If you also
have multi-class factors, the value of k
will have a
capricious effect in model building.
The deviance
functions return ``deviance'', and
extractAIC
returns effective degrees of freedom and ``AIC''.
These functions are unfounded and untested and they should not be used
directly or implicitly. Moreover, usual caveats in using
step
are very valid.
Jari Oksanen
GodÃnez-DomÃnguez, E. & Freire, J. (2003) Information-theoretic approach for selection of spatial and temporal models of community organization. Marine Ecology Progress Series 253, 17–24.
cca
, rda
, anova.cca
,
step
, extractAIC
.
# The deviance of correspondence analysis equals Chi-square data(dune) data(dune.env) chisq.test(dune) deviance(cca(dune)) # Backward elimination from a complete model "dune ~ ." ord <- cca(dune ~ ., dune.env) ord step(ord) # Stepwise selection (forward from an empty model "dune ~ 1") step(cca(dune ~ 1, dune.env), scope = formula(ord)) # ANOVA: added variable + the first left out anova(cca(dune ~ Moisture + Management, dune.env), permut=200, by = "terms")