Description Usage Arguments Details Value Note Author(s) References See Also Examples
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.
1 2 3 4 5 | ## 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
( |
fit |
fitted model from constrained ordination. |
scale |
optional numeric specifying the scale parameter of the model,
see |
k |
numeric specifying the "weight" of the equivalent degrees of
freedom (=: |
... |
further arguments. |
The functions find statistics that
resemble deviance
and AIC
in constrained
ordination. Actually, constrained ordination methods do not have a
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 the 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 the
variables with the highest eigenvalues are not necessarily the most
significant 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 step
function
will pass arguments to add1.cca
and
drop1.cca
, and setting test = "permutation"
will provide permutation tests of each deletion and addition which
can help in judging the validity of the 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
Godínez-Domínguez, 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
,
add1.cca
, drop1.cca
.
1 2 3 4 5 6 7 8 |
Loading required package: permute
Loading required package: lattice
This is vegan 2.4-4
Pearson's Chi-squared test
data: dune
X-squared = 1449, df = 551, p-value < 2.2e-16
Warning message:
In chisq.test(dune) : Chi-squared approximation may be incorrect
[1] 1448.956
Start: AIC=87.66
dune ~ 1
Df AIC
+ Moisture 3 86.608
+ Management 3 86.935
+ A1 1 87.411
<none> 87.657
+ Manure 4 88.832
+ Use 2 89.134
Step: AIC=86.61
dune ~ Moisture
Df AIC
<none> 86.608
+ Management 3 86.813
+ A1 1 86.992
+ Use 2 87.259
+ Manure 4 87.342
- Moisture 3 87.657
Call: cca(formula = dune ~ Moisture, data = dune.env)
Inertia Proportion Rank
Total 2.1153 1.0000
Constrained 0.6283 0.2970 3
Unconstrained 1.4870 0.7030 16
Inertia is mean squared contingency coefficient
Eigenvalues for constrained axes:
CCA1 CCA2 CCA3
0.4187 0.1330 0.0766
Eigenvalues for unconstrained axes:
CA1 CA2 CA3 CA4 CA5 CA6 CA7 CA8 CA9 CA10 CA11
0.4098 0.2259 0.1761 0.1234 0.1082 0.0908 0.0859 0.0609 0.0566 0.0467 0.0419
CA12 CA13 CA14 CA15 CA16
0.0201 0.0143 0.0099 0.0085 0.0080
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.