ordistep {vegan}  R Documentation 
Automatic stepwise model building for constrained ordination methods
(cca
, rda
, capscale
).
The function is modelled after step
and can do forward,
backward and stepwise model selection.
ordistep(object, scope, direction = c("both", "backward", "forward"), Pin = 0.05, Pout = 0.1, pstep = 100, perm.max = 1000, steps = 50, trace = TRUE, ...)
object 
An ordination object inheriting from cca .

scope 
Defines the range of models examined in the stepwise search.
This should be either a single formula, or a list containing
components upper and lower , both formulae.
See step for details.

direction 
The mode of stepwise search, can be one of "both" ,
"backward" , or "forward" , with a default of "both" .
If the scope argument is missing the default for direction
is "backward" .

Pin, Pout 
Limits of permutation Pvalues for adding (Pin ) a term to
the model, or dropping (Pout ) from the model. Term is added if
P <= Pin , and removed if P > Pout .

pstep 
Number of permutations in one step. See add1.cca .

perm.max 
Maximum number of permutation in anova.cca .

steps 
Maximum number of iteration steps of dropping and adding terms. 
trace 
If positive, information is printed during the model building. Larger values may give more information. 
... 
Any additional arguments to add1.cca and
drop1.cca .

The basic functions for model choice in constrained ordination are
add1.cca
and drop1.cca
. With these functions,
ordination models can be chosen with standard R\ function
step
which bases the term choice on AIC. AIClike
statistics for ordination are provided by functions
deviance.cca
and extractAIC.cca
(with
similar functions for rda
). Actually, constrained
ordination methods do not have AIC, and therefore the step
may not be trusted. This function provides an alternative using
permutation Pvalues.
Function ordistep
defines the model, scope
of models
considered, and direction
of the procedure similarly as
step
. The function alternates with drop
and
add
steps and stops when the model was not changed during one
step. The 
and +
signs in the summary
table indicate which stage is performed. The number of permutations
is selected adaptively with respect to the defined decision limit. It
is often sensible to have Pout
> Pin
in stepwise
models to avoid cyclic adds and drops of single terms.
Function returns the chosen model.
Jari Oksanen
The function handles constrained ordination methods cca
,
rda
and capscale
. The underlying functions
are add1.cca
and drop1.cca
, and the
function is modelled after standard step
(which also can
be used directly but uses AIC for model choice, see
extractAIC.cca
).
## See add1.cca for another example data(dune) data(dune.env) mod1 < rda(dune ~ ., dune.env) ordistep(mod1) ordistep(rda(dune ~ 1, dune.env), scope = formula(mod1))