| add1.cca {vegan} | R Documentation |
Compute all single terms that can be added or dropped from a constrained ordination model.
## S3 method for class 'cca':
add1(object, scope, test = c("none", "permutation"),
pstep = 100, perm.max = 200, ...)
## S3 method for class 'cca':
drop1(object, scope, test = c("none", "permutation"),
pstep = 100, perm.max = 200, ...)
object |
A constrained ordination object from
cca, rda or capscale. |
scope |
A formula giving the terms to be considered for adding
or dropping; see add1 for details. |
test |
Should a permutation test added using anova.cca. |
pstep |
Number of permutations in one step, passed as argument
step to anova.cca. |
perm.max |
Maximum number of permutation in anova.cca. |
... |
Other arguments passed to add1.default,
drop1.default, and anova.cca. |
With argument test = "none" the functions will only call
add1.default or drop1.default. With
argument test = "permutation" the functions will add test
results from anova.cca. Function drop1.cca will
call anova.cca with argument by = "margin".
Function add1.cca will implement a test for single term
additions that is not directly available in anova.cca.
Functions are used implicitly in step and
ordistep. The deviance.cca and
deviance.rda used in step have no firm
basis, and setting argument test = "permutation" may help in
getting useful insight into validity of model building. Function
ordistep calls alternately drop1.cca and
add1.cca with argument test = "permutation" and
selects variables by their permutation P-values. Meticulous
use of add1.cca and drop1.cca will allow more
judicious model building.
The default perm.max is set to a low value, because
permutation tests can take a long time. It should be sufficient to
give a impression on the significances of the terms, but higher
values of perm.max should be used if P values really
are important.
Returns a similar object as add1 and drop1.
Jari Oksanen
add1, drop1 and
anova.cca for basic methods. You probably need these
functions with step and link{ordistep}. Functions
deviance.cca and extractAIC.cca are used
to produce the other arguments than test results in the
output. Functions cca, rda and
capscale produce result objects for these functions.
data(dune) data(dune.env) ## Automatic model building based on AIC but with permutation tests step(cca(dune ~ 1, dune.env), reformulate(names(dune.env)), test="perm") ## The same, but based on permutation P-values ordistep(cca(dune ~ 1, dune.env), reformulate(names(dune.env)), perm.max=500) ## Manual model building ## -- define the maximal model for scope mbig <- rda(dune ~ ., dune.env) ## -- define an empty model to start with m0 <- rda(dune ~ 1, dune.env) ## -- manual selection and updating add1(m0, scope=formula(mbig), test="perm") m0 <- update(m0, . ~ . + Management) add1(m0, scope=formula(mbig), test="perm") m0 <- update(m0, . ~ . + Moisture) ## -- included variables still significant? drop1(m0, test="perm") add1(m0, scope=formula(mbig), test="perm")