add1.cca: Add or Drop Single Terms to a Constrained Ordination Model
In vegan: Community Ecology Package
Description
Usage
Arguments
Details
Value
Author(s)
See Also
Examples
Description
Compute all single terms that can be added to or dropped from a
constrained ordination model.
Usage
1
2
3
4
5
6
## S3 method for class 'cca'
add1(object, scope, test = c("none", "permutation"),
permutations = how(nperm=199), ...)
## S3 method for class 'cca'
drop1(object, scope, test = c("none", "permutation"),
permutations = how(nperm=199), ...)
Arguments
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 be added using anova.cca.
permutations
a list of control values for the permutations
as returned by the function how, or the
number of permutations required, or a permutation matrix where each
row gives the permuted indices.
...
Other arguments passed to add1.default,
drop1.default, and anova.cca.
Details
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,
ordiR2step 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.
Value
Returns a similar object as add1 and drop1.
Author(s)
Jari Oksanen
See Also
add1, drop1 and
anova.cca for basic methods. You probably need these
functions with step and 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.
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
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")
## see ?ordistep to do the same, but based on permutation P-values
## Not run:
ordistep(cca(dune ~ 1, dune.env), reformulate(names(dune.env)), perm.max=200)
## End(Not run)
## 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")
Example output
Loading required package: permute
Loading required package: lattice
This is vegan 2.5-4
Start: AIC=87.66
dune ~ 1
Df AIC F Pr(>F)
+ Moisture 3 86.608 2.2536 0.005 **
+ Management 3 86.935 2.1307 0.005 **
+ A1 1 87.411 2.1400 0.025 *
<none> 87.657
+ Manure 4 88.832 1.5251 0.035 *
+ Use 2 89.134 1.1431 0.235
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Step: AIC=86.61
dune ~ Moisture
Df AIC F Pr(>F)
<none> 86.608
+ Management 3 86.813 1.4565 0.075 .
+ A1 1 86.992 1.2624 0.255
+ Use 2 87.259 1.2760 0.180
+ Manure 4 87.342 1.3143 0.095 .
- Moisture 3 87.657 2.2536 0.005 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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 scaled Chi-square
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
Start: dune ~ 1
Df AIC F Pr(>F)
+ Moisture 3 86.608 2.2536 0.005 **
+ Management 3 86.935 2.1307 0.005 **
+ A1 1 87.411 2.1400 0.015 *
+ Manure 4 88.832 1.5251 0.025 *
+ Use 2 89.134 1.1431 0.265
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Step: dune ~ Moisture
Df AIC F Pr(>F)
- Moisture 3 87.657 2.2536 0.005 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Df AIC F Pr(>F)
+ Management 3 86.813 1.4565 0.065 .
+ Manure 4 87.342 1.3143 0.140
+ Use 2 87.259 1.2760 0.170
+ A1 1 86.992 1.2624 0.210
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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 scaled Chi-square
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
Df AIC F Pr(>F)
<none> 89.620
A1 1 89.591 1.9217 0.035 *
Moisture 3 87.707 2.5883 0.005 **
Management 3 87.082 2.8400 0.005 **
Use 2 91.032 1.1741 0.270
Manure 4 89.232 1.9539 0.005 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Df AIC F Pr(>F)
<none> 87.082
A1 1 87.424 1.2965 0.210
Moisture 3 85.567 1.9764 0.005 **
Use 2 88.284 1.0510 0.335
Manure 3 87.517 1.3902 0.095 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Df AIC F Pr(>F)
<none> 85.567
Management 3 87.707 2.1769 0.01 **
Moisture 3 87.082 1.9764 0.01 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Df AIC F Pr(>F)
<none> 85.567
A1 1 86.220 0.8359 0.63
Use 2 86.842 0.8027 0.76
Manure 3 85.762 1.1225 0.38
vegan documentation built on May 2, 2019, 5:51 p.m.
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Description Usage Arguments Details Value Author(s) See Also Examples
Description
Compute all single terms that can be added to or dropped from a constrained ordination model.
Usage
1 2 3 4 5 6 | ## S3 method for class 'cca'
add1(object, scope, test = c("none", "permutation"),
permutations = how(nperm=199), ...)
## S3 method for class 'cca'
drop1(object, scope, test = c("none", "permutation"),
permutations = how(nperm=199), ...)
|
Arguments
object |
A constrained ordination object from
|
scope |
A formula giving the terms to be considered for adding
or dropping; see |
test |
Should a permutation test be added using |
permutations |
a list of control values for the permutations
as returned by the function |
... |
Other arguments passed to |
Details
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,
ordiR2step 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.
Value
Returns a similar object as add1 and drop1.
Author(s)
Jari Oksanen
See Also
add1, drop1 and
anova.cca for basic methods. You probably need these
functions with step and 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.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | 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")
## see ?ordistep to do the same, but based on permutation P-values
## Not run:
ordistep(cca(dune ~ 1, dune.env), reformulate(names(dune.env)), perm.max=200)
## End(Not run)
## 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")
|
Example output
Loading required package: permute
Loading required package: lattice
This is vegan 2.5-4
Start: AIC=87.66
dune ~ 1
Df AIC F Pr(>F)
+ Moisture 3 86.608 2.2536 0.005 **
+ Management 3 86.935 2.1307 0.005 **
+ A1 1 87.411 2.1400 0.025 *
<none> 87.657
+ Manure 4 88.832 1.5251 0.035 *
+ Use 2 89.134 1.1431 0.235
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Step: AIC=86.61
dune ~ Moisture
Df AIC F Pr(>F)
<none> 86.608
+ Management 3 86.813 1.4565 0.075 .
+ A1 1 86.992 1.2624 0.255
+ Use 2 87.259 1.2760 0.180
+ Manure 4 87.342 1.3143 0.095 .
- Moisture 3 87.657 2.2536 0.005 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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 scaled Chi-square
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
Start: dune ~ 1
Df AIC F Pr(>F)
+ Moisture 3 86.608 2.2536 0.005 **
+ Management 3 86.935 2.1307 0.005 **
+ A1 1 87.411 2.1400 0.015 *
+ Manure 4 88.832 1.5251 0.025 *
+ Use 2 89.134 1.1431 0.265
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Step: dune ~ Moisture
Df AIC F Pr(>F)
- Moisture 3 87.657 2.2536 0.005 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Df AIC F Pr(>F)
+ Management 3 86.813 1.4565 0.065 .
+ Manure 4 87.342 1.3143 0.140
+ Use 2 87.259 1.2760 0.170
+ A1 1 86.992 1.2624 0.210
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
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 scaled Chi-square
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
Df AIC F Pr(>F)
<none> 89.620
A1 1 89.591 1.9217 0.035 *
Moisture 3 87.707 2.5883 0.005 **
Management 3 87.082 2.8400 0.005 **
Use 2 91.032 1.1741 0.270
Manure 4 89.232 1.9539 0.005 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Df AIC F Pr(>F)
<none> 87.082
A1 1 87.424 1.2965 0.210
Moisture 3 85.567 1.9764 0.005 **
Use 2 88.284 1.0510 0.335
Manure 3 87.517 1.3902 0.095 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Df AIC F Pr(>F)
<none> 85.567
Management 3 87.707 2.1769 0.01 **
Moisture 3 87.082 1.9764 0.01 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Df AIC F Pr(>F)
<none> 85.567
A1 1 86.220 0.8359 0.63
Use 2 86.842 0.8027 0.76
Manure 3 85.762 1.1225 0.38
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