goodness.cca {vegan} | R Documentation |

Functions `goodness`

and `inertcomp`

can
be used to assess the goodness of fit for individual sites or
species. Function `vif.cca`

and `alias.cca`

can be used to
analyse linear dependencies among constraints and conditions. In
addition, there are some other diagnostic tools (see 'Details').

## S3 method for class 'cca': goodness(object, display = c("species", "sites"), choices, model = c("CCA", "CA"), statistic = c("explained", "distance"), summarize = FALSE, ...) inertcomp(object, display = c("species", "sites"), statistic = c("explained", "distance"), proportional = FALSE) spenvcor(object) intersetcor(object) vif.cca(object) ## S3 method for class 'cca': alias(object, ...)

`object` |
A result object from `cca` ,
`rda` , `capscale` or `decorana` . |

`display` |
Display `"species"` or `"sites"` . |

`choices` |
Axes shown. Default is to show all axes of the `"model"` . |

`model` |
Show constrained (`"CCA"` ) or unconstrained
(`"CA"` ) results. |

`statistic` |
Statistic used: `"explained"` gives the cumulative
percentage accounted for, `"distance"` shows the residual
distances. Distances are not available for sites in constrained or
partial analyses. |

`summarize` |
Show only the accumulated total. |

`proportional` |
Give the inertia components as proportional for the corresponding total. |

`...` |
Other parameters to the functions. |

Function `goodness`

gives the diagnostic statistics for species
or sites. The alternative statistics are the cumulative proportion of
inertia accounted for by the axes, and the residual distance left
unaccounted for. The conditional (``partialled out'') constraints are
always regarded as explained and included in the statistics.

Function `inertcomp`

decomposes the inertia into partial,
constrained and unconstrained components for each site or
species. Instead of inertia, the function can give the total
dispersion or distances from the centroid for each component.

Function `spenvcor`

finds the so-called “species –
environment correlation” or (weighted) correlation of
weighted average scores and linear combination scores. This is a bad
measure of goodness of ordination, because it is sensitive to extreme
scores (like correlations are), and very sensitive to overfitting or
using too many constraints. Better models often have poorer
correlations. Function `ordispider`

can show the same
graphically.

Function `intersetcor`

finds the so-called “interset
correlation” or (weighted) correlation of weighted averages scores
and constraints. The defined contrasts are used for factor
variables. This is a bad measure since it is a correlation. Further,
it focuses on correlations between single contrasts and single axes
instead of looking at the multivariate relationship. Fitted vectors
(`envfit`

) provide a better alternative. Biplot scores
(see `scores.cca`

) are a multivariate alternative for
(weighted) correlation between linear combination scores and
constraints.

Function `vif.cca`

gives the variance inflation factors for each
constraint or contrast in factor constraints. In partial ordination,
conditioning variables are analysed together with constraints. Variance
inflation is a diagnostic tool to identify useless constraints. A
common rule is that values over 10 indicate redundant
constraints. If later constraints are complete linear combinations of
conditions or previous constraints, they will be completely removed
from the estimation, and no biplot scores or centroids are calculated
for these aliased constraints. A note will be printed with default
output if there are aliased constraints. Function `alias`

will
give the linear coefficients defining the aliased constraints.

The functions return matrices or vectors as is appropriate.

It is a common practise to use `goodness`

statistics to remove
species from ordination plots, but this may not be a good idea, as the
total inertia is not a meaningful concept in `cca`

, in particular
for rare species.

Function `vif`

is defined as generic in package car
(`vif`

), but if you have not loaded that package
you must specify the call as `vif.cca`

. Variance inflation
factor is useful diagnostic tool for detecting nearly collinear
constraints, but these are not a problem with algorithm used in this
package to fit a constrained ordination.

Jari Oksanen. The `vif.cca`

relies heavily on the code by
W. N. Venables. `alias.cca`

is a simplified version of
`alias.lm`

.

Greenacre, M. J. (1984). Theory and applications of correspondence analysis. Academic Press, London.

Gross, J. (2003). Variance inflation factors. *R News* 3(1),
13–15.

`cca`

, `rda`

, `capscale`

,
`decorana`

, `vif`

.

data(dune) data(dune.env) mod <- cca(dune ~ A1 + Management + Condition(Moisture), data=dune.env) goodness(mod) goodness(mod, summ = TRUE) # Inertia components inertcomp(mod, prop = TRUE) inertcomp(mod, stat="d") # vif.cca vif.cca(mod) # Aliased constraints mod <- cca(dune ~ ., dune.env) mod vif.cca(mod) alias(mod) with(dune.env, table(Management, Manure)) # The standard correlations (not recommended) spenvcor(mod) intersetcor(mod)

[Package *vegan* version 1.16-32 Index]