predict.cca {vegan} | R Documentation |

Function `predict`

can be used to find site and species scores
with new data sets.

## S3 method for class 'cca': fitted(object, model = c("CCA", "CA"), type = c("response", "working"), ...) ## S3 method for class 'capscale': fitted(object, model = c("CCA", "CA", "Imaginary"), type = c("response", "working"), ...) ## S3 method for class 'cca': residuals(object, ...) ## S3 method for class 'cca': predict(object, newdata, type = c("response", "wa", "sp", "lc"), rank = "full", model = c("CCA", "CA"), scaling = FALSE, ...) ## S3 method for class 'cca': calibrate(object, newdata, rank = "full", ...) ## S3 method for class 'cca': coef(object, ...) ## S3 method for class 'decorana': predict(object, newdata, type = c("response", "sites", "species"), rank = 4, ...)

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

`model` |
Show constrained (`"CCA"` ) or unconstrained
(`"CA"` ) results. For `capscale` this can also be
`"Imaginary"` for imaginary components with negative eigenvalues. |

`newdata` |
New data frame to be used in
prediction of species and site scores or for calibration. Usually
this a new community data frame, but for `predict.cca`
`type = "lc"` it must be an environment data frame, and for
`type = "response"` this is ignored. |

`type` |
The type of prediction, fitted values or residuals: In
`fitted` and `residuals` , `"response"` scales results so
that the same ordination gives the same results, and `"working"`
gives the values used internally, that is after Chi-square
standardization in `cca` and scaling and centring in
`rda` . In `capscale` the `"response"` gives
the dissimilarities, and `"working"` the scaled scores that produce
the dissimilarities as Euclidean distances.
In `predict` `"response"`
gives an approximation of the original data matrix or dissimilarities,
`"wa"` the site scores as weighted averages of the community data,
`"lc"` the site scores as linear combinations of environmental data,
and `"sp"` the species scores. In `predict.decorana` the
alternatives are scores for `"sites"` or `"species"` . |

`rank` |
The rank or the number of axes used in the approximation.
The default is to use all axes (full rank) of the `"model"` or
all available four axes in `predict.decorana` . |

`scaling` |
Scaling or predicted scores
with the same meaning as in `cca` , `rda` and
`capscale` . |

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

Function `fitted`

gives the approximation of the original data
matrix or dissimilarities from the ordination result either in the
scale of the response
or as scaled internally by the function. Function `residuals`

gives
the approximation of the original data from the unconstrained
ordination. With argument `type = "response"`

the
`fitted.cca`

and `residuals.cca`

function
both give the same marginal totals as the original data matrix, and
their entries do not add up to the original data.
Functions `fitted.capscale`

and `residuals.capscale`

give the
dissimilarities with `type = "response"`

, but these are not additive,
but the `"working"`

scores are additive.
All variants of `fitted`

and `residuals`

are defined so
that for model `mod <- cca(y ~ x)`

, `cca(fitted(mod))`

is equal
to constrained ordination, and `cca(residuals(mod))`

is equal to
unconstrained part of the ordination.

Function `predict`

can find the estimate of the original data
matrix or dissimilarities (`type = "response"`

) with any rank.
With `rank = "full"`

it is identical to `fitted`

.
In addition, the function
can find the species scores or site scores from the community data
matrix for `cca`

or `rda`

.
The function can be used with new data, and it can be used to
add new species or site scores to existing ordinations. The function
returns (weighted) orthonormal scores by default, and you must
specify explicit `scaling`

to
add those scores to ordination diagrams. With
`type = "wa"`

the function finds the site scores from species
scores. In that case, the new data can contain new sites, but species
must match in the original and new data. With `type = "sp"`

the
function finds species scores from site constraints (linear
combination scores). In that case the new data can contain new
species, but sites must match in the original and new
data. With `type = "lc"`

the function finds the linear
combination scores for sites from environmental data. In that case the
new data frame must contain all constraining and conditioning environmental
variables of the model formula. If a completely new data frame is created,
extreme care is needed defining variables similarly as in the original
model, in particular with (ordered) factors. If ordination was
performed with the formula interface, the `newdata`

also can be a
data frame or matrix, but extreme care is needed that the columns
match in the original and `newdata`

.

Function `calibrate.cca`

finds estimates of constraints from
community ordination or `"wa"`

scores from `cca`

,
`rda`

and `capscale`

. This is often known as
calibration, bioindication or environmental reconstruction.
Basically, the method is similar to projecting site scores onto biplot
arrows, but it uses regression coefficients. The function can be called
with `newdata`

so that cross-validation is possible. The
`newdata`

may contain new sites, but species must match in the
original and new data The function
does not work with ‘partial’ models with `Condition`

term,
and it cannot be used with `newdata`

for `capscale`

results. The results may only be interpretable for continuous variables.

Function `coef`

will give the regression coefficients from centred
environmental variables (constraints and conditions) to linear
combination scores. The coefficients are for unstandardized environmental
variables. The coefficients will be `NA`

for aliased effects.

Function `predict.decorana`

is similar to `predict.cca`

.
However, `type = "species"`

is not available in detrended
correspondence analysis (DCA), because detrending destroys the mutual
reciprocal averaging (except for the first axis when rescaling is not
used). Detrended CA does not attempt to approximate the original data
matrix, so `type = "response"`

has no meaning in detrended
analysis (except with `rank = 1`

).

The functions return matrices, vectors or dissimilarities as is appropriate.

Jari Oksanen.

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

`cca`

, `rda`

, `capscale`

,
`decorana`

, `vif`

, `goodness.cca`

.

data(dune) data(dune.env) mod <- cca(dune ~ A1 + Management + Condition(Moisture), data=dune.env) # Definition of the concepts 'fitted' and 'residuals' mod cca(fitted(mod)) cca(residuals(mod)) # Remove rare species (freq==1) from 'cca' and find their scores # 'passively'. freq <- specnumber(dune, MARGIN=2) freq mod <- cca(dune[, freq>1] ~ A1 + Management + Condition(Moisture), dune.env) predict(mod, type="sp", newdata=dune[, freq==1], scaling=2) # New sites predict(mod, type="lc", new=data.frame(A1 = 3, Management="NM", Moisture="2"), scal=2) # Calibration and residual plot mod <- cca(dune ~ A1 + Moisture, dune.env) pred <- calibrate(mod) pred with(dune.env, plot(A1, pred[,"A1"] - A1, ylab="Prediction Error")) abline(h=0)

[Package *vegan* version 1.16-32 Index]