predict.cca {vegan} | R Documentation

## Prediction Tools for [Constrained] Ordination (CCA, RDA, DCA, CA, PCA)

### Description

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

### Usage

    ## S3 method for class 'cca':
    fitted(object, model = c("CCA", "CA"), ...)
    ## S3 method for class 'cca':
    predict(object, newdata, type = c("response", "wa", "sp", "lc"),
        rank = "full", model = c("CCA", "CA"), scaling = FALSE, ...)
    calibrate.cca(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, ...)

### Arguments

`object` | A result object from `cca`, `rda`, `capscale` or `decorana`.
`model` | Show constrained (`"CCA"`) or unconstrained (`"CA"`) results.
`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: `"response"` gives an approximation of the original data matrix, `"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.

### Details

Function `fitted` gives the approximation of the original data matrix from the ordination result. Function `residuals` gives the approximation of the original data from the unconstrained ordination. The `fitted.cca` and `residuals.cca` function both have the same marginal totals as the original data matrix, and their entries do not add up to the original data. They 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 (`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. The function can be used with new data, and it can be used to add new species or site sccores to existing ordinations. The function returns (weighted) orthornormal 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`).

### Value

The functions return matrices or vectors as is appropriate.

### Author(s)

Jari Oksanen.

### References

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

### See Also

`cca`, `rda`, `capscale`, `decorana`, `vif`, `goodness.cca`.

### Examples

    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.cca(mod)
    pred
    with(dune.env, plot(A1, pred[,"A1"] - A1, ylab="Prediction Error"))
    abline(h=0)

[Package vegan version 1.8-8 Index]