smoothCon {mgcv} | R Documentation

## Prediction/Construction wrapper functions for GAM smooth terms

### Description

Wrapper functions for construction of and prediction from smooth terms in a GAM. The purpose of the wrappers is to allow user-transparant re-parameterization of smooth terms, in order to allow identifiability constraints to be absorbed into the parameterization of each term, if required.

### Usage

    smoothCon(object,data,knots,absorb.cons=FALSE)
    PredictMat(object,data)

### Arguments

`object` | is a smooth specification object or a smooth object.
`data` | A data frame containing the values of the (named) covariates at which the smooth term is to be evaluated.
`knots` | An optional data frame supplying any knot locations to be supplied for basis construction.
`absorb.cons` | Set to `TRUE` in order to have identifiability constraints absorbed into the basis.

### Details

These wrapper functions exist to allow smooths specified using `smooth.construct` and `Predict.matrix` method functions to be re-parameterized so that identifiability constraints are no longer required in fitting. This is done in a user transparent manner, but is typically of no importance in use of GAMs.

The parameterization used by `gam` can be controlled via `gam.control`.

### Value

From `smoothCon` a `smooth` object returned by the appropriate `smooth.construct` method function. If constraints are to be absorbed then the object will have an attributes `"qrc"` and `"nCons"`, the qr decomposition of the constraint matrix (returned by `qr`) and the number of constraints, respectively: these are used in the re-parameterization.
For `predictMat` a matrix which will map the parameters associated with the smooth to the vector of values of the smooth evaluated at the covariate values given in `object`.

### Author(s)

Simon N. Wood simon.wood@r-project.org

### References

http://www.stats.gla.ac.uk/~simon/

### See Also

`gam.control`, `smooth.construct`, `Predict.matrix`

[Package mgcv version 1.3-1 Index]