Predict.matrix {mgcv} | R Documentation

## Prediction methods for smooth terms in a GAM

### Description

Takes `smooth` objects produced by `smooth.construct` methods and obtains the matrix mapping the parameters associated with such a smooth to the predicted values of the smooth at a set of new covariate values.

In practice this method is often called via the wrapper function `PredictMat`.

### Usage

    Predict.matrix(object,data)
    Predict.matrix2(object,data)

### Arguments

`object` | is a smooth object produced by a `smooth.construct` method function. The object contains all the information required to specify the basis for a term of its class, and this information is used by the appropriate `Predict.matrix` function to produce a prediction matrix for new covariate values. Further details are given in `smooth.construct`.
`data` | A data frame containing the values of the (named) covariates at which the smooth term is to be evaluated. Exact requirements are as for `smooth.construct` and `smooth.construct2`

.

### Details

Smooth terms in a GAM formula are turned into smooth specification objects of class `xx.smooth.spec` during processing of the formula. Each of these objects is converted to a smooth object using an appropriate `smooth.construct` function. The `Predict.matrix` functions are used to obtain the matrix that will map the parameters associated with a smooth term to the predicted values for the term at new covariate values.

Note that new smooth classes can be added by writing a new `smooth.construct` method function and a corresponding `Predict.matrix` method function: see the example code provided for `smooth.construct` for details.

### Value

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`. If the smooth class is one which generates offsets the corresponding offset is returned as attribute `"offset"` of the matrix.

### Author(s)

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

### References

Wood S.N. (2006) Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC Press.

### See Also

`gam`,`gamm`, `smooth.construct`, `PredictMat`

### Examples

    # See smooth.construct examples

[Package mgcv version 1.7-18 Index]