mgcv: Predict.matrix – R documentation

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Predict.matrix

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. (2017) Generalized Additive Models: An Introduction with R (2nd edition). Chapman and Hall/CRC Press.

Examples

# See smooth.construct examples

mgcv

Mixed GAM Computation Vehicle with Automatic Smoothness Estimation

v1.8-35

GPL (>= 2)

Authors

Simon Wood <simon.wood@r-project.org>

Initial release