| Predict.matrix {mgcv} | R Documentation |
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.
Predict.matrix(object,data)
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. |
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.
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.
Simon N. Wood simon.wood@r-project.org
Wood, S.N. (2000) Modelling and Smoothing Parameter Estimation with Multiple Quadratic Penalties. J.R.Statist.Soc.B 62(2):413-428
Wood, S.N. (2003) Thin plate regression splines. J.R.Statist.Soc.B 65(1):95-114
Wood, S.N. (in press) Stable and efficient multiple smoothing parameter estimation for generalized additive models. J. Amer. Statist. Ass.
http://www.stats.gla.ac.uk/~simon/
gam,gamm,
smooth.construct, PredictMat
# See smooth.construct examples