Constructs prediction matrix for smooth classes used in
gam, specifically by summary and predict.
## S3 method for class 'sp.smooth' Predict.matrix(object, data)
Object should be a
A data frame containing the values of the named (time) covariate
at which the smooth term is to be evaluated. Just as in
Requires that summary compute standard errors using frequentist
approach, i.e. that the full original data set including missing
values (tagged as NA) be passed. This is accomplished in practice
summary.gam(object, p.type=5, freq=TRUE). This
is accomplished in this package by overloading the
function, so the user should not have to worry about this.
This function is not intended to be called directly by the user.
A matrix mapping the coeffients for the smooth term to its
values at the supplied data values. Consists explicitly of an
augmented Slepian basis matrix, subselected to the elements
Wesley Burr email@example.com
Wood S.N. (2006) Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC Press.
Wood, S.N. (2011) Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society - Series B (Applied Statistics), 73(1), 3-36.
Hastie, T.J. & Tibshirani, R.J. (1990) Generalized Additive Models. Chapman and Hall.
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