refund: smooth.construct.peer.smooth.spec – R documentation

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smooth.construct.peer.smooth.spec

Basis constructor for PEER terms

Description

Smooth basis constructor to define structured penalties (Randolph et al., 2012) for smooth terms.

Usage

## S3 method for class 'peer.smooth.spec'
smooth.construct(object, data, knots)

Arguments

`object`	a `peer.smooth.spec` object, usually generated by a term `s(x, bs="peer")`; see Details.
`data`	a list containing the data (including any `by` variable) required by this term, with names corresponding to `object$term` (and `object$by`). Only the first element of this list is used.
`knots`	not used, but required by the generic `smooth.construct`.

Details

The smooth specification object, defined using s(), should contain an xt element. xt will be a list that contains additional information needed to specify the penalty. The type of penalty is indicated by xt$pentype. There are four types of penalties available:

xt$pentype=="RIDGE" for a ridge penalty, the default
xt$pentype=="D" for a difference penalty. The order of the difference penalty is specified by the m argument of s().
xt$pentype=="DECOMP" for a decomposition-based penalty, bP_Q + a(I-P_Q), where P_Q = Q^t(QQ^t)^{-1}Q. The Q matrix must be specified by xt$Q, and the scalar a by xt$phia. The number of columns of Q must be equal to the length of the data. Each row represents a basis function where the functional predictor is expected to lie, according to prior belief.
xt$pentype=="USER" for a user-specified penalty matrix L, supplied by xt$L.

Value

An object of class "peer.smooth". See smooth.construct for the elements that this object will contain.

Author(s)

Madan Gopal Kundu mgkundu@iupui.edu and Jonathan Gellar

References

Randolph, T. W., Harezlak, J, and Feng, Z. (2012). Structured penalties for functional linear models - partially empirical eigenvectors for regression. Electronic Journal of Statistics, 6, 323-353.