qss {quantreg}R Documentation

Additive Nonparametric Terms for rqss Fitting

Description

In the formula specification of rqss nonparametric terms are specified with qss. Both univariate and bivariate specifications are possible, and qualitative constraints may also be specified for the qss terms.

Usage

qss(x, constraint = "N", lambda = 1, ndum = 0, dummies = NULL, w = rep(1, length(x)))

Arguments

x The covariate determining the nonparametric component, if x is a matrix with two columns then the qss function will construct a penalized triogram term.
lambda The smoothing parameter governing the tradeoff between fidelity and the penalty component for this term. In future versions there should be an automatic mechanism for default choice of the lambdas. For now, this is the responsibility of the user.
constraint Optional specification of qualitative constraints on the fitted univariate qss functions, take the values: "N","I","D","U","C" "UI","UD","CI","CD" for none, increasing, decreasing, convex, concave, convex and increasing, etc. And for bivariate qss components can take the values "N","U","C" for none, convex, and concave.
ndum number of dummy vertices: this is only relevant for qss2 terms. In addition to vertices at the observed (x,y) points ndum dummy vertices are generated – distributed uniformly over the rectangle given by the Cartesian product of the ranges of x and y – observations that fall in the convex hull of the observations are retained. So the actual number of dummy vertices used is smaller than ndum. The values of these vertices are returned in the list dummies, so that they can be reused.
dummies list of dummy vertices as generated, for example by triogram.fidelity when ndum > 0. Should be a list with x and y components. These points should lie inside the convex hull of the real xy points, but no explicit checking of this assertion is currently done.
w weights not yet unimplemented

Details

The various pieces returned are stored in sparse matrix.csr form. See rqss for details on how they are assembled. To preserve the sparsity of the design matrix the first column of each qss term is dropped. This differs from the usual convention that would have forced qss terms to have mean zero. This convention has implications for prediction that need to be recognized. The penalty components for qss terms are based on total variation penalization of the first derivative (and gradient, for bivariate x) as described in the references appearing in the help for rqss.

Value

F Fidelity component of the design matrix
dummies List of dummy vertices
A Penalty component of the design matrix
R Constraint component of the design matrix
r Constraint component of the rhs

Author(s)

Roger Koenker

See Also

rqss


[Package quantreg version 3.82 Index]