ncross.rq.fitXB: Estimation of noncrossing regression quantiles with...
In quantregGrowth: Non-Crossing Additive Regression Quantiles and Non-Parametric Growth Charts

ncross.rq.fitXB

R Documentation

Estimation of noncrossing regression quantiles with monotonicity restrictions.

Description

These are internal functions of package quantregGrowth and should be not called by the user.

Usage

ncross.rq.fitXB(y, x, B=NULL, X=NULL, taus, monotone=FALSE, concave=FALSE, 
    nomiBy=NULL, byVariabili=NULL, ndx=10, deg=3, dif=3, lambda=0, eps=.0001, 
    var.pen=NULL, penMatrix=NULL, lambda.ridge=0, dropcList=FALSE, 
    decomList=FALSE, vcList=FALSE, dropvcList=FALSE, centerList=FALSE, 
    ridgeList=FALSE, ps.matrix.list=FALSE, colmeansB=NULL, Bconstr=NULL, 
    adjX.constr=TRUE, adList=FALSE, it.j=10, myeps=NULL, ...)

ncross.rq.fitXBsparse(y, x, B=NULL, X=NULL, taus, monotone=FALSE, concave=FALSE,
    nomiBy=NULL, byVariabili=NULL, ndx=10, deg=3, dif=3, lambda=0, eps=.0001, 
    var.pen=NULL, penMatrix=NULL, lambda.ridge=0,  dropcList=FALSE, decomList=FALSE, 
    vcList=FALSE, dropvcList=FALSE, centerList=FALSE, ridgeList=FALSE, 
    ps.matrix.list=FALSE, colmeansB=NULL, Bconstr=NULL, adjX.constr=TRUE, 
    adList=FALSE, it.j=10, myeps=NULL, ...)
    

ncross.rq.fitX(y, X = NULL, taus, adjX.constr=TRUE, lambda.ridge = 0, 
    eps = 1e-04, ...) 
    

gcrq.rq.cv(y, B, X, taus, monotone, concave, ndx, lambda, deg, dif, var.pen=NULL, 
    penMatrix=NULL, lambda.ridge=0, dropcList=FALSE, decomList=FALSE, 
    vcList=vcList, dropvcList=FALSE, nfolds=10, foldid=NULL, eps=.0001, 
    sparse=FALSE, ...)

Arguments

`y`	the responses vector. see `gcrq`
`x`	the covariate supposed to have a nonlinear relationship.
`B`	the B-spline basis.
`X`	the design matrix for the linear parameters.
`taus`	the percentiles of interest.

`monotone`	numerical value (-1/0/+1) to define a non-increasing, unconstrained, and non-decreasing flexible fit, respectively.
`concave`	numerical value (-1/0/+1) to possibly define concave or convex fits.
`nomiBy`	useful for VC models (when `B` is not provided).
`byVariabili`	useful for VC models (when `B` is not provided).
`ndx`	number of internal intervals within the covariate range, see `ndx` in `ps`.
`deg`	spline degree, see `ps`.
`dif`	difference order of the spline coefficients in the penalty term.
`lambda`	smoothing parameter value(s), see `lambda` in `ps`.
`eps`	tolerance value.
`var.pen`	Varying penalty, see `ps`.
`penMatrix`	Specified penalty matrix, see `pen.matrix` in `ps`.

`lambda.ridge`	a (typically very small) value, see `lambda.ridge` `gcrq`.
`dropcList`	see `dropc` in `ps`.
`decomList`	see `decompose` in `ps`.
`vcList`	to indicate if the smooth is VC or not, see `by` in `ps`.
`dropvcList`	see `ps`.
`centerList`	see `center` in `ps`.
`ridgeList`	see `ridge` in `ps`.
`ps.matrix.list`	nothing relevant for the user.
`colmeansB`	see `center` in `ps`.
`Bconstr`	see `constr.fit` in `ps.`
`foldid`	vector (optional) to perform cross validation, see the same arguments in `gcrq`.
`nfolds`	number of folds for crossvalidation, see the same arguments in `gcrq`.
`cv`	returning cv scores; see the same arguments in `gcrq`.
`adjX.constr`	logical to shift the linear covariates. Appropriate only with linear terms.
`adList`	see `ad` in `ps`.
`it.j`	Ignore.
`myeps`	Ignore.
`sparse`	logical, meaning if sparse computations have to be used.
`...`	optional.

Details

These functions are called by gcrq to fit growth charts based on regression quantiles with non-crossing and monotonicity restrictions. The computational methods are based on the package quantreg by R. Koenker and details are described in the reference paper.