ncross.rq.fitXB {quantregGrowth}

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.

Value

A list of fit information.

Author(s)

Vito M. R. Muggeo

See Also

gcrq

Examples

##See ?gcrq