# ncross.rq.fitXB: Estimation of noncrossing regression quantiles with... In quantregGrowth: Growth Charts via Smooth Regression Quantiles with Automatic Smoothness Estimation and Additive Terms

## Description

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

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10``` ```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, dropvcList=FALSE, ...) ncross.rq.fitX(y, X = NULL, taus, 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, dropvcList=FALSE, nfolds=10, foldid=NULL, eps=.0001, ...) ```

## 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`. `dropvcList` see `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`. `...` 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

`gcrq`
 `1` ```##See ?gcrq ```