Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/ncross.rq.fitXB.r

These are internal functions of package `quantregGrowth`

and should be not
called by the user.

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, ...)
``` |

`y` |
the responses vector. see |

`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 |

`byVariabili` |
useful for VC models (when |

`ndx` |
number of internal intervals within the covariate range, see |

`deg` |
spline degree, see |

`dif` |
difference order of the spline coefficients in the penalty term. |

`lambda` |
smoothing parameter value(s), see |

`eps` |
tolerance value. |

`var.pen` |
Varying penalty, see |

`penMatrix` |
Specified penalty matrix, see |

`lambda.ridge` |
a (typically very small) value, see |

`dropcList` |
see |

`decomList` |
see |

`dropvcList` |
see |

`foldid` |
vector (optional) to perform cross validation, see the same arguments in |

`nfolds` |
number of folds for crossvalidation, see the same arguments in |

`cv` |
returning cv scores; see the same arguments in |

`...` |
optional. |

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.

A list of fit information.

Vito M. R. Muggeo

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

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