Internal functions not inteded for users.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
NLLbeta(y, x,
Spline = c("b-spline", "tp-spline", "tpi-spline"),
Knots = NULL,
Degree = 3,
Intercept = FALSE,
Boundary.knots = range(x),
Keep.duplicates = TRUE,
outer.ok = TRUE,
...)
NPHalpha(x,
timevar,
Spline = c("b-spline", "tp-spline", "tpi-spline"),
Knots.t = NULL,
Degree.t = 3,
Intercept.t = TRUE,
Boundary.knots.t = range(timevar),
Keep.duplicates.t = TRUE,
outer.ok = TRUE,
...)
``` |

`x` |
the predictor variable. |

`timevar` |
the time variable. |

`y` |
the name of variable for which tests NLL effect. |

`Spline` |
type of spline basis. "b-spline" for B-spline basis, "tp-spline" for truncated power basis and "tpi-spline" for monotone (increasing) truncated power basis. |

`Knots` |
the internal breakpoints that define the spline used to estimate the NLL effect. By default there are none. |

`Degree` |
degree of splines which are considered. |

`Intercept` |
a logical value indicating whether intercept/first basis of spline should be considered. |

`Boundary.knots` |
range of variable which is analysed. |

`Keep.duplicates` |
Should duplicate interior knots be kept or removed. Defaults is |

`Knots.t` |
the internal breakpoints that define the spline used to estimate the NPH effect. By default there are none. |

`Degree.t` |
degree of splines which are considered. |

`Intercept.t` |
a logical value indicating whether intercept/first basis of spline should be considered. |

`Boundary.knots.t` |
range of time period which is analysed. By default it is the range of time variable. |

`Keep.duplicates.t` |
Should duplicate interior knots be kept or removed. Defaults is |

`outer.ok` |
logical indicating how are managed |

`...` |
not used |

Internal functions.

`NLLbeta(x, y, ...)`

returns `y * NLL(x, ...)`

.

`NPH(x, timevar, ...)`

is equal to `x * NPHalpha(x, timevar, ...)`

.

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