pspline {survival} | R Documentation |

## Penalised smoothing splines

### Description

Specifies a penalised spline basis for the predictor. This is done by fitting a comparatively small set of splines and penalising the integrated second derivative. Results are similar to smoothing splines with a knot at each data point but computationally simpler.

### Usage

pspline(x, df=4, theta, nterm=2.5 * df, degree=3, eps=0.1, method, ...)

### Arguments

`x` |
predictor |

`df` |
approximate degrees of freedom. `df=0` means use AIC |

`theta` |
roughness penalty |

`nterm` |
number of splines in the basis |

`degree` |
degree of splines |

`eps` |
accuracy for `df` |

`method` |
Method for automatic choice of `theta` |

`...` |
I don't know what this does |

### Value

Object of class `coxph.penalty`

containing the spline basis with
attributes specifying control functions.

### See Also

`coxph`

,`survreg`

,`ridge`

,`frailty`

### Examples

lfit6 <- survreg(Surv(time, status)~pspline(age, df=2), cancer)
plot(cancer$age, predict(lfit6), xlab='Age', ylab="Spline prediction")
title("Cancer Data")
fit0 <- coxph(Surv(time, status) ~ ph.ecog + age, cancer)
fit1 <- coxph(Surv(time, status) ~ ph.ecog + pspline(age,3), cancer)
fit3 <- coxph(Surv(time, status) ~ ph.ecog + pspline(age,8), cancer)
fit0
fit1
fit3

[Package

*survival* version 2.17

Index]