# pspline

From survival v2.9-6
by Thomas Lumley

##### Penalised smoothing splines

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.

- Keywords
- survival

##### 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

##### Examples

```
data(cancer)
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
```

*Documentation reproduced from package survival, version 2.9-6, License: GPL2*

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