survival (version 2.34-1)

pspline: 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