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.
pspline(x, df=4, theta, nterm=2.5 * df, degree=3, eps=0.1, method, ...)
- approximate degrees of freedom.
df=0means use AIC
- roughness penalty
- number of splines in the basis
- degree of splines
- accuracy for
- Method for automatic choice of
- I don't know what this does
- Object of class
coxph.penaltycontaining the spline basis with attributes specifying control functions.
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