| pspline {survival} | R Documentation |
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, ...)
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 |
Object of class coxph.penalty containing 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