pspline

0th

Percentile

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

coxph,survreg,ridge,frailty

Aliases
  • pspline
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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