R Under development (unstable) (2023-08-12 r84939 ucrt) -- "Unsuffered Consequences"
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> options(na.action=na.exclude) # preserve missings
> options(contrasts=c('contr.treatment', 'contr.poly')) #ensure constrast type
> library(survival)
> 
> #
> # Here is a test case with multiple smoothing terms
> #
> 
> fit0 <- coxph(Surv(time, status) ~ ph.ecog + age, lung)
> fit1 <- coxph(Surv(time, status) ~ ph.ecog + pspline(age,3), lung)
> fit2 <- coxph(Surv(time, status) ~ ph.ecog + pspline(age,4), lung)
> fit3 <- coxph(Surv(time, status) ~ ph.ecog + pspline(age,8), lung)
> 
> 
> 
> fit4 <- coxph(Surv(time, status) ~ ph.ecog + pspline(wt.loss,3), lung)
> 
> fit5 <-coxph(Surv(time, status) ~ ph.ecog + pspline(age,3) + 
+ 	     pspline(wt.loss,3), lung)
> 
> fit1
Call:
coxph(formula = Surv(time, status) ~ ph.ecog + pspline(age, 3), 
    data = lung)

                            coef se(coef)      se2    Chisq   DF       p
ph.ecog                  0.44802  0.11707  0.11678 14.64453 1.00 0.00013
pspline(age, 3), linear  0.01126  0.00928  0.00928  1.47231 1.00 0.22498
pspline(age, 3), nonlin                             2.07924 2.08 0.37143

Iterations: 4 outer, 12 Newton-Raphson
     Theta= 0.861 
Degrees of freedom for terms= 1.0 3.1 
Likelihood ratio test=21.9  on 4.08 df, p=2e-04
n= 227, number of events= 164 
   (1 observation deleted due to missingness)
> fit2
Call:
coxph(formula = Surv(time, status) ~ ph.ecog + pspline(age, 4), 
    data = lung)

                            coef se(coef)      se2    Chisq   DF       p
ph.ecog                  0.45047  0.11766  0.11723 14.65751 1.00 0.00013
pspline(age, 4), linear  0.01117  0.00927  0.00927  1.45195 1.00 0.22822
pspline(age, 4), nonlin                             2.95816 3.08 0.41197

Iterations: 4 outer, 11 Newton-Raphson
     Theta= 0.797 
Degrees of freedom for terms= 1.0 4.1 
Likelihood ratio test=22.7  on 5.07 df, p=4e-04
n= 227, number of events= 164 
   (1 observation deleted due to missingness)
> fit3
Call:
coxph(formula = Surv(time, status) ~ ph.ecog + pspline(age, 8), 
    data = lung)

                            coef se(coef)      se2    Chisq   DF       p
ph.ecog                  0.47640  0.12024  0.11925 15.69732 1.00 7.4e-05
pspline(age, 8), linear  0.01172  0.00923  0.00923  1.61161 1.00    0.20
pspline(age, 8), nonlin                             6.93188 6.99    0.43

Iterations: 5 outer, 15 Newton-Raphson
     Theta= 0.691 
Degrees of freedom for terms= 1 8 
Likelihood ratio test=27.6  on 8.97 df, p=0.001
n= 227, number of events= 164 
   (1 observation deleted due to missingness)
> fit4
Call:
coxph(formula = Surv(time, status) ~ ph.ecog + pspline(wt.loss, 
    3), data = lung)

                              coef se(coef)      se2    Chisq   DF     p
ph.ecog                    0.51545  0.12960  0.12737 15.81939 1.00 7e-05
pspline(wt.loss, 3), line -0.00702  0.00655  0.00655  1.14638 1.00  0.28
pspline(wt.loss, 3), nonl                             2.44612 2.09  0.31

Iterations: 3 outer, 10 Newton-Raphson
     Theta= 0.776 
Degrees of freedom for terms= 1.0 3.1 
Likelihood ratio test=21.1  on 4.06 df, p=3e-04
n= 213, number of events= 151 
   (15 observations deleted due to missingness)
> fit5
Call:
coxph(formula = Surv(time, status) ~ ph.ecog + pspline(age, 3) + 
    pspline(wt.loss, 3), data = lung)

                              coef se(coef)      se2    Chisq   DF       p
ph.ecog                    0.47422  0.13495  0.13206 12.34842 1.00 0.00044
pspline(age, 3), linear    0.01368  0.00976  0.00974  1.96406 1.00 0.16108
pspline(age, 3), nonlin                               1.90116 2.07 0.40284
pspline(wt.loss, 3), line -0.00717  0.00661  0.00660  1.17529 1.00 0.27832
pspline(wt.loss, 3), nonl                             2.07729 2.03 0.35929

Iterations: 4 outer, 12 Newton-Raphson
     Theta= 0.85 
     Theta= 0.779 
Degrees of freedom for terms= 1.0 3.1 3.0 
Likelihood ratio test=25.2  on 7.06 df, p=7e-04
n= 213, number of events= 151 
   (15 observations deleted due to missingness)
> 
> rm(fit1, fit2, fit3, fit4, fit5)
> 
> proc.time()
   user  system elapsed 
   1.31    0.10    1.40