[R] Efficient way to update a survival model
Frank S.
|_j_rod @end|ng |rom hotm@||@com
Thu Aug 29 12:38:52 CEST 2019
Hi Vito,
Thanks for your reply! Following your suggestion, I have tried:
Cox[[3]] <- update(Cox[[2]], . ~ . + cos(3 * v), init=c(coef(Cox[[1]]), 0, 0), data = pbc)
Cox[[3]] <- update(Cox[[2]], . ~ . + cos(3 * v), data = pbc)
and both expressions lead to the same result. Is that OK?
Additionally, in my original question I wondered about the possibility of reducing the
10 lines of code to one general expression or some loop. Is it possible?
Best,
Frank
________________________________
De: Vito Michele Rosario Muggeo <vito.muggeo using unipa.it>
Enviado: jueves, 29 de agosto de 2019 8:54
Para: Frank S. <f_j_rod using hotmail.com>
Cc: r-help using r-project.org <r-help using r-project.org>
Asunto: Re: [R] Efficient way to update a survival model
dear Frank,
update() does not update actually.. It just builds a new call which is
evaluated. To speed up the procedure you could try to supply starting
values via argument 'init'. The first values come from the previous
fit, and the last one referring to new coefficients is set to zero (or
any other appropriate value).
Something like (untested), for instance
update(Cox[[2]], . ~ . + cos(3 * v), init=c(coef(Cox[[1]]),0), data = pbc)
Hope this helps,
best,
vito
"Frank S." <f_j_rod using hotmail.com> ha scritto:
> Hello everybody, I come with a question which I do not know how to
> conduct in an efficient way. In order to
> provide a toy example, consider the dataset "pbc" from the package
> "survival". First, I fit the Cox model "Cox0":
>
> library("survival")
> set.seed(1)
> v <- runif(nrow(pbc), min = 0, max = 2)
> Cox0 <- coxph(Surv(pbc$time,pbc$status == 2) ~ v, data = pbc)
>
> Then, from the above model, I can fit recursively 10 additional models as:
>
> Cox <- list()
>
> Cox[[1]] <- update(Cox0, . ~ . + cos(1 * v), data = pbc)
> Cox[[2]] <- update(Cox[[1]], . ~ . + cos(2 * v), data = pbc)
> Cox[[3]] <- update(Cox[[2]], . ~ . + cos(3 * v), data = pbc)
> Cox[[4]] <- update(Cox[[3]], . ~ . + cos(4 * v), data = pbc)
> ...
> Cox[[10]] <- update(Cox[[9]], . ~ . + cos(10* v), data = pbc)
>
> Since in practice I have to repeat above step until Cox[[100]], say,
> do you know an efficient way to
> wrap this code chunk in a loop or similar?
>
> I had tried:
>
> set.seed(1)
> v <- runif(nrow(pbc), min = 0, max = 2)
> Cox0 <- coxph(Surv(pbc$time,pbc$status == 2) ~ v, data = pbc)
>
> Cox <- list()
> Cox[[1]] <- update(Cox0, . ~ . + cos(1 * v), data = pbc)
> for (k in 1:10) {
> Cox[[k + 1]] <- update(Cox[[k]], . ~ . + cos((k + 1) * v), data = pbc)
> }
>
> However, from Cox[[3]] onwards, the intermediate values of integer k
> are not included here (for
> instance, the model Cox[[10]] would only include the cosinus terms
> for cos(1*v) and cos(10*v)).
>
[[elided Hotmail spam]]
>
> Frank
>
> [[alternative HTML version deleted]]
>
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