I have a data set
> head(data) id centre u time event x c1 c2 c3 c4 c5 c6 c7 c8 c9 c10 c11 c12 c13 c14 1 1 0.729891 0.3300478 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0.729891 7.0100000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 3 1 0.729891 7.0150000 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 4 1 0.729891 1.3616940 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 5 1 0.729891 7.0250000 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 6 1 0.729891 5.0824055 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
and I want to fit a proportional hazards model.
IN R
I first defined formula
:
> formula Surv(time, event) ~ x + c1 + c2 + c3 + c4 + c5 + c6 + c7 + c8 + c9 + c10 + c11 + c12 + c13 + c14
and I used the coxph
function:
> library(survival) > mod <- coxph(formula, data=data) Erreur dans fitter(X, Y, strats, offset, init, control, weights = weights, : NA/NaN/Inf dans un appel à une fonction externe (argument 6) De plus : Message d'avis : In fitter(X, Y, strats, offset, init, control, weights = weights, : Ran out of iterations and did not converge
IN SAS
I used proc phreg
:
proc phreg data=data; model time*event(0) = x c1-c14 / ties=breslow; run;
Partial output:
Convergence Status Convergence criterion (GCONV=1E-8) satisfied.
Contents
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Question:
- Do you have any suggestion to make it work in R?
I have tried to increase iter.max
but the problem is still there… If needed, I can provide the data (600 rows).
Best Answer
What about changing of the starting values? Supply to init
similar values to the output of SAS and see what happens. This in general is not a good strategy to get the convergence, but it helps to see whether the problem is in algorithm, or just in starting values.
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