You can ignore the message below. The maximizing routine buried within the frailty()
command buried with coxph() has a maximizer that is not the brightest. It sometimes gets
lost but then finds its way again. The message is from one of those. It likely took a
not-so-good update step, and took a couple of iterations to recover.
In coxpenal.fit(X, Y, strats, offset, init = init, control, weights = weights,
:
Inner loop failed to coverge for iterations 3 4
To be fair the maximizing problem for a mixed effects Cox model is not easy. In the coxme
code I have spent much more time on the details of this.
Terry Therneau
------------------------------
On 09/06/2016 05:00 AM, r-help-requ...@r-project.org wrote:
Dear Elisabetta,
I have no direct answer to your question, but a suggestion: Use the
'coxme' function (in the package with the same name). In the help page
for 'frailty' (survival) you will find: "The coxme package has
superseded this method. It is faster, more stable, and more flexible."
Hth, G?ran
On 2016-09-05 11:42, Elisabetta Petracci wrote:
>Dear users,
>
>I am fitting a conditional gap time frailty cox model weighting
>observations by means of inverse probability time dependent weigths.
>Attached find the self-explaining dataset.
>
>I have used the following sintax:
>
>coxph(Surv(gaptstart,gaptstop,status)~treat+strata(nrecord01)+frailty(id,distribution="gamma",method="em"),
>data=dataNOTDrr,weights=dataNOTDrr$weight)
>
>
>And I get the following warning:
>
>Warning message:
>In coxpenal.fit(X, Y, strats, offset, init = init, control, weights =
>weights, :
> Inner loop failed to coverge for iterations 3 4
>
>
>I have tried to:
>- leave out the weights but I get the error anyway
>- to randomly select a subset of patients and I don't get the error. This
>seems to suggest that the problem is with some observations.
>
>Any suggestion?
>
>Many thanks,
>
>Elisabetta
>
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