Dear all,
I am using R x64 3.3.2 on Windows 10.
I have fitted a Prentice-Williams-Peterson counting process model with a number
of covariates as follows:
Coxmod1 <-
coxph(Surv(start,stop,status)~var1+var2+factor(var3)+cluster(var4)+strata(var5),data=Lauras)
I would now like to make predictions based on either a subset of patients from
the original data set, or a new patient with randomly selected characteristics.
I have used the following code for both scenario:
Original dataset:
Subset1 <- subset(Lauras,(var1=="M") & (var2==2) & (var3<=10))
Output1 <- predict(Coxmod1,Subset1,type="expected",se.fit=TRUE)
New patient:
Subset2 <- (0, 365, 1, "M", 2, 10, "A001", 12)
Output2 <- predict(Coxmod1,Subset2,type="expected",se.fit=TRUE)
In both cases I receive in excess of 50 warnings which all state:
1: In min(diff(time)) : no non-missing arguments to min; returning Inf
The outputted predictions contain a surprisingly high number of 1s, as a result
of "returning Inf" I assume. However, the characteristics for the patients
with a prediction of 1 are not unusual i.e. there are no missing values etc.
Can anyone suggest what might be causing these warning messages, and what steps
I can take to prevent them reoccurring as they are causing predictions for
every patient subgroup to have the same summary statistics.
Kind regards,
Laura
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