On 11/17/05, Christoph Scherber [EMAIL PROTECTED] wrote:
Dear list,
I have data on insect survival in different cages; these have the
following structure:
deathtime status id cageS F G L S
1.5 1 1 C1 8 2 1 1 1
1.5 1 2 C1 8 2 1 1 1
11.5 1 3 C1 8 2 1 1 1
11.5 1 4 C1 8 2 1 1 1
There are 81 cages and each 20 individuals whose survival was followed
over time. The columns S,F,G,L and S are experimentally manipulated
factors thought to have an influence on survival.
Using survfit(Surv(deathtime,status)~cage) gives me the survivorship
curves for every cage. But what I´d like to have is a mean survivorship
value for every cage.
Obviously, using tapply (deathtime,cage,mean) gives me mean values, but
I´d like to have a better estimate of this using a proper statistical
model. I´ve tried a glm with poisson errors (as suggested in Crawley´s
book, page 628), but the back-transformed estimates (using status as the
response variable and deathtime as an offset) were totally unrealistic.
As I´m new to survival analysis, it would be great if anyone could give
me some hints on what method would be best.
No method is best, but some methods may be useful ;) One such may be
to fit a parametric model to your data. Check 'survreg'.
Göran
Thanks a lot!
Christoph
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
--
Göran Broström
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
