Hi
I'm encountering some problems with coxme
My data:
I'm looking at the survival of animals in an experiment with 3 treatments,
which came from 4 different populations, two of which were infected with a
parasite and two of which were not. I'm interested if infected animals
differe from uninfected ones across treatments.
Factor 1: treatment (3 levels)
Factor 2: infection state (infected/uninfected)
Random effect 1: (population nested within infection state)
modelling this with
m<-coxme(Surv(day,status)~condition*infection+(1|infection/population),data=all)
gives me the following
Cox mixed-effects model fit by maximum likelihood
Data: all
events, n = 476, 720
Iterations= 7 53
NULL Integrated Fitted
Log-likelihood -2915.527 -2641.427 -2634.182
Chisq df p AIC BIC
Integrated loglik 548.20 7.00 0 534.20 505.04
Penalized loglik 562.69 6.96 0 548.78 519.80
Model: Surv(day, status) ~ condition * infestation + (1 |
infestation/population1)
Fixed coefficients
coef exp(coef) se(coef)
z p
conditionstarved 3.3960657 29.8464431 0.3228277
10.52 0.0000
conditionwater 3.3277968 27.8768547 0.3224368
10.32 0.0000
infestationinfestationyes 1.5596539 4.7571747 0.7254405
2.15 0.0320
conditionstarved:infestationinfestationyes -1.1100987 0.3295264 0.3712690
-2.99 0.0028
conditionwater:infestationinfestationyes -0.9150922 0.4004797 0.3709914
-2.47 0.0140
Random effects
Group Variable Std Dev Variance
infestation/population1 (Intercept) 0.6367618042 0.4054655953
infestation (Intercept) 0.0199767654 0.0003990712
To assess if the interaction is needed I would normally do a model
simplification
m1<-update(m,~.-condition:infection)
however, this gives me
error in formula.default(object, env = baseenv()) : invalid formula
I do not encounter this problem without a random effect in coxph. So my
question is
(1)Is it not possible to do model simplification with coxme?
(2)Is there another way to assess an overall significant interaction with coxme?
Thanks in advance
Simon
Simon Tragust
Animal Ecology I
NW I
University of Bayreuth
D-95440 Bayreuth
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