Hello: I am new to R Help so hopefully I will have included enough
information to allow for valuable feedback on an error message I am
receiving. I am trying to a fit a mixed effects cox model to binary elk
telemetry data to look at movement decisions relative to other
possibilities (specifically selection or avoidance of risk <- kauf_avg ) in
a matched-case-control framework.
> sessionInfo()
R version 2.15.1 (2012-06-22)
Platform: x86_64-pc-mingw32/x64 (64-bit)
locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United
States.1252
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] splines stats graphics grDevices utils datasets methods
base
other attached packages:
[1] coxme_2.2-3 nlme_3.1-104 bdsmatrix_1.3-1 survival_2.36-14
AICcmodavg_1.28 lme4_0.999999-0
[7] Matrix_1.0-6 lattice_0.20-6 plotrix_3.4-5
loaded via a namespace (and not attached):
[1] grid_2.15.1 stats4_2.15.1 tools_2.15.1
I have included a snapshot of the dataset below. The columns represents
Animal ID (ID), Record # (Record), Location groupings (Strata), Use versus
possible location (Used = 1), Variable of interest (kauf_avg), categorical
data (Dummy_Age; Dummy_Time), and required time variable (faketime).
FID_ ID Record Strata Used kauf_avg Dummy_Age Dummy_Time
faketime
1 55 2 1 1 0.710135 3
2 1
2 55 2 1 0 0.555335 3
2 2
3 55 2 1 0 0.710135 3
2 2
4 55 2 1 0 2.100059 3
2 2
5 55 3 2 1 0.569817 3
3 1
6 55 3 2 0 0.558852 3
3 2
7 55 3 2 0 1.331789 3
3 2
8 55 3 2 0 2.306629 3
3 2
9 55 4 3 1 0.569817 3
1 1
10 55 4 3 0 0.616777 3
1 2
11 55 4 3 0 0.737392 3
1 2
12 55 4 3 0 1.637539 3
1 2
13 55 5 4 1 0.548600 3
2 1
14 55 5 4 0 0.222890 3
2 2
15 55 5 4 0 0.598823 3
2 2
> str(data)
'data.frame': 57195 obs. of 9 variables:
$ FID_ : int 1 2 3 4 5 6 7 8 9 10 ...
$ ID : int 55 55 55 55 55 55 55 55 55 55 ...
$ Record : int 2 2 2 2 3 3 3 3 4 4 ...
$ Strata : int 1 1 1 1 2 2 2 2 3 3 ...
$ Used : int 1 0 0 0 1 0 0 0 1 0 ...
$ kauf_avg : num 0.71 0.555 0.71 2.1 0.57 ...
$ Dummy_Age : Factor w/ 4 levels "1","2","3","4": 3 3 3 3 3 3 3 3 3 3 ...
$ Dummy_Time: Factor w/ 3 levels "1","2","3": 2 2 2 2 3 3 3 3 1 1 ...
$ faketime : num 1 2 2 2 1 2 2 2 1 2 ...
The faketime column is built using the following code:
faketime <- rep(1, times = nrow(mccallrisk))
faketime[mccallrisk$Used == 0] <- 2 #2 for control, 1 for case
data <- cbind(mccallrisk, faketime)
rm(faketime)
When I run the analysis code for a univariate model I have no issues:
mcc.risk<-coxme(Surv(faketime,Used)~ kauf_avg +
(1|ID)+strata(Strata),data=data)
mcc.risk
Cox mixed-effects model fit by maximum likelihood
Data: data
events, n = 14344, 57195
Iterations= 1 5
NULL Integrated Fitted
Log-likelihood -19781.73 -19739.71 -19739.71
Chisq df p AIC BIC
Integrated loglik 84.05 2 0 80.05 64.91
Penalized loglik 84.05 1 0 82.05 74.48
Model: Surv(faketime, Used) ~ kauf_avg + (1 | ID) + strata(Strata)
Fixed coefficients
coef exp(coef) se(coef) z
p
kauf_avg 0.0490237 1.050245 0.005330567 9.2 0
Random effects
Group Variable Std Dev Variance
ID Intercept 2e-02 4e-04
However, when I include a second variable the following error message
follows.
> mcc.risk_time<-coxme(Surv(faketime,Used)~ kauf_avg + Dummy_Time +
(1|ID)+strata(Strata),data=data)
Error in logfun(as.numeric(testvals[i, ]), varlist, vparm, kfun, ntheta, :
NA/NaN/Inf in foreign function call (arg 2)
I have removed all NA values from kauf_avg and neither Time or Age have NA
values.
The only information I can seem to find relevant to this error message is
the following (I have contacted this person and am waiting to hear back but
I thought I might post on here to see if anyone else had additional
thoughts/comments):
Feb 14, 2013; 7:49amRe: NA/NaN/Inf in foreign function call (arg 6) error
from coxph function
The NaN/Inf message is almost certainly from a singular fit. I would be
interested in a copy of the data set to test it: data sets that cause this are
rare, and help me to tune the convergence/signularity criteria in the
program. Note: it is much easier to use coxph(survobj ~ therapy +
ReceptorA + ReceptorB, data=sample.data) than to put "sample.data$" in
front of every variable name; and easier to read as well.
Terry Therneau (author of coxph function)
Thank you,
--
Michel Kohl
<><><><><><><><><><>
Ph.D. Student
Department of Wildland Resources
Utah State University
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