Dear SPLUS users
I am estimating coxph with frailty. When I don't introduce the frailty
term, the model takes some time to converge, but it finally does (Model
1). Nervertheless, when I introduce the frailty term I obtain the output
at the end of the message (Model 2). Could (please) anyone explain me what
this result means? Is it a convergence problem? Am I doing anything wrong?
I have tried to modify the number of iterations in "outer.max" and
"iter.max", but I do not get any different result. I have also come to the
same results with simpler versions of Model 1 with frailty when I fix the
variance of the gamma distribution (in these cases the model offered the
usual output if I did not fix the gamma variance).
Thanks a lot.
Jaime Gómez.
Model 1. MODEL WITHOUT THE FRAILTY TERM
coxph(formula = Surv(ANOI, ANOF, CENSURA) ~ log(SIZE) + INTER + PROFI +
PROXIC + PROXIE + DENSI2 + MGROW2 + DEPHA + NUMSB + CMOBC + NMARK + (
NMARK2) + MMKC + (MMKC^2) + SYMM, data = datos, na.action =
na.omit, iter.max = 1000, method = "efron")
n= 34529
coef exp(coef) se(coef) z p
log(SIZE) 1.26096 3.53e+000 0.12694 9.9335 0.0e+000
INTER 3.09687 2.21e+001 1.41465 2.1891 2.9e-002
PROFI -0.00486 9.95e-001 0.14586 -0.0333 9.7e-001
PROXIC 2.14637 8.55e+000 0.20287 10.5802 0.0e+000
PROXIE 1.70102 5.48e+000 0.24168 7.0382 1.9e-012
DENSI2 0.17147 1.19e+000 0.55422 0.3094 7.6e-001
MGROW2 0.25917 1.30e+000 2.56358 0.1011 9.2e-001
DEPHA 0.04178 1.04e+000 0.47238 0.0884 9.3e-001
NUMSB -0.11842 8.88e-001 0.02586 -4.5798 4.7e-006
CMOBC -13.42520 1.48e-006 2.97066 -4.5193 6.2e-006
NMARK 0.02665 1.03e+000 0.04498 0.5924 5.5e-001
NMARK2 -0.07864 9.24e-001 0.08140 -0.9662 3.3e-001
MMKC 0.14606 1.16e+000 0.07524 1.9414 5.2e-002
I(MMKC^2) -0.00550 9.95e-001 0.00385 -1.4279 1.5e-001
SYMM 2.06643 7.90e+000 0.47051 4.3919 1.1e-005
Rsquare= 0.025 (max possible= 0.083 )
Likelihood ratio test= 886 on 15 df, p=0
Wald test = 800 on 15 df, p=0
Score (logrank) test = 3055 on 15 df, p=0
Model 2. EXAMPLE OF AN ESTIMATION WITH THE FRAILTY TERM
coxph(formula = Surv(ANOI, ANOF, CENSURA) ~ log(SIZE) + INTER + PROFI +
PROXIC + PROXIE + DENSI2 + MGROW2 + DEPHA + NUMSB + CMOBC + NMARK + (
+ NMARK2) + MMKC + (MMKC^2) + SYMM + frailty(NCAJA), data = datos,
na.action = na.omit, iter.max = 1000, method = "efron")
Call:
coxph(formula = Surv(ANOI, ANOF, CENSURA) ~ log(SIZE) + INTER + PROFI +
PROXIC + PROXIE + DENSI2 + MGROW2 + DEPHA + NUMSB + CMOBC + NMARK +
(
NMARK2) + MMKC + (MMKC^2) + SYMM + frailty(NCAJA), data = datos,
na.action = na.omit, iter.max = 1000, method = "efron")
coef se(coef) se2 Chisq DF p
log(SIZE) 2.29e-299 0.1202 0.0773 0 1.0 1.0e+000
INTER 1.15e-299 1.5005 1.3453 0 1.0 1.0e+000
PROFI 3.65e-300 0.1742 0.1651 0 1.0 1.0e+000
PROXIC 1.15e-299 0.2622 0.2616 0 1.0 1.0e+000
PROXIE 2.05e-301 0.3875 0.3857 0 1.0 1.0e+000
DENSI2 1.04e-300 0.6089 0.6088 0 1.0 1.0e+000
MGROW2 -5.80e-299 2.4184 2.4180 0 1.0 1.0e+000
DEPHA 3.85e-300 0.4242 0.4238 0 1.0 1.0e+000
NUMSB -2.93e-300 0.0241 0.0241 0 1.0 1.0e+000
CMOBC 7.14e-299 3.1358 2.0606 0 1.0 1.0e+000
NMARK -6.46e-300 0.0844 0.0721 0 1.0 1.0e+000
NMARK2 2.25e-299 0.2453 0.2238 0 1.0 1.0e+000
MMKC 8.49e-300 0.1601 0.1523 0 1.0 1.0e+000
I(MMKC^2) -5.69e-301 0.0137 0.0134 0 1.0 1.0e+000
SYMM 4.61e-299 0.6364 0.6107 0 1.0 1.0e+000
frailty(NCAJA) 184 52.6 2.2e-016
Iterations: 10 outer, 10000 Newton-Raphson
Variance of random effect= 0.992 EM likelihood = -1417.8
Degrees of freedom for terms= 0.4 0.8 0.9 1.0 1.0 1.0 1.0 1.0 1.0
0.4 0.7 0.8 0.9 1.0 0.9 52.6
Likelihood ratio test=268 on 65.44 df, p=0 n= 34529
Warning messages:
Inner loop failed to coverge for iterations 1 2 3 4 5 6 7 8 9 10 in:
coxpenal.fit(X, Y, strats, offset, init = init, iter.max = iter.max,
outer.max = outer.max, eps = eps, toler.chol = toler.chol, weights =
...
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