Hi,
I am running the following model:
fit1.full <- coxme(Surv(age_sym1, sym1) ~ sex + lifedxm*sex + (1|famid),
data=bip.surv)
I would like to extract the AIC from that object to calculate the AICC.
However, when I look at str(fit1.full) and summary(fit1.full) (pasted
below) I don't see anything that would allow me to get pull the AIC out
from that object.
Is there a way to retrieve the AIC from a coxme object?
Please cc me on your response.
Thanks,
Chris
1> str(fit1.full)
List of 20
$ coefficients :List of 2
..$ fixed : Named num [1:5] 0.197 -1.132 -0.499 0.114 -0.329
.. ..- attr(*, "names")= chr [1:5] "sexMALES"
"lifedxmCONTROL"
"lifedxmMAJOR" "sexMALES:lifedxmCONTROL" ...
..$ random:List of 1
.. ..$ famid: Named num 0.964
.. .. ..- attr(*, "names")= chr "Intercept"
$ frail :List of 1
..$ famid: Named num [1:97] -0.8144 -0.6222 -0.0979 0.5179 -0.5587 ...
.. ..- attr(*, "names")= chr [1:97] "1" "2"
"3" "4" ...
$ penalty : num 22.6
$ loglik : Named num [1:3] -479 -467 -435
..- attr(*, "names")= chr [1:3] "NULL"
"Integrated" "Penalized"
$ variance :Formal class 'bdsmatrix' [package
"bdsmatrix"] with
6 slots
.. ..@ blocksize: int [1:97] 1 1 1 1 1 1 1 1 1 1 ...
.. ..@ blocks : num [1:97] 0.545 0.606 0.485 0.415 0.636 ...
.. ..@ rmat : num [1:102, 1:5] -0.00096 -0.000778 -0.000286
0.000102 -0.000688 ...
.. ..@ offdiag : num 0
.. ..@ Dim : int [1:2] 102 102
.. ..@ Dimnames :List of 2
.. .. ..$ : NULL
.. .. ..$ : NULL
$ df : num [1:2] 6 49.3
$ hmat :Formal class 'gchol.bdsmatrix' [package
"bdsmatrix"]
with 6 slots
.. ..@ blocksize: int [1:97] 1 1 1 1 1 1 1 1 1 1 ...
.. ..@ blocks : num [1:97] 1.87 1.68 2.12 2.47 1.61 ...
.. ..@ rmat : num [1:102, 1:5] -0.1885 0.0659 -0.2205 -0.0816
-0.1502 ...
.. ..@ rank : int 102
.. ..@ Dim : int [1:2] 102 102
.. ..@ Dimnames :List of 2
.. .. ..$ : NULL
.. .. ..$ : NULL
$ iter : num [1:2] 10 54
$ control :List of 6
..$ eps : num 1e-08
..$ toler.chol : num 1.82e-12
..$ iter.max : num 20
..$ inner.iter : num 5
..$ sparse.calc: num 1
..$ optpar :List of 2
.. ..$ method : chr "BFGS"
.. ..$ control:List of 1
.. .. ..$ reltol: num 1e-05
$ u : num [1:102] 2.70e-05 2.02e-05 1.48e-05 -1.61e-05
1.78e-05 ...
$ means : num [1:5] 0.45 0.307 0.444 0.148 0.206
$ scale : num [1:5] 0.495 0.425 0.494 0.252 0.328
$ linear.predictor: num [1:189] -1.313 -1.313 -1.754 -1.443 -0.597 ...
$ n : num [1:2] 99 189
$ terms :Classes 'terms', 'formula' length 3
Surv(age_sym1,
sym1) ~ sex + lifedxm * sex
.. ..- attr(*, "variables")= language list(Surv(age_sym1, sym1),
sex,
lifedxm)
.. ..- attr(*, "factors")= int [1:3, 1:3] 0 1 0 0 0 1 0 1 1
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:3] "Surv(age_sym1, sym1)" "sex"
"lifedxm"
.. .. .. ..$ : chr [1:3] "sex" "lifedxm"
"sex:lifedxm"
.. ..- attr(*, "term.labels")= chr [1:3] "sex"
"lifedxm" "sex:lifedxm"
.. ..- attr(*, "specials")=Dotted pair list of 2
.. .. ..$ strata : NULL
.. .. ..$ cluster: NULL
.. ..- attr(*, "order")= int [1:3] 1 1 2
.. ..- attr(*, "intercept")= num 1
.. ..- attr(*, "response")= int 1
.. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
$ formulaList :List of 2
..$ fixed :Class 'formula' length 3 Surv(age_sym1, sym1) ~ sex +
lifedxm * sex
.. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
..$ random:List of 1
.. ..$ : language, mode "(": (1 | famid)
$ na.action :Class 'omit' Named int [1:3] 27 101 102
.. ..- attr(*, "names")= chr [1:3] "27" "101"
"102"
$ y : Surv [1:189, 1:2] 16.13+ 19.33+ 16.55+ 19.37+ 5.77
21.51 6.18 10.47 16.46+ 19.95+ ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:189] "1" "2" "3" "4"
...
.. ..$ : chr [1:2] "time" "status"
..- attr(*, "type")= chr "right"
$ call : language coxme(formula = Surv(age_sym1, sym1) ~ sex
+ lifedxm * sex + (1 | famid), data = bip.surv)
$ ties : chr "efron"
- attr(*, "class")= chr "coxme"
1> summary(fit1.full)
Length Class Mode
coefficients 2 -none- list
frail 1 -none- list
penalty 1 -none- numeric
loglik 3 -none- numeric
variance 1 bdsmatrix S4
df 2 -none- numeric
hmat 1 gchol.bdsmatrix S4
iter 2 -none- numeric
control 6 -none- list
u 102 -none- numeric
means 5 -none- numeric
scale 5 -none- numeric
linear.predictor 189 -none- numeric
n 2 -none- numeric
terms 3 terms call
formulaList 2 -none- list
na.action 3 omit numeric
y 378 Surv numeric
call 3 -none- call
ties 1 -none- character
--
Christopher David Desjardins
PhD student, Quantitative Methods in Education
MS student, Statistics
University of Minnesota
192 Education Sciences Building
http://cddesjardins.wordpress.com
