Hi,
I'm trying to use the R Survival analysis on a windows 7 system.
The input data format is described at the end of this mail.
1/ I tried to perform a survival analysis including stratified variables
using the following formula.
cox.xtab_miR=coxph(Surv(time, status) ~ miR + strata(sex,nbligne, age),
data=matrix)
and obtain the following error message
Warning message:
In fitter(X, Y, strats, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
Is this due to the model (error in formula) or is the number of
stratified variables fixed?
2/ I wanted to used the predict information to create a survival model
based on the same parameter as previously described - (Surv(time,
status) ~ miR + strata(sex,nbligne, age) - and than testing it on the
predict set of data.. I there any way to do that? Is there any way to
plot this?
Many thanks for your help.
Kind regards
/ Sandrine
Input data
> matrix
$time
[1] 58.14 17.10 14.71 17.43 16.00 7.00 7.50 8.00 8.00 9.85 20.00
9.14
[13] 3.85 5.00 4.00 13.00 15.71 32.00 33.00 8.00 36.00 8.00 42.00 31.43
[25] 29.71 80.00 25.14 40.00 25.14 30.00 12.00 10.71 28.00 4.57 9.00 15.71
[37] 6.85 39.85 6.57 42.00 16.28 14.00 6.71 12.57 65.14 35.28 33.85 52.00
[49] 24.57 32.71 7.28 70.00 7.28 6.71 23.00 10.00 7.14 19.86 55.42 40.28
[61] 21.28 31.14 34.00 44.00 7.28 70.71 7.57 44.85 56.00 21.14 91.71
$status
[1] 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1
0 1 0
[26] 0 0 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1
1 1 0
[51] 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0
$miR
[1] 836.12000 458.64000 442.03000 125.47000 2914.44000
[6] 114.53000 8091.10000 2645.75000 3269.46000 1168.67000
[11] 3541.42000 508.79000 177.34000 4705.90000 6677.30000
[16] 4223.73000 787.69265 415.92000 100.57000 10.58000
[21] 42.78000 29.36000 13.47000 1.00000 13.08000
[26] 67.89000 16.09000 49.01000 75.78000 26.36000
[31] 6.36000 31.64173 218.00000 15699.17000 3215.71000
[36] 294.98000 106678.83000 554.79000 198.53000 161.64000
[41] 2481.18283 1093.70000 5496.18000 144.28000 12.50000
[46] 79.49000 45.64000 31.07000 8.12000 15.27000
[51] 15.65780 18.00000 2172.67000 345.35000 3256.34000
[56] 3332.58000 296.32000 7889.62000 936.50000 458.60000
[61] 1212.32000 2762.48000 1047.78000 3193.43000 319.27000
[66] 16.29000 69.31000 84.11000 35.43000 4.98000
[71] 41.75000
$nbligne
[1] 1 2 3 3 2 4 2 3 2 3 3 2 3 4 3 2 2 2 2 3 3 3
2 2 NA
[26] 2 2 2 3 4 5 2 1 2 2 3 2 2 3 3 3 2 2 1 6 3 2
1 2 2
[51] 1 2 2 2 2 2 2 2 2 2 1 2 2 2 1 2 2 2 2 2 2
$sex
[1] 1 2 1 1 1 2 1 2 1 1 1 2 1 1 1 2 2 1 1 1 1 2
2 1 1
[26] 1 1 1 1 1 1 2 1 2 2 1 1 2 2 1 2 1 1 1 1 1 2
1 2 2
[51] 1 1 1 2 1 1 1 1 1 1 2 1 2 1 2 1 1 1 2 2 2
$age
[1] 77.00 64.00 50.00 57.00 60.00 53.00 51.00 57.69 53.50 77.37 65.39
78.10
[13] 51.38 73.22 57.33 53.39 44.68 55.94 58.00 47.00 62.00 48.00 55.00 55.00
[25] 56.00 22.00 77.00 56.09 65.63 48.00 61.00 73.99 68.71 47.34 71.60 61.16
[37] 77.94 73.21 66.45 67.69 48.84 70.96 74.43 65.41 64.73 77.87 58.70 65.00
[49] 73.34 75.64 87.86 57.23 60.43 68.27 64.57 51.12 61.91 60.12 84.54 53.99
[61] 33.88 75.95 43.29 67.38 67.80 66.30 52.36 58.81 69.35 80.47 54.38
$predict
[1] TRAINING TRAINING TRAINING TRAINING TRAINING TRAINING TRAINING
TRAINING
[9] TRAINING TRAINING TRAINING PREDICT PREDICT PREDICT PREDICT
PREDICT
[17] PREDICT PREDICT TRAINING TRAINING TRAINING TRAINING TRAINING TRAINING
[25] TRAINING TRAINING TRAINING TRAINING TRAINING PREDICT PREDICT PREDICT
[33] PREDICT PREDICT PREDICT PREDICT PREDICT PREDICT PREDICT PREDICT
[41] PREDICT PREDICT PREDICT PREDICT PREDICT PREDICT PREDICT PREDICT
[49] PREDICT PREDICT PREDICT PREDICT PREDICT PREDICT PREDICT PREDICT
[57] PREDICT PREDICT PREDICT PREDICT PREDICT PREDICT PREDICT PREDICT
[65] PREDICT PREDICT PREDICT PREDICT PREDICT PREDICT PREDICT
Levels: PREDICT TRAINING
--
Sandrine Imbeaud
INSERM, UMR U-674, IUH
Université Paris Descartes
Génomique Fonctionnelle des tumeurs solides
27 rue Juliette Dodu
F75010 Paris, France
TEL: +33 (0)1 53 72 51 98
FAX: +33 (0)1 53 72 51 92
MOBILE: +33 (0)6 12 69 80 29
http://www.inserm-u674.net/
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