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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