micro.censure | R Documentation |
This dataset provides Microsat specifications and survival times.
A data frame with 117 observations on the following 43 variables.
a factor with levels B1006
B1017
B1028
B1031
B1046
B1059
B1068
B1071
B1102
B1115
B1124
B1139
B1157
B1161
B1164
B1188
B1190
B1192
B1203
B1211
B1221
B1225
B1226
B1227
B1237
B1251
B1258
B1266
B1271
B1282
B1284
B1285
B1286
B1287
B1290
B1292
B1298
B1302
B1304
B1310
B1319
B1327
B1353
B1357
B1363
B1368
B1372
B1373
B1379
B1388
B1392
B1397
B1403
B1418
B1421t1
B1421t2
B1448
B1451
B1455
B1460
B1462
B1466
B1469
B1493
B1500
B1502
B1519
B1523
B1529
B1530
B1544
B1548
B500
B532
B550
B558
B563
B582
B605
B609
B634
B652
B667
B679
B701
B722
B728
B731
B736
B739
B744
B766
B771
B777
B788
B800
B836
B838
B841
B848
B871
B873
B883
B889
B912
B924
B925
B927
B938
B952
B954
B955
B968
B972
B976
B982
B984
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a
factor with levels 0
1
2
3
4
a numeric vector
a numeric vector
Allelotyping identification of genomic alterations in rectal chromosomally unstable tumors without preoperative treatment, #' Benoît Romain, Agnès Neuville, Nicolas Meyer, Cécile Brigand, Serge Rohr, Anne Schneider, Marie-Pierre Gaub and Dominique Guenot, BMC Cancer 2010, 10:561, doi:10.1186/1471-2407-10-561.
plsRcox, Cox-Models in a high dimensional setting in R, Frederic
Bertrand, Philippe Bastien, Nicolas Meyer and Myriam Maumy-Bertrand (2014).
Proceedings of User2014!, Los Angeles, page 152.
Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data, Philippe Bastien, Frederic Bertrand, Nicolas Meyer and Myriam Maumy-Bertrand (2015), Bioinformatics, 31(3):397-404, doi:10.1093/bioinformatics/btu660.
data(micro.censure) Y_train_micro <- micro.censure$survyear[1:80] C_train_micro <- micro.censure$DC[1:80] Y_test_micro <- micro.censure$survyear[81:117] C_test_micro <- micro.censure$DC[81:117] rm(Y_train_micro,C_train_micro,Y_test_micro,C_test_micro)
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