I've attached a sample of the data sets, this is my full code.
#R 2.13
#library(survival)
#library(Hmisc)
#library(splines)
library(rms)
train<-as.data.frame( train<-read.csv("G:\\train.txt", header=T, sep="\t"))
test<-as.data.frame( test<-read.table("G:\\test.txt", header=T, sep="\t"))
f.1<-cph(Surv(time,event)~age, x=T, y=T,surv=T, data=train)
val.surv(f.1, newdata=test, u=10)
#plot(calibrate(f.1, u=30, B=20))
________________________________
From: David Winsemius <dwinsem...@comcast.net>
To: Salvo Mac <salvo_...@yahoo.com>
Cc: "r-help@R-project.org" <r-help@r-project.org>
Sent: Sunday, August 21, 2011 5:29 AM
Subject: Re: [R] val.surv
On Aug 20, 2011, at 10:25 PM, Salvo Mac wrote:
> The test and train are like split data sets, contain similar variables but
> from different countries so the two sets are somehow independent. And yes it
> is a data frame.
What is a data.frame? test and train may be dataframes, but test[, "age"] is
not a dataframe.
> So I extracted age, time and event.
Code? The code you offered before would have created a newdata object (yes, a
data.frame) with a single column bearing the same name as the vector argument
,,,,, "test1". Not named "age". Try it. do str on such an object:
str(dataframe(test1))
> So test is data frame,(age, time, event). does that suffice?
It certainly does not allow me to reproduce the error you got (which I still
think is probably related to the structure of your argument to newdata.)
That's all I can say without data and code.
--David.
>
>
>
> ________________________________
> From: David Winsemius <dwinsem...@comcast.net>
>
> Cc: "r-help@R-project.org" <r-help@r-project.org>
> Sent: Sunday, August 21, 2011 3:19 AM
> Subject: Re: [R] val.surv
>
>
> On Aug 20, 2011, at 8:08 PM, Salvo Mac wrote:
>
>> Thanks David
>>
>> However, I tried your trick on val.surv with newdata=test['age'] but still
>> didn't work.
>> Still gives the same error message:
>>
>> Error in val.surv(f.1, newdata = test1["age"], u = 10) :
>> dims [product 1797] do not match the length of object [2496]
>> In addition: Warning message:
>> In est.surv + S[, 1] :
>> longer object length is not a multiple of shorter object length
>>
>
> As I said (and you did
> not act upon):
>
> The fundamental thing you are doing wrong for q1 is failing to unambiguously
> describe the test object.
>
> I said it was a guess. Now stop wasting our time and offer what is needed.
>
> --david.
>>
>> Salvo
>> ________________________________
>> From: David Winsemius <dwinsem...@comcast.net>
>>
>> Cc: "r-help@R-project.org" <r-help@r-project.org>
>> Sent: Sunday, August 21, 2011 12:55 AM
>> Subject: Re: [R] val.surv
>>
>>
>> On Aug 20, 2011, at 3:32 PM, Salvo Mac wrote:
>>
>>> Dear R-users,
>>>
>>> I have two questions regarding
> validation and calibration of Survival regression models.
>>>
>>> 1. I am trying to calibrate and validate a cox model using val.surv.
>>> here is my code:
>>> f.1<-cph(Surv(time,event)~age, x=T, y=T, data=train)
>>> test1<-test[,"age"]
>>> val.surv(f.1, newdata=data.frame(test1), u=10)
>>>
>>> but I get an error message:
>>>
>>> Error in val.surv(f.1, newdata = data.frame(testi), u = 10) :
>>> dims [product 1797] do not match the length of object [2496]
>>> In addition: Warning message:
>>> In est.surv + S[, 1] :
>>> longer object length is not a multiple of shorter object length
>>>
>>> I ran the example in the r-documentation but couldn't extract dxy from
>>>result.
>>>
>>> What am I doing wrong?
>>
>> The fundamental thing you are doing wrong for q1 is failing to
>> unambiguously describe the test object. I would think that if test were a
>> dataframe then wrapping data.frame around a vector might not get it named
>> correctly as 'age'. You might try newdata= test['age']. Just a guess.
>>
>>>
>>> 2. In validate and calibrate cph functions. If it is frailty fit, does
>>>the the bootstrap resample clusters or just individuals
>>
>> The code above appears to be dependent on the rms package. The frailty
>> function is part of the underlying survival package and I do not see it
>> mentioned in the index for Harrell's RMS text. You will probably need to
>> wait until Frank comes across this. He is generally very good about
>> correction my errors and knowledge gaps.
>>
>>>
>>
David Winsemius, MD
West Hartford, CT
id age weight gender time event
1 32 14 0 183 0
3 56 9 0 12 1
4 42 5 0 183 0
5 79 13 0 4 1
6 30 3 0 183 0
7 19 10 0 183 0
8 18 12 0 3 1
9 26 14 0 183 0
10 24 14 0 1 1
11 35 6 0 183 0
12 78 14 0 2 1
13 22 7 0 183 0
14 48 5 0 183 0
15 49 13 0 183 0
16 61 5 0 183 0
19 82 14 0 6 1
20 22 10 0 183 0
21 32 4 1 183 0
22 38 9 1 6 1
23 37 3 1 183 0
24 30 13 1 183 0
25 83 14 0 183 0
26 42 14 0 9 1
27 24 8 0 183 0
29 24 14 0 183 0
32 21 14 0 183 0
33 34 3 0 183 0
35 87 13 0 183 0
36 27 14 0 8 1
37 34 3 1 183 0
38 86 13 0 183 0
39 20 5 0 9 1
40 66 9 0 183 0
41 89 13 0 183 0
42 79 8 1 183 0
43 40 7 0 2 1
44 35 3 1 183 0
45 31 14 0 183 0
46 33 9 0 183 0
47 38 10 0 4 1
49 23 7 1 183 0
50 28 14 0 183 0
51 22 13 0 183 0
52 28 3 0 5 1
53 71 3 0 183 0
54 20 5 1 183 0
55 42 14 0 8 1
56 44 14 0 183 0
57 22 8 0 183 0
58 32 4 0 183 0
59 74 5 0 1 1
60 63 12 0 183 0
62 34 3 0 183 0
63 60 14 0 3 1
64 21 12 0 183 0
65 48 6 0 183 0
66 34 4 0 1 1
71 26 3 0 183 0
72 48 14 0 183 0
73 32 13 0 183 0
74 16 11 0 183 0
75 16 4 0 183 0
76 36 5 0 183 0
78 56 11 0 183 0
79 22 8 0 183 0
80 81 14 0 9 1
81 32 12 0 183 0
82 30 3 1 183 0
83 34 11 0 183 0
84 81 3 0 1 1
85 39 14 0 183 0
86 21 13 0 183 0
87 70 14 0 183 0
88 20 10 0 183 0
90 36 3 0 183 0
91 21 9 0 183 0
92 76 9 0 13 1
93 55 12 0 183 0
95 41 8 0 183 0
96 40 11 0 183 0
97 23 4 0 183 0
98 38 3 1 183 0
99 35 3 1 183 0
101 40 12 0 183 0
102 30 3 1 183 0
103 16 3 0 183 0
104 18 11 0 183 0
105 65 14 0 183 0
107 24 3 0 183 0
109 51 14 0 183 0
110 31 13 0 183 0
111 58 13 1 7 1
112 40 3 0 183 0
113 22 3 0 183 0
114 20 12 0 183 0
116 27 7 0 183 0
117 27 14 0 183 0
118 19 10 0 8 1
119 20 13 0 183 0
124 38 9 0 183 0
127 24 12 0 183 0
128 35 13 0 6 1
129 32 4 0 183 0
132 86 10 0 183 0
134 39 11 0 6 1
136 45 12 0 183 0
138 20 3 0 183 0
142 71 12 0 6 1
143 39 12 0 183 0
146 42 9 0 183 0
148 63 3 0 183 0
149 53 12 0 183 0
150 24 11 0 6 1
152 30 10 0 183 0
153 43 11 0 183 0
154 59 10 0 183 0
156 50 11 0 183 0
159 19 7 0 183 0
162 20 11 0 183 0
165 48 5 1 3 1
167 32 13 0 183 0
169 30 7 0 183 0
173 31 12 0 183 0
174 29 10 0 183 0
175 72 7 0 6 1
176 19 10 0 183 0
178 30 9 0 183 0
180 26 3 0 183 0
182 26 12 0 1 1
183 22 10 0 183 0
184 36 11 0 183 0
185 32 11 0 183 0
186 59 5 1 183 0
192 64 6 0 21 1
196 22 13 0 183 0
198 45 3 0 183 0
199 45 12 0 183 0
201 18 10 0 8 1
203 77 10 0 183 0
205 29 11 0 183 0
210 22 10 0 183 0
211 58 11 0 183 0
212 72 13 0 183 0
214 47 10 0 183 0
219 32 13 0 183 0
220 34 3 1 4 1
221 22 10 0 183 0
222 35 8 0 183 0
223 42 10 0 183 0
224 25 12 0 7 1
226 16 10 0 183 0
228 24 10 0 183 0
229 62 6 0 183 0
233 39 9 0 183 0
234 22 11 0 183 0
239 17 3 0 183 0
245 25 9 0 183 0
247 23 10 0 2 1
250 35 8 0 183 0
258 76 6 0 183 0
260 25 12 0 6 1
262 26 8 0 183 0
264 35 10 0 183 0
265 48 9 0 183 0
266 50 11 0 183 0
269 63 13 0 183 0
279 31 11 0 183 0
281 49 3 1 1 1
289 63 10 0 183 0
290 22 8 0 183 0
292 23 13 0 183 0
294 21 8 0 1 1
299 60 10 0 183 0
308 66 10 0 183 0
309 40 12 0 4 1
311 81 13 0 183 0
312 22 11 0 183 0
321 23 11 0 2 1
322 32 13 0 183 0
323 38 11 0 183 0
326 21 8 0 183 0
328 25 11 0 183 0
329 86 13 0 2 1
331 26 3 0 183 0
343 59 11 0 183 0
346 56 8 0 183 0
347 43 11 0 3 1
348 43 12 0 183 0
349 16 11 0 183 0
350 39 12 0 2 1
353 18 10 0 183 0
354 54 11 0 183 0
355 54 10 0 32 1
357 45 5 0 183 0
358 77 9 0 183 0
362 50 11 0 183 0
363 52 9 0 183 0
366 33 12 0 183 0
367 47 11 0 183 0
368 41 10 0 183 0
372 65 12 0 7 1
373 36 5 0 183 0
375 44 11 0 183 0
379 27 12 0 183 0
381 40 13 0 183 0
391 46 11 0 183 0
394 55 8 0 12 1
395 47 13 0 183 0
396 29 3 1 183 0
401 71 11 0 183 0
403 21 6 0 183 0
410 41 10 0 183 0
411 52 3 1 11 1
418 23 5 0 183 0
422 60 3 1 183 0
423 20 5 1 183 0
424 23 11 0 183 0
425 25 9 1 183 0
426 52 12 0 183 0
428 48 10 0 183 0
429 43 3 1 183 0
430 32 5 1 183 0
434 29 3 1 183 0
436 35 3 1 2 1
444 47 10 0 183 0
445 25 10 0 183 0
449 24 8 1 183 0
450 57 11 0 183 0
451 19 12 0 183 0
453 48 11 0 183 0
454 26 12 0 1 1
455 22 11 0 183 0
457 19 11 0 183 0
459 51 3 1 183 0
462 54 11 0 12 1
466 30 6 1 183 0
467 58 13 0 183 0
469 23 13 0 183 0
471 64 11 0 183 0
473 29 3 1 2 1
474 24 11 0 183 0
477 25 11 0 183 0
492 52 5 1 183 0
497 21 5 0 32 1
498 22 13 0 183 0
499 40 3 0 183 0
502 37 11 0 183 0
506 40 6 0 183 0
508 23 5 0 12 1
510 21 10 0 183 0
511 32 11 0 183 0
512 43 10 0 183 0
514 66 10 0 183 0
516 50 10 0 23 1
517 30 3 0 183 0
518 58 8 0 183 0
519 46 10 0 183 0
520 52 3 0 183 0
522 55 3 0 1 1
523 80 10 0 183 0
525 21 5 0 183 0
528 48 11 0 183 0
529 49 10 0 43 1
531 51 11 0 183 0
533 18 3 0 183 0
534 32 3 0 183 0
535 56 3 0 12 1
536 75 7 0 183 0
537 59 10 0 183 0
541 45 3 0 183 0
542 26 10 0 183 0
545 21 3 0 183 0
547 20 3 0 4 1
549 49 3 0 183 0
550 62 5 0 183 0
563 21 9 0 183 0
567 18 10 0 183 0
568 84 11 0 183 0
569 31 10 0 183 0
570 25 11 0 183 0
573 55 10 0 6 1
574 19 11 0 183 0
579 68 5 1 183 0
580 71 13 0 183 0
585 42 10 0 2 1
587 24 4 0 183 0
588 53 8 0 183 0
589 41 13 0 183 0
594 36 11 0 183 0
603 77 10 0 4 1
608 39 12 0 183 0
609 72 13 1 183 0
612 22 12 0 183 0
623 58 13 0 183 0
624 35 10 0 183 0
625 26 12 0 183 0
629 53 9 0 183 0
631 16 13 0 1 1
633 71 11 0 183 0
634 18 11 0 183 0
637 36 10 0 183 0
638 62 10 0 183 0
643 56 9 0 183 0
646 21 13 0 183 0
647 58 3 0 2 1
649 24 11 0 183 0
652 19 10 0 183 0
657 54 12 0 183 0
660 84 8 0 6 1
661 43 8 0 183 0
666 57 5 0 183 0
668 20 7 0 183 0
674 28 10 0 3 1
678 52 10 0 183 0
id age weight gender time event
1 20 13 0 24 1
2 38 9 0 183 0
3 24 12 0 3 1
4 35 13 0 183 0
5 32 4 0 183 0
6 86 10 0 183 0
7 39 11 0 183 0
8 45 12 0 4 1
9 20 3 0 183 0
10 71 12 0 183 0
11 39 12 0 2 1
12 42 9 0 183 0
13 63 3 0 183 0
14 53 12 0 183 0
15 24 11 0 6 1
16 30 10 0 183 0
17 43 11 0 183 0
18 59 10 0 9 1
19 50 11 0 183 0
20 19 7 0 183 0
21 20 11 0 183 0
22 48 5 1 3 1
23 32 13 0 183 0
24 30 7 0 183 0
25 31 12 0 4 1
26 29 10 0 183 0
27 72 7 0 183 0
28 19 10 0 6 1
29 30 9 0 183 0
30 26 3 0 183 0
31 26 12 0 3 1
32 22 10 0 183 0
33 36 11 0 12 1
34 32 11 0 183 0
35 59 5 1 183 0
36 64 6 0 1 1
37 22 13 0 183 0
38 45 3 0 183 0
39 45 12 0 183 0
40 18 10 0 1 1
41 77 10 0 183 0
42 29 11 0 1 1
43 22 10 0 183 0
44 58 11 0 2 1
45 72 13 0 183 0
46 47 10 0 183 0
47 32 13 0 183 0
48 34 3 1 4 1
49 22 10 0 183 0
50 35 8 0 183 0
51 42 10 0 183 0
52 25 12 0 12 1
53 16 10 0 183 0
54 24 10 0 183 0
55 62 6 0 183 0
56 39 9 0 183 0
57 22 11 0 183 0
58 17 3 0 33 1
59 25 9 0 183 0
60 23 10 0 183 0
61 35 8 0 183 0
62 76 6 0 23 1
63 25 12 0 183 0
64 26 8 0 183 0
65 35 10 0 183 0
66 48 9 0 23 1
67 50 11 0 183 0
68 63 13 0 183 0
69 31 11 0 183 0
70 49 3 1 1 1
71 63 10 0 183 0
72 22 8 0 183 0
73 23 13 0 183 0
74 21 8 0 183 0
75 60 10 0 183 0
76 66 10 0 183 0
77 40 12 0 183 0
78 81 13 0 183 0
79 22 11 0 183 0
80 23 11 0 183 0
81 32 13 0 183 0
82 38 11 0 183 0
83 21 8 0 183 0
84 25 11 0 6 1
85 86 13 0 183 0
86 26 3 0 183 0
87 59 11 0 183 0
88 56 8 0 183 0
89 43 11 0 183 0
90 43 12 0 8 1
91 16 11 0 183 0
92 39 12 0 183 0
93 18 10 0 183 0
94 54 11 0 183 0
95 54 10 0 183 0
96 45 5 0 183 0
97 77 9 0 9 1
98 50 11 0 183 0
99 52 9 0 183 0
100 33 12 0 183 0
101 47 11 0 183 0
102 41 10 0 183 0
103 65 12 0 7 1
104 36 5 0 183 0
105 44 11 0 183 0
106 27 12 0 183 0
107 40 13 0 183 0
108 38 13 0 9 1
109 46 11 0 183 0
110 55 8 0 183 0
111 47 13 0 183 0
112 29 3 1 183 0
113 71 11 0 183 0
114 21 6 0 183 0
115 41 10 0 183 0
116 52 3 1 11 1
117 23 5 0 183 0
118 60 3 1 183 0
119 20 5 1 183 0
120 23 11 0 183 0
121 25 9 1 183 0
122 52 12 0 12 1
123 48 10 0 183 0
124 43 3 1 183 0
125 32 5 1 183 0
126 29 3 1 183 0
127 35 3 1 2 1
128 47 10 0 183 0
129 25 10 0 183 0
130 24 8 1 183 0
131 57 11 0 183 0
132 19 12 0 183 0
133 48 11 0 2 1
134 26 12 0 183 0
135 22 11 0 183 0
136 19 11 0 183 0
137 51 3 1 183 0
138 54 11 0 8 1
139 30 6 1 183 0
140 58 13 0 183 0
141 23 13 0 183 0
142 64 11 0 183 0
143 29 3 1 2 1
144 24 11 0 183 0
145 25 11 0 183 0
146 52 5 1 183 0
147 21 5 0 183 0
148 22 13 0 183 0
149 40 3 0 183 0
150 37 11 0 12 1
151 40 6 0 183 0
152 23 5 0 183 0
153 21 10 0 183 0
154 32 11 0 183 0
155 43 10 0 3 1
156 66 10 0 183 0
157 50 10 0 183 0
158 30 3 0 183 0
159 58 8 0 183 0
160 46 10 0 183 0
161 52 3 0 5 1
162 55 3 0 183 0
163 80 10 0 183 0
164 21 5 0 4 1
165 48 11 0 183 0
166 49 10 0 183 0
167 51 11 0 183 0
168 18 3 0 183 0
169 32 3 0 183 0
170 56 3 0 183 0
171 75 7 0 9 1
172 59 10 0 183 0
173 45 3 0 183 0
174 26 10 0 183 0
175 21 3 0 183 0
176 20 3 0 183 0
177 49 3 0 183 0
178 62 5 0 183 0
179 21 9 0 8 1
180 18 10 0 183 0
181 84 11 0 183 0
182 31 10 0 8 1
183 25 11 0 183 0
184 55 10 0 183 0
185 19 11 0 183 0
186 68 5 1 183 0
187 71 13 0 2 1
188 42 10 0 183 0
189 24 4 0 1 1
190 53 8 0 183 0
191 41 13 0 183 0
192 36 11 0 183 0
193 77 10 0 4 1
194 39 12 0 183 0
195 72 13 1 183 0
196 22 12 0 183 0
197 58 13 0 12 1
198 35 10 0 183 0
199 26 12 0 1 1
200 53 9 0 183 0
201 16 13 0 183 0
202 71 11 0 1 1
203 18 11 0 183 0
204 36 10 0 3 1
205 62 10 0 183 0
206 56 9 0 6 1
207 21 13 0 183 0
208 58 3 0 2 1
209 24 11 0 183 0
210 19 10 0 183 0
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