scatterplot: Create scatter plot with imputed survival times
In dynpred: Companion Package to "Dynamic Prediction in Clinical Survival Analysis"
Description
Usage
Arguments
Details
Value
Author(s)
References
Examples
Description
Create scatter plot with imputed survival times.
Usage
1
scatterplot(formula, data, horizon, plot = TRUE, xlab)
Arguments
formula
Formula for prediction model to be used as in
coxph
data
Data set in which to interpret the formula
horizon
The horizon, maximum value to be imputed in case of censored
observations; default is 1.05 times largest event time
plot
Should the tolerance plot actually be plotted? Default is
TRUE
xlab
Label for x-axis
Details
Imputation is used for censored survival times.
Value
A data frame with columns
x
Predictor (centered at zero)
imputed
(Imputed) survival time
and with attribute "horizon"
(copied from input or default).
Author(s)
Hein Putter H.Putter@lumc.nl
References
Royston P (2001), The lognormal distribution as a model for
survival time in cancer, with an emphasis on prognostic factors, Statistica
Neerlandica 55, 89-104.
van Houwelingen HC, Putter H (2012). Dynamic Prediction in Clinical Survival
Analysis. Chapman & Hall.
Examples
1
2
data(ova)
scatterplot(Surv(tyears, d) ~ Karn + Broders + FIGO + Ascites + Diam, data = ova)
Example output
Loading required package: survival
x imputed
1 0.646116141 0.96712374
2 -0.272525041 2.66575332
3 -0.160811663 1.05479398
4 0.236090645 0.68219291
5 -0.003220693 4.72054856
6 0.356721095 0.41917974
7 0.396104763 0.88219055
8 -0.781353647 6.16191718
9 0.565175875 1.47945194
10 -0.832984837 6.16191718
11 0.317039340 0.45753364
12 0.190646897 0.94246708
13 0.490265775 1.73972685
14 -0.336460817 2.83835614
15 0.185641207 5.00821909
16 -0.259127998 1.39452054
17 -0.850838925 6.16191718
18 -0.659758402 2.98082243
19 1.086096147 0.90958928
20 0.011233020 1.63561781
21 0.239005001 6.16191718
22 -0.402669346 6.16191718
23 0.010268226 6.16191718
24 -1.172760555 6.16191718
25 0.500331938 0.35616441
26 0.673558373 1.29862983
27 1.034464956 0.52328814
28 0.506392448 2.74794625
29 0.689193839 1.88218972
30 -0.260309750 6.16191718
31 -0.312905735 1.16986219
32 0.529947530 2.13424599
33 0.243242881 0.75342352
34 0.529947530 1.05753324
35 0.912869712 1.91232914
36 0.139920857 5.16164409
37 -0.055816678 1.45205628
38 -0.353473735 6.16191718
39 -0.773311903 2.01643777
40 -0.353473735 6.16191718
41 -0.208678559 3.72602962
42 0.892106129 0.11232937
43 0.085178326 0.26575480
44 0.317039340 1.27397280
45 0.506392448 0.54246531
46 0.183494660 3.09863017
47 1.033500162 0.44109518
48 -1.044854673 5.86849255
49 -0.013286857 5.29589206
50 0.454761257 0.12328726
51 0.097482911 1.98904050
52 0.443935781 0.08219248
53 0.010268226 0.40000226
54 0.317039340 2.18082183
55 0.131863470 3.32876815
56 -0.160811663 1.46301272
57 0.765564839 0.18082221
58 0.009868428 2.38629944
59 -0.579296155 3.73972663
60 0.317039340 1.15342518
61 0.274109721 1.24931339
62 -0.509810877 1.25479422
63 0.010268226 1.14246657
64 0.817196029 2.27397113
65 0.236090645 1.95890431
66 0.201346936 1.89315225
67 -0.952433535 0.70410700
68 0.183494660 2.14520258
69 -0.206532013 6.16191718
70 0.062864210 2.30137153
71 -0.259127998 0.84383554
72 1.140641693 0.23835538
73 -0.850838925 3.29041114
74 -0.422596191 6.16191718
75 -0.108215678 1.12876687
76 0.836718644 2.17534330
77 0.140416963 0.66575332
78 -0.261274544 1.26301376
79 -0.798242940 6.16191718
80 0.409317080 3.01095995
81 0.568563388 6.16191718
82 -0.507664330 6.16191718
83 0.478316340 0.37260313
84 -0.353473735 3.25479496
85 0.635416101 0.18904309
86 -0.214989197 6.16191718
87 0.532094077 1.21369843
88 -0.781353647 0.84931274
89 0.592338404 2.57260117
90 -0.079371761 0.91506750
91 -0.160811663 3.79726039
92 0.967415258 5.47671319
93 -0.509810877 6.16191718
94 -1.099375258 2.52328935
95 0.088785772 2.02739694
96 0.621927183 0.02465731
97 -0.402669346 6.16191718
98 0.236090645 0.50137037
99 0.409317080 3.03287650
100 0.761957392 1.50136859
101 -0.261274544 6.16191718
102 1.206726597 1.25753587
103 0.145959451 5.66849293
104 0.183494660 2.10684877
105 0.687047292 2.12054783
106 -1.345986990 6.16191718
107 -0.625016505 6.16191718
108 0.981868971 0.73424635
109 -0.625016505 2.56712272
110 -0.701167573 6.16191718
111 0.010268226 6.16191718
112 0.131863470 1.52876816
113 0.860273727 1.09315109
114 -0.579296155 1.18356042
115 -0.208678559 0.30684943
116 -1.099375258 0.52602734
117 0.311000745 1.47671095
118 0.687047292 1.56712223
119 -0.131125807 3.89315013
120 -1.197691593 6.16191718
121 -0.336460817 6.16191718
122 -0.131125807 1.77808304
123 0.755769950 2.01369783
124 -0.030662926 6.16191718
125 0.011233020 0.64931382
126 -0.509810877 3.18082213
127 -0.003220693 1.32054850
128 -0.041362965 0.30684997
129 -0.336460817 5.32876637
130 0.343232177 0.35616441
131 0.343232177 6.16191718
132 0.372426824 2.31506909
133 -0.825907888 6.16191718
134 -0.588617619 0.61917852
135 1.112017709 0.86301307
136 0.357685890 1.81643803
137 -0.252598195 2.20547824
138 0.486869833 2.27123421
139 -0.781353647 0.50959059
140 -0.926148823 5.10410752
141 -0.088048109 3.30136984
142 0.438634584 6.16191718
143 0.990422464 3.94246576
144 0.620562591 1.97260223
145 0.660096268 6.16191718
146 -0.260309750 2.17808262
147 -0.075743524 5.73150723
148 0.486869833 0.22739782
149 0.010268226 2.61917785
150 -0.677612490 1.27123169
151 1.155095406 0.38630286
152 0.118374551 3.74520562
153 0.566416842 1.35616352
154 0.356721095 1.22191715
155 0.142563509 0.37808433
156 -1.617529760 5.14246594
157 -0.402669346 0.39999981
158 0.582543515 0.20821886
159 1.193237678 0.72602816
160 0.400858083 1.19452244
161 0.323305331 1.33150871
162 0.070531887 6.16191718
163 0.490265775 0.53698666
164 0.108308388 6.16191718
165 -0.625016505 5.25753440
166 -0.781353647 3.69588973
167 0.313643398 1.87123361
168 0.281534822 0.50137066
169 0.033547135 1.98356120
170 0.224988996 1.27123414
171 -0.253632598 1.95890279
172 -0.064918047 5.08766945
173 0.412231436 6.16191718
174 1.270790430 0.02465656
175 -0.160811663 6.16191718
176 0.183094863 0.12876654
177 0.688012086 0.64657641
178 -0.629135950 2.32054736
179 0.343232177 1.80821887
180 -1.047620443 3.28219095
181 0.542380174 0.31232879
182 0.860273727 0.24657466
183 1.020011243 1.72602701
184 -0.579296155 3.33424665
185 -1.099375258 6.16191718
186 -0.342005887 1.07671122
187 -0.108215678 1.69588949
188 0.108308388 6.16191718
189 -0.790679544 6.16191718
190 0.085178326 6.16191718
191 0.239005001 0.89589079
192 0.435238642 2.26027473
193 -1.294232175 6.16191718
194 -0.353473735 2.72054835
195 -0.720690188 6.16191718
196 -0.720690188 6.16191718
197 0.688012086 0.47945292
198 -0.088048109 6.16191718
199 0.513820857 2.63835593
200 -0.388215632 1.02739924
201 -0.347286294 4.77808201
202 -0.238144482 1.55342449
203 0.356721095 1.01095930
204 0.515967404 1.02191881
205 0.265408149 1.14794584
206 1.097563996 1.29041074
207 -0.003220693 6.16191718
208 -0.275920983 0.61643724
209 0.319185886 1.83013638
210 -0.419558639 6.16191718
211 -0.677612490 1.36438485
212 0.513421059 0.80822089
213 0.486869833 0.51506886
214 0.183494660 0.77808274
215 -0.850838925 2.00821842
216 0.594011364 2.01369739
217 -1.345986990 6.16191718
218 0.184459455 2.63013806
219 -0.406069720 2.18356035
220 0.513421059 6.16191718
221 -0.608127212 6.16191718
222 0.582543515 1.47397437
223 0.305089905 2.16164402
224 -0.053670131 6.16191718
225 0.635416101 5.73150723
226 -0.160811663 2.58629930
227 0.755769950 1.69314970
228 -0.278563635 0.04109387
229 0.513820857 2.52602886
230 -0.579296155 0.61643878
231 -0.139679300 6.16191718
232 -0.850838925 6.16191718
233 -0.825907888 2.76438334
234 -0.096747829 1.37534193
235 0.011233020 0.73424802
236 -0.509810877 0.60547866
237 0.065010757 1.36712091
238 -0.924224223 6.16191718
239 -0.139679300 5.86849255
240 -0.871628238 6.16191718
241 0.356721095 0.52328670
242 0.042100628 5.73150723
243 -0.013286857 1.08767011
244 -1.197691593 4.36986315
245 0.431631195 3.03835642
246 -1.024465158 6.16191718
247 -0.429624803 6.16191718
248 0.356721095 1.98904067
249 1.206726597 1.18904188
250 -0.059616850 0.36712414
251 -0.526700170 2.91780672
252 -1.097450658 6.16191718
253 0.236090645 6.16191718
254 -0.184366745 4.73698619
255 0.703173965 5.16164409
256 0.462189666 2.15616310
257 0.012414772 1.41917711
258 0.117409757 0.26849283
259 0.244424634 2.81917917
260 -1.370918028 6.16191718
261 -0.206532013 0.65205567
262 -0.160811663 0.96438372
263 0.412231436 0.26575409
264 -0.458056062 1.10410967
265 -0.773311903 4.27397245
266 1.336875334 0.28493007
267 -0.526700170 5.08766945
268 -0.253632598 0.55068681
269 -0.023147539 3.29863045
270 -0.108215678 1.35342510
271 0.247558494 0.85205491
272 -1.044854673 1.85753370
273 -0.088048109 1.72328677
274 -0.336460817 1.90685106
275 0.183494660 1.46027372
276 0.012414772 0.36438403
277 0.224988996 1.75616392
278 -0.282683080 6.16191718
279 -0.184366745 0.62191925
280 0.412231436 1.41643813
281 0.741789823 0.02465724
282 -0.677612490 1.27671389
283 1.033500162 0.68219222
284 -0.259127998 5.08766945
285 -0.357593180 1.49041064
286 -0.652681453 6.16191718
287 -0.336584442 0.72602610
288 -0.406069720 6.16191718
289 0.333166013 1.62739512
290 -0.312905735 1.36438474
291 0.742557633 0.22465690
292 0.608465077 1.24657607
293 -0.030662926 5.29589206
294 -0.261274544 0.56438502
295 0.516458612 1.01369706
296 0.739643277 0.51233045
297 -0.312905735 1.61643784
298 1.336875334 0.02739762
299 0.703173965 1.70137072
300 0.062864210 1.94246444
301 -0.013286857 0.74246432
302 -0.449147418 1.16712407
303 0.012414772 6.16191718
304 0.183494660 1.39726135
305 0.201346936 0.94520489
306 -0.054851884 4.72054856
307 0.817196029 0.57260391
308 -0.261274544 6.16191718
309 -0.825907888 4.00000016
310 0.024097613 3.11781061
311 0.470247054 0.75068561
312 -0.472154624 2.43013745
313 -0.579296155 6.16191718
314 -0.174300581 3.75342264
315 0.817196029 1.98082162
316 0.794188823 2.35616393
317 0.462189666 4.77808201
318 -0.449147418 3.81369850
319 -1.294232175 6.16191718
320 0.183494660 4.21369831
321 -1.121005740 6.16191718
322 0.958313889 0.08767131
323 1.033500162 3.42191676
324 -0.096747829 6.16191718
325 0.315789944 4.10958748
326 -0.353473735 5.25753440
327 0.011233020 6.16191718
328 -1.099375258 6.16191718
329 -1.097450658 1.99999927
330 0.265408149 1.02739863
331 0.010268226 6.16191718
332 0.010268226 2.52054852
333 0.463862627 0.20000095
334 1.097563996 0.04109684
335 -0.059616850 6.16191718
336 -0.232843285 1.23561561
337 0.356721095 0.97534167
338 -0.924224223 6.16191718
339 -1.022689405 6.16191718
340 0.011233020 2.68492981
341 -0.720690188 2.88219159
342 0.912869712 0.03561696
343 -0.030662926 1.59178288
344 0.641326942 5.73150723
345 -0.174300581 2.83835499
346 -0.926148823 6.16191718
347 -0.526700170 6.16191718
348 -0.561442067 4.07397293
349 -0.142228225 0.59999893
350 -1.088774309 6.16191718
351 0.054405213 6.16191718
352 0.088785772 6.16191718
353 -1.044854673 6.16191718
354 0.183494660 0.13150880
355 -0.592785074 6.16191718
356 0.739643277 6.16191718
357 -0.186513292 6.16191718
358 -0.871628238 1.61095975
dynpred documentation built on May 2, 2019, 5:07 a.m.
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Description Usage Arguments Details Value Author(s) References Examples
Description
Create scatter plot with imputed survival times.
Usage
1 | scatterplot(formula, data, horizon, plot = TRUE, xlab)
|
Arguments
formula |
Formula for prediction model to be used as in
|
data |
Data set in which to interpret the formula |
horizon |
The horizon, maximum value to be imputed in case of censored observations; default is 1.05 times largest event time |
plot |
Should the tolerance plot actually be plotted? Default is
|
xlab |
Label for x-axis |
Details
Imputation is used for censored survival times.
Value
A data frame with columns
x |
Predictor (centered at zero) |
imputed |
(Imputed) survival time |
and with attribute "horizon"
(copied from input or default).
Author(s)
Hein Putter H.Putter@lumc.nl
References
Royston P (2001), The lognormal distribution as a model for survival time in cancer, with an emphasis on prognostic factors, Statistica Neerlandica 55, 89-104.
van Houwelingen HC, Putter H (2012). Dynamic Prediction in Clinical Survival Analysis. Chapman & Hall.
Examples
1 2 | data(ova)
scatterplot(Surv(tyears, d) ~ Karn + Broders + FIGO + Ascites + Diam, data = ova)
|
Example output
Loading required package: survival
x imputed
1 0.646116141 0.96712374
2 -0.272525041 2.66575332
3 -0.160811663 1.05479398
4 0.236090645 0.68219291
5 -0.003220693 4.72054856
6 0.356721095 0.41917974
7 0.396104763 0.88219055
8 -0.781353647 6.16191718
9 0.565175875 1.47945194
10 -0.832984837 6.16191718
11 0.317039340 0.45753364
12 0.190646897 0.94246708
13 0.490265775 1.73972685
14 -0.336460817 2.83835614
15 0.185641207 5.00821909
16 -0.259127998 1.39452054
17 -0.850838925 6.16191718
18 -0.659758402 2.98082243
19 1.086096147 0.90958928
20 0.011233020 1.63561781
21 0.239005001 6.16191718
22 -0.402669346 6.16191718
23 0.010268226 6.16191718
24 -1.172760555 6.16191718
25 0.500331938 0.35616441
26 0.673558373 1.29862983
27 1.034464956 0.52328814
28 0.506392448 2.74794625
29 0.689193839 1.88218972
30 -0.260309750 6.16191718
31 -0.312905735 1.16986219
32 0.529947530 2.13424599
33 0.243242881 0.75342352
34 0.529947530 1.05753324
35 0.912869712 1.91232914
36 0.139920857 5.16164409
37 -0.055816678 1.45205628
38 -0.353473735 6.16191718
39 -0.773311903 2.01643777
40 -0.353473735 6.16191718
41 -0.208678559 3.72602962
42 0.892106129 0.11232937
43 0.085178326 0.26575480
44 0.317039340 1.27397280
45 0.506392448 0.54246531
46 0.183494660 3.09863017
47 1.033500162 0.44109518
48 -1.044854673 5.86849255
49 -0.013286857 5.29589206
50 0.454761257 0.12328726
51 0.097482911 1.98904050
52 0.443935781 0.08219248
53 0.010268226 0.40000226
54 0.317039340 2.18082183
55 0.131863470 3.32876815
56 -0.160811663 1.46301272
57 0.765564839 0.18082221
58 0.009868428 2.38629944
59 -0.579296155 3.73972663
60 0.317039340 1.15342518
61 0.274109721 1.24931339
62 -0.509810877 1.25479422
63 0.010268226 1.14246657
64 0.817196029 2.27397113
65 0.236090645 1.95890431
66 0.201346936 1.89315225
67 -0.952433535 0.70410700
68 0.183494660 2.14520258
69 -0.206532013 6.16191718
70 0.062864210 2.30137153
71 -0.259127998 0.84383554
72 1.140641693 0.23835538
73 -0.850838925 3.29041114
74 -0.422596191 6.16191718
75 -0.108215678 1.12876687
76 0.836718644 2.17534330
77 0.140416963 0.66575332
78 -0.261274544 1.26301376
79 -0.798242940 6.16191718
80 0.409317080 3.01095995
81 0.568563388 6.16191718
82 -0.507664330 6.16191718
83 0.478316340 0.37260313
84 -0.353473735 3.25479496
85 0.635416101 0.18904309
86 -0.214989197 6.16191718
87 0.532094077 1.21369843
88 -0.781353647 0.84931274
89 0.592338404 2.57260117
90 -0.079371761 0.91506750
91 -0.160811663 3.79726039
92 0.967415258 5.47671319
93 -0.509810877 6.16191718
94 -1.099375258 2.52328935
95 0.088785772 2.02739694
96 0.621927183 0.02465731
97 -0.402669346 6.16191718
98 0.236090645 0.50137037
99 0.409317080 3.03287650
100 0.761957392 1.50136859
101 -0.261274544 6.16191718
102 1.206726597 1.25753587
103 0.145959451 5.66849293
104 0.183494660 2.10684877
105 0.687047292 2.12054783
106 -1.345986990 6.16191718
107 -0.625016505 6.16191718
108 0.981868971 0.73424635
109 -0.625016505 2.56712272
110 -0.701167573 6.16191718
111 0.010268226 6.16191718
112 0.131863470 1.52876816
113 0.860273727 1.09315109
114 -0.579296155 1.18356042
115 -0.208678559 0.30684943
116 -1.099375258 0.52602734
117 0.311000745 1.47671095
118 0.687047292 1.56712223
119 -0.131125807 3.89315013
120 -1.197691593 6.16191718
121 -0.336460817 6.16191718
122 -0.131125807 1.77808304
123 0.755769950 2.01369783
124 -0.030662926 6.16191718
125 0.011233020 0.64931382
126 -0.509810877 3.18082213
127 -0.003220693 1.32054850
128 -0.041362965 0.30684997
129 -0.336460817 5.32876637
130 0.343232177 0.35616441
131 0.343232177 6.16191718
132 0.372426824 2.31506909
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