I'm new to plotting in Rstudio.
I want to fit a parametric survival distribution/model to some hypothetical data for the time-to-progression (TTP) for a standard of care.
Using the 'flexsurv' package, I fit several commonly used parametric survival distributions to data that includes a column for the time (in years) and a status indicator whether the time corresponds to an event, i.e. disease progression (status = 1), or to the last time of follow up, i.e. censoring (status = 0).
head(df_TTP)
time status
1 6.13998781 1
2 2.68245570 1
3 5.20061733 1
4 1.93995197 1
5 0.02420781 0
6 9.66624332 1
I use the following code to fit parametric survival distributions:
l_TTP_SoC_exp <- flexsurvreg(formula = Surv(time, status) ~ 1, data = df_TTP, dist = "exp")
l_TTP_SoC_gamma <- flexsurvreg(formula = Surv(time, status) ~ 1, data = df_TTP, dist = "gamma")
l_TTP_SoC_gompertz <- flexsurvreg(formula = Surv(time, status) ~ 1, data = df_TTP, dist = "gompertz")
l_TTP_SoC_llogis <- flexsurvreg(formula = Surv(time, status) ~ 1, data = df_TTP, dist = "llogis")
l_TTP_SoC_lnorm <- flexsurvreg(formula = Surv(time, status) ~ 1, data = df_TTP, dist = "lnorm")
l_TTP_SoC_weibull <- flexsurvreg(formula = Surv(time, status) ~ 1, data = df_TTP, dist = "weibull")
Then I want to inspect fit based on visual fit:
colors <- rainbow(6)
plot(l_TTP_SoC_exp, col = colors[1], ci = FALSE, ylab = "Event-free proportion", xlab = "Time in years", las = 1)
lines(l_TTP_SoC_gamma, col = colors[2], ci = FALSE)
lines(l_TTP_SoC_gompertz, col = colors[3], ci = FALSE)
lines(l_TTP_SoC_llogis, col = colors[4], ci = FALSE)
lines(l_TTP_SoC_lnorm, col = colors[5], ci = FALSE)
lines(l_TTP_SoC_weibull, col = colors[6], ci = FALSE)
legend("right",
legend = c("exp", "gamma", "gompertz", "llogis", "lnorm", "weibull"),
col = colors,
lty = 1,
bty = "n")
I get the following graph when I do this:
I can understand that the colored lines match the distributions on the right. But what are the black dotted lines? What are they, and why are there so many of them? Also, is there a way to plot a line to represent the survival original data among the colored lines to better see which colored line matches up with the original survival data?
I include the example data I use below:
time status
1 6.139987810 1
2 2.682455703 1
3 5.200617334 1
4 1.939951970 1
5 0.024207815 0
6 9.666243316 1
7 4.394700402 1
8 1.855542818 1
9 4.243317837 1
10 4.869644446 1
11 1.155341021 1
12 4.891988824 1
13 3.431407080 1
14 4.106660932 1
15 8.293441583 1
16 1.786907695 1
17 6.512490436 1
18 9.790415889 1
19 5.807602615 1
20 1.186825739 1
21 1.881704034 1
22 3.331969749 1
23 3.670999791 1
24 0.117667753 0
25 0.752084289 0
26 3.228468223 1
27 4.291003529 1
28 3.968534505 1
29 6.126456936 1
30 1.824315009 0
31 1.067576833 1
32 0.207942678 0
33 3.345709542 1
34 2.630963333 1
35 9.583448954 0
36 4.726709972 1
37 2.891900978 1
38 6.804436004 1
39 5.885603560 1
40 2.256993024 0
41 7.673061950 1
42 5.161129560 1
43 5.166943102 1
44 5.492783542 1
45 7.543131314 1
46 7.728777122 1
47 6.310096008 0
48 4.806289563 1
49 6.329450639 1
50 1.179123729 0
51 9.656833097 1
52 4.968277719 1
53 2.605911365 1
54 7.978906534 1
55 4.181906829 1
56 6.907489546 1
57 7.892817552 1
58 2.524973740 0
59 1.831432953 1
60 5.451004683 1
61 3.512235699 1
62 8.441303260 1
63 5.380989861 1
64 6.254610034 1
65 1.212849987 0
66 0.793727186 0
67 2.524263850 1
68 2.507031055 1
69 2.639087620 1
70 4.318995059 0
71 2.919318585 1
72 3.743526925 1
73 1.152062483 0
74 14.939483927 1
75 4.743329033 1
76 6.766536900 1
77 5.411471601 1
78 0.910766582 0
79 5.621576851 1
80 8.152265017 1
81 6.535860404 1
82 0.435747574 0
83 5.139246701 1
84 2.683237441 1
85 8.294505650 1
86 2.935649641 0
87 0.677132739 1
88 1.849762603 1
89 1.909292774 1
90 7.260559336 1
91 7.845714091 1
92 2.950981222 0
93 5.685308829 1
94 3.566271737 1
95 5.867931880 1
96 7.113857527 1
97 2.725272282 1
98 1.838957663 0
99 4.800776307 1
100 4.503277677 1
101 3.931034439 1
102 5.768823178 1
103 4.654522474 1
104 1.187223843 1
105 0.599267793 0
106 1.874365670 1
107 1.644908363 1
108 3.874953071 1
109 5.188448113 1
110 3.501001799 0
111 1.422450954 1
112 5.982149127 0
113 9.205680321 1
114 0.874650503 0
115 3.148365707 1
116 7.680052104 1
117 4.257221958 1
118 1.192320519 1
119 1.470623365 0
120 2.804878402 0
121 3.623448170 1
122 5.872380191 1
123 5.970895259 1
124 6.770105723 1
125 4.720251077 0
126 0.693668386 1
127 7.520110995 1
128 3.871928081 0
129 7.685419082 1
130 2.730749887 0
131 3.807477008 1
132 1.864886242 1
133 3.461118570 1
134 1.576016548 0
135 4.440189075 1
136 3.546371029 1
137 2.409905038 0
138 2.696909409 1
139 0.785257496 1
140 4.987360910 1
141 5.938235225 1
142 5.197076696 1
143 8.679309490 0
144 7.157690914 1
145 2.275095893 1
146 6.656221384 1
147 6.579052067 1
148 8.813685668 1
149 5.421954233 0
150 3.071099114 1
151 2.237626206 1
152 4.595397012 1
153 4.572749676 0
154 6.509082644 1
155 8.152915561 1
156 5.337048806 1
157 4.111151151 1
158 6.799459898 1
159 4.950618640 1
160 6.788218941 1
161 4.563868708 1
162 5.605490272 1
163 3.609968685 1
164 5.449143774 1
165 0.266878015 0
166 4.358360411 1
167 3.015752648 1
168 6.756558950 1
169 5.173867862 1
170 6.332020640 1
171 3.738698509 1
172 7.157932674 1
173 2.105821388 1
174 2.973402799 1
175 3.491703379 1
176 3.815422192 1
177 5.467513685 1
178 4.383428738 1
179 2.012537821 1
180 4.048068396 1
181 2.298380810 1
182 3.361948075 0
183 3.230055974 1
184 6.338038927 1
185 3.967244050 1
186 4.703338416 1
187 6.337904532 1
188 4.158431775 1
189 0.467085405 0
190 1.767546548 1
191 6.256523180 1
192 5.859013909 1
193 0.661446870 1
194 3.685012770 0
195 0.123654632 0
196 4.802439128 1
197 5.215883749 1
198 3.550301335 1
199 7.544422354 1
200 0.973865424 0
201 6.582649250 1
202 1.077323275 1
203 3.922205759 1
204 3.590529969 0
205 4.316233642 0
206 1.964302884 1
207 5.528386680 1
208 6.134323135 1
209 0.160850760 0
210 7.295046192 1
211 4.698309692 1
212 6.448159332 1
213 6.806009407 1
214 3.452172977 1
215 9.595233070 1
216 2.962352465 0
217 5.620883007 1
218 5.200851465 1
219 2.442962682 1
220 0.583044966 0
221 6.183468898 1
222 1.095478361 1
223 3.096224404 1
224 3.374020417 1
225 9.431196562 1
226 5.299193248 1
227 4.726379077 1
228 4.186386486 1
229 0.725098539 0
230 1.632346408 1
231 3.813283923 1
232 5.063728268 1
233 4.303872156 1
234 9.267289968 1
235 6.375692099 1
236 5.285257667 1
237 7.002038506 1
238 1.476317382 0
239 7.537317543 1
240 0.773277083 0
241 4.273129564 1
242 3.530340351 1
243 7.300750804 1
244 3.718900574 1
245 5.936866355 1
246 3.121939279 1
247 5.272388153 1
248 0.970292931 1
249 0.503570026 0
250 3.107563505 1
251 6.408942533 1
252 6.749630841 1
253 2.833333594 0
254 6.315560685 1
255 4.093816601 0
256 1.749620491 0
257 0.147443207 0
258 5.233251639 1
259 1.349483594 0
260 2.068471853 1
261 1.528133965 1
262 1.949117019 1
263 3.453404373 1
264 1.243505388 1
265 4.470883277 1
266 4.081677976 1
267 5.741269973 1
268 5.655902742 1
269 10.876670614 1
270 4.560481325 1
271 2.043629042 1
272 12.380597999 1
273 4.275103835 0
274 7.392543683 1
275 1.783457613 0
276 3.050562270 1
277 0.263555069 0
278 4.802846379 1
279 0.853799653 0
280 3.617490284 1
281 2.890979424 1
282 7.752854974 1
283 5.289347190 1
284 5.793151335 0
285 9.281854161 1
286 5.294338092 1
287 9.094930939 1
288 6.708473611 1
289 4.022503442 0
290 3.478751959 1
291 6.053820467 1
292 8.332808901 1
293 8.923607255 1
294 1.962605087 1
295 2.920882413 1
296 0.009120685 0
297 0.738333449 1
298 3.467213801 0
299 8.363279454 1
300 2.606588656 1
301 2.709065572 1
302 11.877796290 1
303 2.748127510 1
304 3.089540574 1
305 3.737678730 1
306 4.705870873 1
307 3.122080753 0
308 12.052067876 1
309 4.897963747 1
310 4.630074129 1
311 1.686443269 0
312 4.815049867 1
313 3.197021981 1
314 9.357999946 1
315 5.554961390 0
316 0.127240628 0
317 2.330046991 1
318 6.592061660 1
319 5.604868700 1
320 2.161514471 1
321 1.519945497 0
322 6.069341778 1
323 7.615055399 1
324 3.258434138 1
325 0.592943949 0
326 10.125728028 1
327 0.134232615 1
328 10.075649724 1
329 5.725160292 1
330 0.333856252 0
331 3.820441954 1
332 6.151071358 1
333 3.032919701 1
334 2.342875794 1
335 5.917295351 1
336 0.674378226 0
337 3.301987477 1
338 3.664852606 1
339 3.649017920 1
340 0.823082176 1
341 5.159793600 1
342 8.018004139 1
343 4.408234319 1
344 1.684378811 0
345 4.669172070 1
346 5.482570121 1
347 0.712668970 1
348 5.349235125 1
349 6.675116584 1
350 3.781550075 1
351 5.078751640 0
352 1.000204677 1
353 4.479880679 1
354 7.406794484 1
355 1.073215282 0
356 0.654139652 1
357 3.488547315 1
358 5.130309382 1
359 5.844084353 1
360 2.333567936 1
361 7.659060181 1
362 7.056350363 1