I’ve written the code to automatically fit all the untruncated distributions. Here it is, in helpers.R:
fit_dispersal_untruncated function(dispersal_data, zero = 7,
model_list = c("hnorm", "exp", "lnorm", "gamma",
"weibull", "invgauss", "logis",
"invgamma")) {
# Fit untruncated dispersal models to data
# dispersal_data must be a data frame containing columns
# ID, Density, Siliques, Seedlings, Distance
# All data in dispersal_data are used in a single fit, so if only a single rep is to
# be analyzed, it should be subset outside this function
if ("invgauss" %in% model_list) library(actuar)
if ("gengamma" %in% model_list) library(flexsurv)
cens_data_tble <- cens_dispersal_data(dispersal_data, zero)
result <- data.frame(ID = factor(),
model = factor(),
AIC = double(),
par1 = double(),
par2 = double(),
par3 = double(),
se1 = double(),
se2 = double(),
se3 = double())
for (model in model_list) {
fit_i <- try(fitdistcens(cens_data_tble, model,
start = start_params(cens_data_tble, model)))
if (class(fit_i) != "try-error") {
par_i <- rep(NA, 3)
se_i <- rep(NA, 3)
n_par <- length(fit_i$est)
par_i[1:n_par] <- fit_i$est
se_i[1:n_par] <- fit_i$sd
result <- rbind(result,
data.frame(ID = dispersal_data$ID[1],
model = model,
AIC = fit_i$aic,
par1 = par_i[1], par2 = par_i[2], par3 = par_i[3],
se1 = se_i[1], se2 = se_i[2], se3 = se_i[3]))
}
}
result
}And here are the results of fitting it to our test dataset:
temp <- filter(disperseLer, ID == "73_0")
fit_dispersal_untruncated(temp) ID model AIC par1 par2 par3 se1 se2 se3
1 73_0 hnorm 1530.806 5.267032 NA NA 0.20882253 NA NA
2 73_0 exp 1563.703 0.237446 NA NA 0.01330460 NA NA
3 73_0 lnorm 1555.858 1.135456 0.8428757 NA 0.04807217 0.03696496 NA
4 73_0 gamma 1533.055 1.630594 0.3855333 NA 0.13143278 0.03515710 NA
5 73_0 weibull 1530.989 1.342346 4.6042573 NA 0.06248417 0.20405702 NA
6 73_0 invgauss 1559.186 4.241039 4.6609548 NA 0.22664962 0.41547646 NA
7 73_0 logis 1647.386 3.890933 1.7743866 NA 0.17518410 0.08275183 NA
8 73_0 invgamma 1600.887 1.660444 3.7678829 NA 0.13029647 0.36331315 NAWhen I first ran it, I had set the “zero” to 0, and I got an error for gengamma. So this distribution might not be too robust.
And now, fitting all the reps:
library(tidyverse)
result <- fiteach_disp_unt(disperseLer)<simpleError in optim(par = vstart, fn = fnobjcens, fix.arg = fix.arg, gr = gradient, rcens = rcens, lcens = lcens, icens = icens, ncens = ncens, ddistnam = ddistname, pdistnam = pdistname, hessian = TRUE, method = meth, lower = lower, upper = upper, ...): non-finite finite-difference value [1]>
Error in fitdistcens(cens_data_tble, model, start = start_params(cens_data_tble, :
the function mle failed to estimate the parameters,
with the error code 100
Error in fitdist(cens_data_tble[, 2], model, start = start, ...) :
the function mle failed to estimate the parameters,
with the error code 1
<simpleError in optim(par = vstart, fn = fnobjcens, fix.arg = fix.arg, gr = gradient, rcens = rcens, lcens = lcens, icens = icens, ncens = ncens, ddistnam = ddistname, pdistnam = pdistname, hessian = TRUE, method = meth, lower = lower, upper = upper, ...): non-finite finite-difference value [2]>
Error in fitdistcens(cens_data_tble, model, start = start, ...) :
the function mle failed to estimate the parameters,
with the error code 100
<simpleError in optim(par = vstart, fn = fnobjcens, fix.arg = fix.arg, gr = gradient, rcens = rcens, lcens = lcens, icens = icens, ncens = ncens, ddistnam = ddistname, pdistnam = pdistname, hessian = TRUE, method = meth, lower = lower, upper = upper, ...): non-finite finite-difference value [2]>
Error in fitdistcens(cens_data_tble, model, start = start_params(cens_data_tble, :
the function mle failed to estimate the parameters,
with the error code 100
Error in fitdist(cens_data_tble[, 2], model, start = start, ...) :
the function mle failed to estimate the parameters,
with the error code 1
<simpleError in optim(par = vstart, fn = fnobjcens, fix.arg = fix.arg, gr = gradient, rcens = rcens, lcens = lcens, icens = icens, ncens = ncens, ddistnam = ddistname, pdistnam = pdistname, hessian = TRUE, method = meth, lower = lower, upper = upper, ...): non-finite finite-difference value [2]>
Error in fitdistcens(cens_data_tble, model, start = start, ...) :
the function mle failed to estimate the parameters,
with the error code 100result ID model AIC par1 par2 par3 se1
1 73_0 hnorm 1530.80560 5.2670324 NA NA 0.208822528
2 73_0 exp 1563.70262 0.2374460 NA NA 0.013304602
3 73_0 lnorm 1555.85827 1.1354557 0.8428757 NA 0.048072168
4 73_0 gamma 1533.05495 1.6305942 0.3855333 NA 0.131432776
5 73_0 weibull 1530.98858 1.3423463 4.6042573 NA 0.062484170
6 73_0 invgauss 1559.18562 4.2410389 4.6609548 NA 0.226649615
7 73_0 logis 1647.38554 3.8909332 1.7743866 NA 0.175184102
8 73_0 invgamma 1600.88662 1.6604441 3.7678829 NA 0.130296466
9 73_0 gengamma 1532.70991 1.5705188 0.7274812 1.11856834 0.092995355
10 75_0 hnorm 1266.46463 5.8823017 NA NA 0.262129280
11 75_0 exp 1294.10823 0.2118871 NA NA 0.013345864
12 75_0 lnorm 1273.67374 1.2716292 0.8046662 NA 0.051362719
13 75_0 gamma 1258.70264 1.7916327 0.3782812 NA 0.158980659
14 75_0 weibull 1259.63111 1.3924367 5.1963624 NA 0.070139456
15 75_0 invgauss 1278.53659 4.7459771 5.7039123 NA 0.272824232
16 75_0 logis 1339.58670 4.3347955 1.8746783 NA 0.205226093
17 75_0 invgamma 1311.98984 1.7428625 4.5892120 NA 0.152317546
18 75_0 gengamma 1260.62837 1.5754181 0.7398792 0.80032607 0.087770126
19 75_1 hnorm 713.51269 4.6564538 NA NA 0.263788197
20 75_1 exp 736.33735 0.2629533 NA NA 0.021046162
21 75_1 lnorm 717.33810 1.0826085 0.7626413 NA 0.062145603
22 75_1 gamma 709.64566 1.9464015 0.5090396 NA 0.223371844
23 75_1 weibull 709.68581 1.4674164 4.2264478 NA 0.095840275
24 75_1 invgauss 717.47726 3.8316349 5.3201363 NA 0.260524891
25 75_1 logis 756.87401 3.5504621 1.4884598 NA 0.209408290
26 75_1 invgamma 735.12953 1.9710233 4.4631912 NA 0.222058642
27 75_1 gengamma 711.43701 1.3897357 0.6990100 0.85168137 0.119597740
28 77_0 hnorm 1154.48392 4.3189682 NA NA 0.189154205
29 77_0 exp 1170.56727 0.2958300 NA NA 0.018308036
30 77_0 lnorm 1172.24716 0.9136948 0.8476576 NA 0.053957937
31 77_0 gamma 1153.47637 1.5389935 0.4527302 NA 0.144150573
32 77_0 weibull 1152.43070 1.2840403 3.6674980 NA 0.066871751
33 77_0 invgauss 1175.67548 3.4199350 3.7724441 NA 0.201501391
34 77_0 logis 1245.45180 3.1060397 1.4155404 NA 0.152701665
35 77_0 invgamma 1207.42812 1.7151041 3.1804392 NA 0.154014252
36 77_0 gengamma 1154.41987 1.3075839 0.7766100 1.02186949 0.093101704
37 78_2 hnorm 3046.93081 6.6139223 NA NA 0.194226822
38 78_2 exp 3145.06731 0.1828970 NA NA 0.007591670
39 78_2 lnorm 3127.12893 1.4231437 0.8233190 NA 0.034550219
40 78_2 gamma 3055.21223 1.8269137 0.3331472 NA 0.106648843
41 78_2 weibull 3041.32405 1.4712434 6.0466580 NA 0.050532206
42 78_2 invgauss 3151.26809 5.4934780 6.2368731 NA 0.214050922
43 78_2 logis 3195.05558 5.1742481 2.1248106 NA 0.155477644
44 78_2 invgamma 3252.73439 1.5886692 4.7076346 NA 0.090684976
45 78_2 gengamma 3036.20618 1.9264567 0.6177754 1.39254074 0.054855243
46 79_0 hnorm 14.83316 1.7220761 NA NA 0.560050751
47 79_0 exp 15.86294 0.6931392 NA NA 0.316223283
48 79_0 lnorm 17.55289 0.2481839 0.6295283 NA 0.319697138
49 79_0 gamma 17.06156 2.4670119 1.6372269 NA 2.266231981
50 79_0 weibull 16.74642 1.7948854 1.6851557 NA 0.882744058
51 79_0 invgauss 17.51835 1.5308945 3.5241013 NA 0.467599238
52 79_0 logis 17.59237 1.5001619 0.5195343 NA 0.439082725
53 79_0 invgamma 18.02673 3.1401792 3.5126521 NA 2.576368089
54 85_1 hnorm 731.12743 3.9224275 NA NA 0.211393536
55 85_1 exp 744.54229 0.3232280 NA NA 0.024610423
56 85_1 lnorm 727.87457 0.8765957 0.7592257 NA 0.059362432
57 85_1 gamma 721.71819 1.8454724 0.5917099 NA 0.210983652
58 85_1 weibull 723.95076 1.3749184 3.4107758 NA 0.083857622
59 85_1 invgauss 729.46785 3.1323950 4.4126513 NA 0.201222265
60 85_1 logis 772.11455 2.8722279 1.2100609 NA 0.160896075
61 85_1 invgamma 746.07460 2.0722270 3.8977887 NA 0.229415494
62 85_1 gengamma 723.56359 1.1042957 0.7434504 0.64516097 0.102188725
63 85_2 hnorm 4354.02345 5.8448366 NA NA 0.140219055
64 85_2 exp 4440.92061 0.2142905 NA NA 0.007266362
65 85_2 lnorm 4454.46718 1.2301652 0.8679806 NA 0.029914455
66 85_2 gamma 4362.56917 1.5889002 0.3393762 NA 0.077536344
67 85_2 weibull 4352.65941 1.3319530 5.0853252 NA 0.037379803
68 85_2 invgauss 4482.50203 4.6979934 4.7706691 NA 0.158089927
69 85_2 logis 4630.75167 4.3386426 1.9138172 NA 0.113579145
70 85_2 invgamma 4619.41046 1.5251798 3.6897006 NA 0.072114229
71 85_2 gengamma 4352.36537 1.6845414 0.7287975 1.16159040 0.046208322
72 87_0 hnorm 2503.79534 7.6047246 NA NA 0.252735336
73 87_0 exp 2590.00982 0.1573617 NA NA 0.007392674
74 87_0 lnorm 2578.61752 1.5782654 0.8248604 NA 0.039086904
75 87_0 gamma 2511.99814 1.8811249 0.2953787 NA 0.122864194
76 87_0 weibull 2496.96837 1.5173888 7.0468414 NA 0.058961971
77 87_0 invgauss 2604.62135 6.3774694 7.1027257 NA 0.283906960
78 87_0 logis 2601.28315 6.0749106 2.3988895 NA 0.198717254
79 87_0 invgamma 2692.59967 1.5242413 5.1844036 NA 0.097569355
80 87_0 gengamma 2489.29664 2.1062651 0.5786449 1.50122029 0.057437468
81 87_1 hnorm 4905.10445 7.2162328 NA NA 0.169701800
82 87_1 exp 5105.92649 0.1632436 NA NA 0.005426232
83 87_1 lnorm 5134.47298 1.5495997 0.8349219 NA 0.028023947
84 87_1 gamma 4950.23018 1.8998148 0.3092386 NA 0.089669063
85 87_1 weibull 4896.22329 1.5758125 6.7990263 NA 0.044455519
86 87_1 invgauss 5210.56941 6.1530944 6.6769370 NA 0.196320365
87 87_1 logis 5030.87876 6.0087778 2.2085551 NA 0.130136482
88 87_1 invgamma 5411.28983 1.4486647 4.6995465 NA 0.065959691
89 87_1 gengamma 4850.76142 2.0847454 0.5446348 1.61348967 0.029466389
90 87_2 hnorm 2516.89447 4.6935381 NA NA 0.141663698
91 87_2 exp 2569.11285 0.2676448 NA NA 0.011415256
92 87_2 lnorm 2508.99452 1.0477988 0.7810924 NA 0.033959472
93 87_2 gamma 2491.09674 1.8078784 0.4813122 NA 0.111347164
94 87_2 weibull 2496.63888 1.3798747 4.1156320 NA 0.047296475
95 87_2 invgauss 2511.86140 3.7663710 4.9251640 NA 0.140556947
96 87_2 logis 2680.31432 3.4189352 1.5026720 NA 0.111728479
97 87_2 invgamma 2568.42236 1.9286590 4.2017863 NA 0.116382767
98 87_2 gengamma 2492.64091 1.2874927 0.7539517 0.64842787 0.062885393
99 88_0 hnorm 503.56417 3.3185885 NA NA 0.207358134
100 88_0 exp 514.20774 0.3813670 NA NA 0.033650928
101 88_0 lnorm 498.07141 0.7269621 0.7289169 NA 0.066541296
102 88_0 gamma 497.09058 1.8989584 0.7162828 NA 0.258116046
103 88_0 weibull 499.35848 1.3833647 2.9022651 NA 0.099763476
104 88_0 invgauss 498.38191 2.6648072 4.1483344 NA 0.188871197
105 88_0 logis 538.33168 2.3952979 1.0198089 NA 0.155727422
106 88_0 invgamma 506.75946 2.3306543 3.9009661 NA 0.305746998
107 88_0 gengamma 498.30700 0.8765649 0.7376402 0.44541340 0.129928529
108 88_1 hnorm 631.99543 3.4971210 NA NA 0.197435815
109 88_1 exp 637.89035 0.3701054 NA NA 0.029518856
110 88_1 lnorm 630.01183 0.7128489 0.7969808 NA 0.065996373
111 88_1 gamma 626.82952 1.6062558 0.5892781 NA 0.199138392
112 88_1 weibull 628.02511 1.2798848 2.9403236 NA 0.085592610
113 88_1 invgauss 629.79778 2.7454661 3.5016135 NA 0.194032404
114 88_1 logis 688.63966 2.4444387 1.1349098 NA 0.157107412
115 88_1 invgamma 641.66312 2.0232205 3.2367013 NA 0.240144148
116 88_1 gengamma 628.53541 0.9385477 0.8006707 0.62020914 0.135404074
117 89_1 hnorm 2406.64126 6.7528483 NA NA 0.224016131
118 89_1 exp 2375.45389 0.2017406 NA NA 0.009463167
119 89_1 lnorm 2402.35380 1.1712851 1.0055974 NA 0.048196724
120 89_1 gamma 2373.23815 1.1516838 0.2320660 NA 0.077767967
121 89_1 weibull 2373.26332 1.0850098 5.1181009 NA 0.042293623
122 89_1 invgauss 2412.58970 4.9876143 3.5287883 NA 0.277949039
123 89_1 logis 2632.08142 4.3161100 2.3671299 NA 0.191894899
124 89_1 invgamma 2473.13372 1.2764551 2.7267200 NA 0.083350747
125 89_1 gengamma 2375.20833 1.6154394 0.9268213 0.96075654 0.088381462
126 90_0 hnorm 1413.69021 4.6059305 NA NA 0.184812145
127 90_0 exp 1455.02807 0.2676296 NA NA 0.015172293
128 90_0 lnorm 1416.81321 1.0697293 0.7551657 NA 0.043607860
129 90_0 gamma 1400.95418 1.9498892 0.5188528 NA 0.160443437
130 90_0 weibull 1402.53955 1.4504372 4.1464021 NA 0.066390806
131 90_0 invgauss 1421.17470 3.7679457 5.3276233 NA 0.179819181
132 90_0 logis 1490.91010 3.4390995 1.4292062 NA 0.140492530
133 90_0 invgamma 1456.95258 2.0050114 4.5119900 NA 0.161547552
134 90_0 gengamma 1402.88945 1.3406103 0.7107653 0.76284388 0.076781418
135 90_1 hnorm 127.80058 3.4429940 NA NA 0.433390916
136 90_1 exp 125.81210 0.3953127 NA NA 0.070337532
137 90_1 lnorm 130.90189 0.4470315 1.0844161 NA 0.212922474
138 90_1 gamma 127.63514 0.8778881 0.3484867 NA 0.276194476
139 90_1 weibull 127.76905 0.9641848 2.4875905 NA 0.171208814
140 90_1 invgauss 130.85989 2.5746310 1.6865461 NA 0.564810150
141 90_1 logis 149.23943 2.2462939 1.3052331 NA 0.408678635
142 90_1 invgamma 134.53019 1.3269110 1.4777013 NA 0.377138771
143 91_2 hnorm 3389.95525 6.7658907 NA NA 0.189161220
144 91_2 exp 3445.10637 0.1863552 NA NA 0.007365288
145 91_2 lnorm 3521.76574 1.3111584 0.9716389 NA 0.039087066
146 91_2 gamma 3423.08379 1.3373590 0.2486607 NA 0.076507939
147 91_2 weibull 3411.51827 1.2377636 5.7396327 NA 0.042088161
148 91_2 invgauss 3556.11901 5.3983976 4.1990234 NA 0.241747800
149 91_2 logis 3646.80711 5.0094588 2.3383819 NA 0.163084836
150 91_2 invgamma 3672.91525 1.2503176 3.0256371 NA 0.068443728
151 91_2 gengamma 3389.31039 1.9958910 0.6802506 1.70706425 0.055953545
152 93_0 hnorm 1028.47719 5.2006061 NA NA 0.250984406
153 93_0 exp 1043.15657 0.2447320 NA NA 0.016693210
154 93_0 lnorm 1050.85199 1.0734728 0.8925654 NA 0.062233784
155 93_0 gamma 1032.59734 1.4502332 0.3535389 NA 0.145279350
156 93_0 weibull 1030.91416 1.2593140 4.4050036 NA 0.072679614
157 93_0 invgauss 1054.42448 4.1200027 3.9869572 NA 0.285532140
158 93_0 logis 1118.26991 3.7318186 1.7834626 NA 0.213268651
159 93_0 invgamma 1083.69104 1.5346292 3.2005757 NA 0.148184481
160 93_0 gengamma 1031.95302 1.5857862 0.7520879 1.27259535 0.118267743
161 95_0 hnorm 642.25865 4.3260645 NA NA 0.254285091
162 95_0 exp 641.01133 0.3058986 NA NA 0.025414894
163 95_0 lnorm 642.24906 0.8530255 0.8702175 NA 0.074649392
164 95_0 gamma 636.45283 1.4013587 0.4265086 NA 0.177288543
165 95_0 weibull 636.96475 1.1966832 3.4888715 NA 0.083100460
166 95_0 invgauss 643.60798 3.3091662 3.4053797 NA 0.270936490
167 95_0 logis 700.04115 2.9147223 1.4244254 NA 0.204031538
168 95_0 invgamma 658.14252 1.6991297 2.9691256 NA 0.206316805
169 95_0 gengamma 638.39670 1.1606068 0.8520061 0.77401440 0.141358793
170 98_0 hnorm 2074.09805 10.0952694 NA NA 0.386884114
171 98_0 exp 2191.32576 0.1097374 NA NA 0.005945103
172 98_0 lnorm 2069.63547 2.0584828 0.6247282 NA 0.033973773
173 98_0 gamma 2000.69634 3.3580967 0.3680144 NA 0.252294053
174 98_0 weibull 1966.99379 2.1956490 10.2615600 NA 0.095863861
175 98_0 invgauss 2105.11072 9.1269851 18.5918087 NA 0.346592275
176 98_0 logis 1978.55109 9.0849252 2.4803804 NA 0.236165721
177 98_0 invgamma 2186.78091 2.2021134 13.5512067 NA 0.163924941
178 98_0 gengamma 1962.85725 2.3950095 0.4239582 1.31165273 0.036203484
179 99_0 hnorm 1251.19367 5.7069982 NA NA 0.254355663
180 99_0 exp 1269.35522 0.2225530 NA NA 0.014020394
181 99_0 lnorm 1291.71326 1.1440372 0.9424203 NA 0.060672705
182 99_0 gamma 1261.29878 1.3558754 0.3008511 NA 0.125418371
183 99_0 weibull 1257.92548 1.2320710 4.8104141 NA 0.066778111
184 99_0 invgauss 1299.20833 4.5273743 3.8463231 NA 0.309198784
185 99_0 logis 1358.41424 4.1493808 1.9821272 NA 0.219892733
186 99_0 invgamma 1339.28917 1.3698891 2.9313040 NA 0.121360314
187 99_0 gengamma 1254.03995 1.7933582 0.7040002 1.61320847 0.100951893
188 100_0 hnorm 208.34117 2.8516928 NA NA 0.267479682
189 100_0 exp 215.09571 0.4364277 NA NA 0.057761326
190 100_0 lnorm 209.68575 0.6041551 0.7210309 NA 0.099634954
191 100_0 gamma 208.62198 1.9355007 0.8327602 NA 0.406552143
192 100_0 weibull 208.90514 1.4276129 2.5542871 NA 0.162226963
193 100_0 invgauss 209.16416 2.3396175 3.8041283 NA 0.243715567
194 100_0 logis 227.13026 2.1323049 0.9098716 NA 0.211935156
195 100_0 invgamma 212.68120 2.4303818 3.6394255 NA 0.486074122
196 100_0 gengamma 210.61427 0.8246610 0.7224616 0.66411003 0.234275754
197 101_0 hnorm 602.67375 4.0014761 NA NA 0.238676167
198 101_0 exp 624.22399 0.3051182 NA NA 0.025704111
199 101_0 lnorm 598.55615 0.9722046 0.7006145 NA 0.060295387
200 101_0 gamma 592.84616 2.1998319 0.6659656 NA 0.273252306
201 101_0 weibull 594.91431 1.5222412 3.6660316 NA 0.101816765
202 101_0 invgauss 600.51498 3.3110917 5.5815670 NA 0.215262309
203 101_0 logis 625.54876 3.0590793 1.1811405 NA 0.172990821
204 101_0 invgamma 614.79721 2.3106725 4.8935321 NA 0.280843602
205 101_0 gengamma 594.82608 1.1830518 0.6772137 0.63878812 0.101784196
206 104_0 hnorm 3987.89170 6.7494942 NA NA 0.173894874
207 104_0 exp 3917.40115 0.2043716 NA NA 0.007445685
208 104_0 lnorm 3949.34844 1.1378882 1.0213516 NA 0.038050245
209 104_0 gamma 3915.81047 1.1050120 0.2256128 NA 0.057676143
210 104_0 weibull 3916.34490 1.0551574 5.0037903 NA 0.031933717
211 104_0 invgauss 3956.39504 4.9222558 3.3495073 NA 0.217238794
212 104_0 logis 4385.37183 4.2303139 2.4083279 NA 0.151912730
213 104_0 invgamma 4047.58285 1.2699648 2.6185271 NA 0.064249033
214 104_0 gengamma 3917.41174 1.5464011 0.9657413 0.86335757 0.076024814
215 105_0 hnorm 1748.68363 3.8993514 NA NA 0.135599866
216 105_0 exp 1799.62488 0.3178830 NA NA 0.015613557
217 105_0 lnorm 1776.27198 0.8880789 0.7799354 NA 0.039359768
218 105_0 gamma 1747.63135 1.7981408 0.5671938 NA 0.133952654
219 105_0 weibull 1744.78015 1.4050750 3.4753995 NA 0.058115898
220 105_0 invgauss 1780.66928 3.1864124 4.2750467 NA 0.135253949
221 105_0 logis 1868.01971 2.9234004 1.2439192 NA 0.106763098
222 105_0 invgamma 1828.66578 1.9626282 3.6858121 NA 0.140659533
223 105_0 gengamma 1746.71170 1.2620166 0.7064396 1.04740184 0.071100487
224 106_0 hnorm 680.96551 6.3281652 NA NA 0.390278320
225 106_0 exp 644.04461 0.2394041 NA NA 0.020886884
226 106_0 lnorm 644.41140 0.8800214 1.1018863 NA 0.100065701
227 106_0 gamma 644.75941 0.8644730 0.2073995 NA 0.112909169
228 106_0 weibull 644.12628 0.9051415 3.9677193 NA 0.067017236
229 106_0 invgauss 644.90963 4.2101155 2.3605804 NA 0.490099893
230 106_0 logis 759.61548 3.3155272 2.2242110 NA 0.326708131
231 106_0 invgamma 656.09025 1.2351717 1.9878564 NA 0.155327018
232 106_0 gengamma 644.53368 1.1390566 1.1264888 0.52882397 0.216466130
233 107_0 hnorm 801.80911 4.8746427 NA NA 0.262978883
234 107_0 exp 783.70680 0.2848220 NA NA 0.021727617
235 107_0 lnorm 802.82923 0.7139231 1.1596380 NA 0.094417838
236 107_0 gamma 783.44373 0.8334343 0.2382387 NA 0.103196099
237 107_0 weibull 784.56520 0.9273500 3.3892504 NA 0.066823560
238 107_0 invgauss 804.83472 3.5494815 1.9122132 NA 0.368241145
239 107_0 logis 910.38511 3.0661486 1.8466923 NA 0.246363258
240 107_0 invgamma 827.46894 1.1336422 1.5068482 NA 0.129733750
241 107_0 gengamma 777.75283 1.7401369 0.8236298 2.26415493 0.170559186
242 107_2 hnorm 1086.10558 4.8985882 NA NA 0.227216678
243 107_2 exp 1061.54498 0.2834719 NA NA 0.018593010
244 107_2 lnorm 1034.83070 0.9323673 0.8222411 NA 0.055216988
245 107_2 gamma 1048.63827 1.4704259 0.4149979 NA 0.140001675
246 107_2 weibull 1054.77947 1.1707968 3.7512571 NA 0.059404702
247 107_2 invgauss 1036.39748 3.5591040 4.0043471 NA 0.220105673
248 107_2 logis 1170.31550 2.9791613 1.5343391 NA 0.169559447
249 107_2 invgamma 1046.74692 1.9078344 3.7144429 NA 0.179088016
250 107_2 gengamma 1036.80396 0.9456040 0.8249566 0.03550689 0.099541484
251 108_0 hnorm 3239.06732 6.4421310 NA NA 0.182576710
252 108_0 exp 3319.92541 0.1915112 NA NA 0.007671950
253 108_0 lnorm 3329.16611 1.3504783 0.8623238 NA 0.034999733
254 108_0 gamma 3253.96785 1.6479936 0.3146089 NA 0.093885161
255 108_0 weibull 3242.88337 1.3762874 5.7186580 NA 0.045804407
256 108_0 invgauss 3355.43247 5.2501724 5.3742311 NA 0.207863501
257 108_0 logis 3433.98558 4.8786969 2.1182562 NA 0.149136693
258 108_0 invgamma 3461.40276 1.5008780 4.0610115 NA 0.083054060
259 108_0 gengamma 3239.24185 1.8564565 0.6758718 1.32637966 0.055343367
260 109_0 hnorm 985.80177 5.4519083 NA NA 0.271331789
261 109_0 exp 995.33514 0.2359062 NA NA 0.016595493
262 109_0 lnorm 1006.05072 1.0757768 0.9366910 NA 0.067400331
263 109_0 gamma 990.10932 1.3300945 0.3128541 NA 0.136305992
264 109_0 weibull 988.78626 1.2011911 4.5152254 NA 0.072142600
265 109_0 invgauss 1007.50890 4.2719399 3.6816970 NA 0.323619325
266 109_0 logis 1084.52437 3.8398708 1.9478778 NA 0.241242000
267 109_0 invgamma 1034.31580 1.4333519 2.9190477 NA 0.141585491
268 109_0 gengamma 988.41157 1.7736910 0.6983398 1.70628572 0.223675682
269 118_0 hnorm 203.64892 3.4824357 NA NA 0.347180724
270 118_0 exp 211.81154 0.3492715 NA NA 0.049156357
271 118_0 lnorm 205.55393 0.8296223 0.7161233 NA 0.103629572
272 118_0 gamma 203.55567 2.0929360 0.7238931 NA 0.442608045
273 118_0 weibull 203.53857 1.5174049 3.2061414 NA 0.178625470
274 118_0 invgauss 205.34059 2.9012580 4.7412230 NA 0.320277445
275 118_0 logis 217.72179 2.6902964 1.0956236 NA 0.272167531
276 118_0 invgamma 209.85353 2.3035521 4.2356323 NA 0.471711353
277 118_0 gengamma 205.47628 1.1173135 0.6736985 0.85511907 0.217675318
278 119_0 hnorm 158.20206 7.1458548 NA NA 0.939827488
279 119_0 exp 158.42950 0.1834794 NA NA 0.034118058
280 119_0 lnorm 164.54442 1.2314662 1.0912705 NA 0.207885633
281 119_0 gamma 160.38053 1.0621646 0.1947974 NA 0.287811309
282 119_0 weibull 160.24207 1.0749555 5.5986498 NA 0.175686202
283 119_0 invgauss 165.77340 5.4843957 3.2357461 NA 1.327039706
284 119_0 logis 175.72063 4.9380821 2.6426074 NA 0.866486800
285 119_0 invgamma 170.67629 1.0877697 2.2816739 NA 0.280334837
286 119_0 gengamma 160.39279 2.2336363 0.6272254 2.46752375 0.389684198
287 125_0 hnorm 1534.59464 4.0806054 NA NA 0.153047842
288 125_0 exp 1577.39568 0.3041302 NA NA 0.016113138
289 125_0 lnorm 1562.39306 0.9226283 0.7959515 NA 0.043267881
290 125_0 gamma 1536.16416 1.7521186 0.5290406 NA 0.139687663
291 125_0 weibull 1533.12642 1.3910295 3.6242742 NA 0.062140077
292 125_0 invgauss 1565.93340 3.3266516 4.2568798 NA 0.155888809
293 125_0 logis 1642.70461 3.0632314 1.3179709 NA 0.122356151
294 125_0 invgamma 1608.92007 1.8838035 3.6145106 NA 0.144560306
295 125_0 gengamma 1534.82245 1.3243076 0.7062166 1.10682576 0.077650583
296 128_1 hnorm 4471.27673 5.1285905 NA NA 0.118176049
297 128_1 exp 4389.93059 0.2681493 NA NA 0.008744306
298 128_1 lnorm 4415.89235 0.9337331 0.9369790 NA 0.031498362
299 128_1 gamma 4374.03999 1.2369992 0.3307430 NA 0.060567687
300 128_1 weibull 4376.87084 1.1142426 3.8914609 NA 0.030107816
301 128_1 invgauss 4431.23458 3.7666763 3.2133349 NA 0.132892092
302 128_1 logis 4845.95430 3.2750356 1.6906377 NA 0.094571256
303 128_1 invgamma 4536.05603 1.4876423 2.6897829 NA 0.069943981
304 128_1 gengamma 4374.84540 1.2674743 0.9127868 0.78235320 0.056245161
305 131_0 hnorm 238.71892 3.7131332 NA NA 0.346835462
306 131_0 exp 237.96283 0.3574163 NA NA 0.047180891
307 131_0 lnorm 243.37916 0.6313429 0.9674197 NA 0.135089262
308 131_0 gamma 239.67275 1.1241267 0.4006482 NA 0.240466315
309 131_0 weibull 239.49706 1.0880380 2.8948756 NA 0.131860406
310 131_0 invgauss 242.86639 2.8420477 2.3717026 NA 0.410159226
311 131_0 logis 272.95212 2.4995971 1.3763649 NA 0.320805219
312 131_0 invgamma 248.60149 1.5266664 2.0983904 NA 0.304937974
313 131_0 gengamma 237.21995 2.0302936 0.2393278 7.74987734 0.286522286
314 133_0 hnorm 155.55114 4.6198641 NA NA 0.562425408
315 133_0 exp 160.47687 0.2651077 NA NA 0.045598201
316 133_0 lnorm 154.07497 1.0973166 0.7093776 NA 0.124120397
317 133_0 gamma 154.02285 2.1679610 0.5716874 NA 0.526223752
318 133_0 weibull 154.92339 1.5096071 4.2173721 NA 0.203741391
319 133_0 invgauss 153.85982 3.7961040 6.1666910 NA 0.513106722
320 133_0 logis 164.93451 3.4725511 1.4231825 NA 0.426662648
321 133_0 invgamma 156.59749 2.2685882 5.3994100 NA 0.549342917
322 133_0 gengamma 155.71217 1.2203364 0.7027501 0.35803631 0.236419214
323 134_0 hnorm 307.99006 4.2956617 NA NA 0.364677459
324 134_0 exp 314.00068 0.2937611 NA NA 0.035237139
325 134_0 lnorm 301.14332 0.9862677 0.7219355 NA 0.088425548
326 134_0 gamma 301.40920 2.0472757 0.5975521 NA 0.352758070
327 134_0 weibull 303.92666 1.4240380 3.7792371 NA 0.130435236
328 134_0 invgauss 301.13139 3.4324492 5.3315048 NA 0.330909858
329 134_0 logis 321.86594 3.1612197 1.2849411 NA 0.268463652
330 134_0 invgamma 306.51578 2.2224878 4.7172383 NA 0.378284571
331 134_0 gengamma 302.37502 1.1033311 0.7198087 0.33926105 0.156449009
332 135_0 hnorm 2062.40474 5.9274765 NA NA 0.207234776
333 135_0 exp 2142.16531 0.2015107 NA NA 0.009956368
334 135_0 lnorm 2095.81710 1.3611796 0.7612268 NA 0.038040915
335 135_0 gamma 2052.44134 2.0764125 0.4169178 NA 0.144899953
336 135_0 weibull 2044.16049 1.5661590 5.5372294 NA 0.063234830
337 135_0 invgauss 2109.27614 4.9875378 6.8399668 NA 0.210526793
338 135_0 logis 2137.78387 4.7207944 1.8292676 NA 0.159135251
339 135_0 invgamma 2175.15129 1.8344736 5.3604406 NA 0.125863899
340 135_0 gengamma 2044.90370 1.7691996 0.6133321 1.18335981 0.060600788
se2 se3
1 NA NA
2 NA NA
3 0.03696496 NA
4 0.03515710 NA
5 0.20405702 NA
6 0.41547646 NA
7 0.08275183 NA
8 0.36331315 NA
9 0.04804562 0.22620815
10 NA NA
11 NA NA
12 0.03878895 NA
13 0.03781596 NA
14 0.24924684 NA
15 0.55813242 NA
16 0.09921562 NA
17 0.48436603 NA
18 0.04251064 0.19854791
19 NA NA
20 NA NA
21 0.04716303 NA
22 0.06488566 NA
23 0.24498194 NA
24 0.66953867 NA
25 0.09955148 NA
26 0.59907003 NA
27 0.05647364 0.29710497
28 NA NA
29 NA NA
30 0.04310527 NA
31 0.04755171 NA
32 0.18838774 NA
33 0.39107713 NA
34 0.07368185 NA
35 0.35586860 NA
36 0.04607035 0.21107458
37 NA NA
38 NA NA
39 0.02608888 NA
40 0.02189122 NA
41 0.18027835 NA
42 0.40063932 NA
43 0.07304418 NA
44 0.32875183 NA
45 0.03200087 0.15514218
46 NA NA
47 NA NA
48 0.29269885 NA
49 1.52246257 NA
50 0.47408212 NA
51 3.44203272 NA
52 0.20403474 NA
53 3.49246402 NA
54 NA NA
55 NA NA
56 0.04657813 NA
57 0.07457987 NA
58 0.20133554 NA
59 0.55197109 NA
60 0.07737419 NA
61 0.51714374 NA
62 0.04677023 0.23777597
63 NA NA
64 NA NA
65 0.02311644 NA
66 0.01880392 NA
67 0.13717166 NA
68 0.25782185 NA
69 0.05399171 NA
70 0.21774459 NA
71 0.02503094 0.10713912
72 NA NA
73 NA NA
74 0.02925577 NA
75 0.02172158 NA
76 0.23014809 NA
77 0.51092800 NA
78 0.09301558 NA
79 0.40724047 NA
80 0.03455947 0.17428449
81 NA NA
82 NA NA
83 0.02131751 NA
84 0.01633577 NA
85 0.15091560 NA
86 0.34491853 NA
87 0.06012294 NA
88 0.26625316 NA
89 0.01791465 0.09055254
90 NA NA
91 NA NA
92 0.02599841 NA
93 0.03310929 NA
94 0.13530096 NA
95 0.33343345 NA
96 0.05398582 NA
97 0.30380521 NA
98 0.02753747 0.14519248
99 NA NA
100 NA NA
101 0.05282021 NA
102 0.10621454 NA
103 0.19824490 NA
104 0.61487494 NA
105 0.07735657 NA
106 0.60712462 NA
107 0.05284602 0.32670323
108 NA NA
109 NA NA
110 0.05311654 NA
111 0.08076324 NA
112 0.19634880 NA
113 0.47864716 NA
114 0.07736072 NA
115 0.46935699 NA
116 0.05408072 0.31858117
117 NA NA
118 NA NA
119 0.03793000 NA
120 0.01855123 NA
121 0.23525451 NA
122 0.27052238 NA
123 0.09407164 NA
124 0.23367626 NA
125 0.04239155 0.16856071
126 NA NA
127 NA NA
128 0.03343266 NA
129 0.04728175 NA
130 0.17228421 NA
131 0.47938616 NA
132 0.06896055 NA
133 0.43260955 NA
134 0.03578577 0.18436440
135 NA NA
136 NA NA
137 0.18903195 NA
138 0.12491423 NA
139 0.50316901 NA
140 0.61580097 NA
141 0.19630039 NA
142 0.60888064 NA
143 NA NA
144 NA NA
145 0.03070431 NA
146 0.01648148 NA
147 0.19393209 NA
148 0.26896417 NA
149 0.07616162 NA
150 0.21714941 NA
151 0.03387011 0.15375545
152 NA NA
153 NA NA
154 0.04892087 NA
155 0.04037638 NA
156 0.25361566 NA
157 0.44442827 NA
158 0.10193754 NA
159 0.38945515 NA
160 0.06301221 0.28525462
161 NA NA
162 NA NA
163 0.06004384 NA
164 0.06100403 NA
165 0.25875939 NA
166 0.48023163 NA
167 0.10047427 NA
168 0.45256722 NA
169 0.06204964 0.30210655
170 NA NA
171 NA NA
172 0.02483601 NA
173 0.02967882 NA
174 0.26594323 NA
175 1.49483488 NA
176 0.11121738 NA
177 1.15168247 NA
178 0.02155646 0.13019346
179 NA NA
180 NA NA
181 0.04796199 NA
182 0.03201310 NA
183 0.26105037 NA
184 0.39798269 NA
185 0.10373226 NA
186 0.33585272 NA
187 0.05940893 0.26973211
188 NA NA
189 NA NA
190 0.08006517 NA
191 0.18882872 NA
192 0.25469269 NA
193 0.86860251 NA
194 0.10286232 NA
195 0.86647284 NA
196 0.08531420 0.62555528
197 NA NA
198 NA NA
199 0.04641870 NA
200 0.09024433 NA
201 0.21602545 NA
202 0.75179090 NA
203 0.08445015 NA
204 0.69500385 NA
205 0.04704184 0.25347646
206 NA NA
207 NA NA
208 0.02986596 NA
209 0.01404648 NA
210 0.18390753 NA
211 0.19927970 NA
212 0.07415305 NA
213 0.17413529 NA
214 0.03404707 0.14200174
215 NA NA
216 NA NA
217 0.03118866 NA
218 0.04659878 NA
219 0.12950964 NA
220 0.34852895 NA
221 0.05173629 NA
222 0.32017375 NA
223 0.03508750 0.17834473
224 NA NA
225 NA NA
226 0.08191775 NA
227 0.03303844 NA
228 0.41037194 NA
229 0.35950668 NA
230 0.16937174 NA
231 0.34218025 NA
232 0.08242011 0.37899596
233 NA NA
234 NA NA
235 0.08120997 NA
236 0.03507174 NA
237 0.30129210 NA
238 0.27662260 NA
239 0.11850328 NA
240 0.25222754 NA
241 0.10716097 0.48143897
242 NA NA
243 NA NA
244 0.04281822 NA
245 0.04505599 NA
246 0.22428638 NA
247 0.42466422 NA
248 0.08813384 NA
249 0.42129163 NA
250 0.04567253 0.21942764
251 NA NA
252 NA NA
253 0.02678797 NA
254 0.02034319 NA
255 0.17610218 NA
256 0.33824034 NA
257 0.07047167 NA
258 0.27943348 NA
259 0.03138857 0.14211558
260 NA NA
261 NA NA
262 0.05286879 NA
263 0.03703391 NA
264 0.28122089 NA
265 0.42312474 NA
266 0.11489950 NA
267 0.36887488 NA
268 0.13372291 0.64672198
269 NA NA
270 NA NA
271 0.08055441 NA
272 0.16674615 NA
273 0.31759592 NA
274 1.08865369 NA
275 0.13002865 NA
276 1.02203122 NA
277 0.09639627 0.58280696
278 NA NA
279 NA NA
280 0.16622188 NA
281 0.06303031 NA
282 1.02816403 NA
283 1.00188349 NA
284 0.40854848 NA
285 0.81109462 NA
286 0.26407179 1.47971117
287 NA NA
288 NA NA
289 0.03415129 NA
290 0.04673619 NA
291 0.14703486 NA
292 0.37227524 NA
293 0.05875731 NA
294 0.33793453 NA
295 0.03935433 0.19478576
296 NA NA
297 NA NA
298 0.02534405 NA
299 0.01869039 NA
300 0.12151466 NA
301 0.17732655 NA
302 0.04675641 NA
303 0.16297196 NA
304 0.02572051 0.11020461
305 NA NA
306 NA NA
307 0.11345750 NA
308 0.09741833 NA
309 0.37889173 NA
310 0.57505290 NA
311 0.15334633 NA
312 0.55705621 NA
313 0.34289683 11.41698144
314 NA NA
315 NA NA
316 0.09259501 NA
317 0.15310048 NA
318 0.51127750 NA
319 1.63926577 NA
320 0.20689775 NA
321 1.52189903 NA
322 0.09430727 0.58690333
323 NA NA
324 NA NA
325 0.06685443 NA
326 0.11365844 NA
327 0.33892139 NA
328 1.00616966 NA
329 0.12884925 NA
330 0.94217921 NA
331 0.06684391 0.37338208
332 NA NA
333 NA NA
334 0.02861180 NA
335 0.03228532 NA
336 0.18480255 NA
337 0.52134514 NA
338 0.07491888 NA
339 0.43871945 NA
340 0.03369890 0.16641702There they all are! The generalized gamma didn’t always converge, which is the source of the warnings and the fact that it is missing from many places.
Now lets calculate the delta-AIC within each dataset:
result <- group_by(result, ID) %>% mutate(delta_AIC = AIC - min(AIC))
print(result, n=50) # A tibble: 340 x 10
# Groups: ID [38]
ID model AIC par1 par2 par3 se1 se2 se3 delta_AIC
<fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 73_0 hnorm 1531. 5.27 NA NA 0.209 NA NA 0
2 73_0 exp 1564. 0.237 NA NA 0.0133 NA NA 32.9
3 73_0 lnorm 1556. 1.14 0.843 NA 0.0481 0.0370 NA 25.1
4 73_0 gamma 1533. 1.63 0.386 NA 0.131 0.0352 NA 2.25
5 73_0 weib… 1531. 1.34 4.60 NA 0.0625 0.204 NA 0.183
6 73_0 invg… 1559. 4.24 4.66 NA 0.227 0.415 NA 28.4
7 73_0 logis 1647. 3.89 1.77 NA 0.175 0.0828 NA 117.
8 73_0 invg… 1601. 1.66 3.77 NA 0.130 0.363 NA 70.1
9 73_0 geng… 1533. 1.57 0.727 1.12 0.0930 0.0480 0.226 1.90
10 75_0 hnorm 1266. 5.88 NA NA 0.262 NA NA 7.76
11 75_0 exp 1294. 0.212 NA NA 0.0133 NA NA 35.4
12 75_0 lnorm 1274. 1.27 0.805 NA 0.0514 0.0388 NA 15.0
13 75_0 gamma 1259. 1.79 0.378 NA 0.159 0.0378 NA 0
14 75_0 weib… 1260. 1.39 5.20 NA 0.0701 0.249 NA 0.928
15 75_0 invg… 1279. 4.75 5.70 NA 0.273 0.558 NA 19.8
16 75_0 logis 1340. 4.33 1.87 NA 0.205 0.0992 NA 80.9
17 75_0 invg… 1312. 1.74 4.59 NA 0.152 0.484 NA 53.3
18 75_0 geng… 1261. 1.58 0.740 0.801 0.0878 0.0425 0.199 1.93
19 75_1 hnorm 714. 4.66 NA NA 0.264 NA NA 3.87
20 75_1 exp 736. 0.263 NA NA 0.0210 NA NA 26.7
21 75_1 lnorm 717. 1.08 0.763 NA 0.0621 0.0472 NA 7.69
22 75_1 gamma 710. 1.95 0.509 NA 0.223 0.0649 NA 0
23 75_1 weib… 710. 1.47 4.23 NA 0.0958 0.245 NA 0.0401
24 75_1 invg… 717. 3.83 5.32 NA 0.261 0.670 NA 7.83
25 75_1 logis 757. 3.55 1.49 NA 0.209 0.0996 NA 47.2
26 75_1 invg… 735. 1.97 4.46 NA 0.222 0.599 NA 25.5
27 75_1 geng… 711. 1.39 0.699 0.851 0.120 0.0565 0.297 1.79
28 77_0 hnorm 1154. 4.32 NA NA 0.189 NA NA 2.05
29 77_0 exp 1171. 0.296 NA NA 0.0183 NA NA 18.1
30 77_0 lnorm 1172. 0.914 0.848 NA 0.0540 0.0431 NA 19.8
31 77_0 gamma 1153. 1.54 0.453 NA 0.144 0.0476 NA 1.05
32 77_0 weib… 1152. 1.28 3.67 NA 0.0669 0.188 NA 0
33 77_0 invg… 1176. 3.42 3.77 NA 0.202 0.391 NA 23.2
34 77_0 logis 1245. 3.11 1.42 NA 0.153 0.0737 NA 93.0
35 77_0 invg… 1207. 1.72 3.18 NA 0.154 0.356 NA 55.0
36 77_0 geng… 1154. 1.31 0.776 1.02 0.0931 0.0461 0.211 1.99
37 78_2 hnorm 3047. 6.61 NA NA 0.194 NA NA 10.7
38 78_2 exp 3145. 0.183 NA NA 0.00759 NA NA 109.
39 78_2 lnorm 3127. 1.42 0.823 NA 0.0346 0.0261 NA 90.9
40 78_2 gamma 3055. 1.83 0.333 NA 0.107 0.0219 NA 19.0
41 78_2 weib… 3041. 1.47 6.05 NA 0.0505 0.180 NA 5.12
42 78_2 invg… 3151. 5.49 6.24 NA 0.214 0.401 NA 115.
43 78_2 logis 3195. 5.17 2.12 NA 0.155 0.0730 NA 159.
44 78_2 invg… 3253. 1.59 4.71 NA 0.0907 0.329 NA 217.
45 78_2 geng… 3036. 1.93 0.618 1.39 0.0548 0.0320 0.155 0
46 79_0 hnorm 14.8 1.72 NA NA 0.560 NA NA 0
47 79_0 exp 15.9 0.693 NA NA 0.316 NA NA 1.03
48 79_0 lnorm 17.6 0.248 0.630 NA 0.320 0.293 NA 2.72
49 79_0 gamma 17.1 2.47 1.64 NA 2.27 1.52 NA 2.23
50 79_0 weib… 16.7 1.79 1.69 NA 0.883 0.474 NA 1.91
# ... with 290 more rowsAnd plot the results:
ggplot(result, aes(x=delta_AIC, group = model)) + geom_histogram() + facet_wrap(~model, scales = "free") `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Only the generalized gamma has consistently small delta-AIC values, suggesting that I should try harder on this. Let’s look at a particular case:
temp <- filter(disperseLer, ID == "73_0")
cens_data <- cens_dispersal_data(temp, 7)
start <- start_params(cens_data, "gengamma")
start[3] <- 0
library(flexsurv)
# dgengamma <- function(x, mu, sigma, Q) {
# print(x)
# print(c(mu, sigma, Q))
# (x > 0) * flexsurv::dgengamma(x, mu, sigma, Q)
# }
fit <- try(fitdistcens(cens_data, "gengamma", start = start, silent = FALSE, lower = c(-Inf, 0, -Inf), optim.method = "L-BFGS-B", control=list(trace=0)))<simpleError in optim(par = vstart, fn = fnobjcens, fix.arg = fix.arg, gr = gradient, rcens = rcens, lcens = lcens, icens = icens, ncens = ncens, ddistnam = ddistname, pdistnam = pdistname, hessian = TRUE, method = meth, lower = lower, upper = upper, ...): L-BFGS-B needs finite values of 'fn'>fit <- try(fitdist(cens_data[,2], "gengamma", start = start, silent = FALSE, lower = c(-Inf, 0, -Inf), optim.method = "L-BFGS-B", control=list(trace=0)))
summary(fit)Fitting of the distribution ' gengamma ' by maximum likelihood
Parameters :
estimate Std. Error
mu 1.4644461 0.07501084
sigma 0.6922186 0.03164702
Q 0.4123414 0.18156173
Loglikelihood: -767.0602 AIC: 1540.12 BIC: 1551.425
Correlation matrix:
mu sigma Q
mu 1.0000000 -0.4631829 0.8533900
sigma -0.4631829 1.0000000 -0.4651418
Q 0.8533900 -0.4651418 1.0000000fit$est mu sigma Q
1.4644461 0.6922186 0.4123414 fit <- try(fitdistcens(cens_data, "gengamma", start = as.list(fit$est), silent = FALSE, lower = c(-Inf, 0, -Inf), optim.method = "L-BFGS-B", control=list(trace=0)))
summary(fit)Fitting of the distribution ' gengamma ' By maximum likelihood on censored data
Parameters
estimate Std. Error
mu 1.5703197 0.09299907
sigma 0.7275757 0.04804571
Q 1.1181647 0.22618933
Loglikelihood: -763.355 AIC: 1532.71 BIC: 1544.015
Correlation matrix:
mu sigma Q
mu 1.0000000 -0.7393269 0.8825932
sigma -0.7393269 1.0000000 -0.7010293
Q 0.8825932 -0.7010293 1.0000000Got it to fit almost everywhere, and still is always better than 4. Some cases where it is 2, suggesting a limiting distribution fits equally. But sometimes it’s at zero.
result2 <- filter(result, ID != "90_1", ID!="79_0")
plot(filter(result2, model == "gengamma")$delta_AIC, filter(result2, model == "weibull")$delta_AIC, xlab = "gengamma delta_AIC", ylab = "weibull delta_AIC")
plot(filter(result2, model == "gengamma")$delta_AIC, filter(result2, model == "gamma")$delta_AIC, xlab = "gengamma delta_AIC", ylab = "gamma delta_AIC")
plot(filter(result2, model == "gengamma")$delta_AIC, filter(result2, model == "lnorm")$delta_AIC, xlab = "gengamma delta_AIC", ylab = "lnorm delta_AIC")