我试图弄清楚如何将已发布的模型拟合到数据集。
模型和方程的详细信息如下所示1
我有一个来自1中的方程的表观扩散 (D*) 数据框以及步数 (n)。
我需要使我的数据分布适合这个模型2
这个等式中的 D 和 x 是否相同？这让我有些困惑。
数据框：
n D
6 16.1693
29 6.95744
23 7.66054
7 24.6076
14 8.88381
11 5.89875
33 8.16877
9 18.8922
10 15.2757
6 12.2641
9 4.2205
22 8.57661
5 2.43809
6 12.2284
9 3.5797
7 16.3401
4 9.23816
4 4.34992
10 5.77003
9 4.43707
8 3.90128
21 7.1065
15 9.08997
34 1.56546
5 15.2622
10 5.19957
5 5.86306
16 7.82247
12 8.11728
63 6.94382
19 10.6853
4 22.634
14 4.64683
15 8.83135
10 11.8849
20 9.88979
7 4.53146
4 5.58701
9 6.46933
5 3.92932
5 13.2794
9 21.9321
17 14.4281
5 4.33572
37 4.27755
11 4.43083
19 6.82866
9 14.927
29 4.39848
6 3.56756
7 5.00384
15 5.41498
7 9.67496
8 8.90499
41 5.59504
5 5.30941
11 4.05351
8 14.1295
5 6.94491
7 4.53821
6 7.43668
14 5.10146
4 18.2141
8 3.21058
12 6.26661
19 5.53264
7 9.14843
9 7.86705
13 7.79207
10 16.0144
37 5.61845
24 5.45841
8 3.72465
6 9.15419
8 9.50775
4 7.2576
5 6.8974
4 9.90056
5 1.76761
4 1.28818
67 9.4782
4 5.88126
8 11.8879
17 8.64635
10 7.49368
6 7.97186
39 6.78984
27 4.6375
6 7.21579
4 1.87345
22 7.53034
13 9.77943
5 7.02081
11 15.3463
8 1.42405
10 10.4216
8 9.32649
4 4.86869
4 8.69224
6 7.2981
5 6.32456
27 5.23167
8 4.26364
4 5.89355
6 15.0599
17 3.79605
75 5.10882
6 10.9887
26 4.29229
8 19.6015
8 7.4532
14 7.77257
5 4.02287
4 8.1798
15 4.50524
4 3.52175
6 3.77109
4 16.1092
5 10.2184
4 1.31019
7 10.9105
16 5.54095
7 8.26732
4 5.46121
7 3.14528
5 2.9076
7 5.38087
13 10.0418
8 6.00922
4 6.38978
4 1.16043
6 7.11854
18 6.61809
5 18.4267
8 9.4416
4 6.34204
4 3.97339
8 9.47709
25 5.72887
17 4.38445
4 9.39688
10 5.1629
6 5.14524
7 6.63644
7 6.61286
4 3.9732
5 5.45305
6 8.66114
10 13.8499
8 8.3179
7 6.27027
39 10.5038
4 11.675
4 2.10659
7 8.05701
8 9.1416
28 10.3282
8 6.09853
5 10.4296
49 6.73058
7 8.08364
5 5.56982
8 7.99871
11 9.07808
14 8.74915
9 3.05946
5 9.02942
24 4.65335
8 5.28659
10 11.7005
9 7.9019
13 1.7073
5 3.98097
4 7.1036
8 4.44103
8 10.5211
22 8.36037
6 16.5121
6 3.36161
7 3.85915
9 14.2648
13 13.9042
8 11.3231
8 4.24438
4 9.2
11 2.77842
4 8.07942
5 5.06842
7 10.0444
4 4.62739
4 9.07243
12 3.80894
45 5.08952
21 6.94786
6 12.6898
4 13.9838
4 6.27831
13 10.8506
4 17.543
6 14.3726
37 4.29764
11 2.84348
7 7.84218
7 1.62582
4 4.25611
4 11.8145
12 7.61603
7 13.6282
4 4.44655
8 16.8558
4 11.2504
4 0.940673
4 3.34161
16 3.00202
14 11.938
26 7.59963
7 5.14337
7 5.2484
5 4.44258
13 2.98905
5 5.10393
5 2.09328
37 3.90394
22 6.01075
17 3.9939
6 19.4055
10 8.05424
55 10.109
4 3.55426
11 2.94729
11 3.80218
6 10.8055
18 6.56886
8 11.0226
6 6.82045
4 2.42667
5 10.8811
18 5.87932
13 3.21932
4 3.36937
20 3.00249
8 6.54755
4 17.1626
7 9.7982
6 4.922
7 2.4292
4 3.74126
5 11.6342
12 9.81202
4 10.1603
4 6.64092
4 4.19567
4 4.72367
6 5.27054
4 3.61887
5 15.066
4 7.80564
4 0.994988
6 6.56088
7 3.02964
11 5.32366
29 8.41929
11 11.1174
4 20.5039
9 9.07833
25 9.91062
6 11.7417
6 5.18911
4 9.01824
45 8.09247
15 4.0735
12 19.8334
7 9.37491
7 9.68244
4 4.10823
7 6.03255
13 8.94892
6 20.4803
17 9.15187
4 41.0176
9 17.0149
15 5.91976
19 9.52719
7 13.8433
10 9.18984
18 10.3163
53 5.11534
12 5.64626
5 17.7302
25 9.71165
5 15.529
17 9.0897
15 10.383
5 4.60562
6 10.9851
7 7.48659
8 4.77201
7 10.4422
16 9.41269
19 7.46288
7 5.29129
8 12.6097
22 9.28684
74 9.29769
5 7.88201
6 7.6608
29 5.01021
8 10.8468
4 10.493
5 5.51872
8 4.00857
5 5.3458
6 7.53767
15 4.03411
32 12.4305
7 15.1404
11 3.89022
4 4.80646
7 12.8567
8 5.19944
18 12.2352
25 5.82377
4 6.82975
4 17.1336
11 3.01867
30 6.0417
6 7.5281
32 6.61119
11 5.75534
14 6.25002
40 9.02338
6 9.8725
36 8.97259
5 15.1208
27 12.6702
5 6.95754
13 11.1277
19 8.36837
6 6.0491
6 14.9543
9 6.33145
32 6.15491
9 7.69184
4 6.17417
8 12.5346
7 4.83411
4 0.886051
14 9.63098
4 4.27818
19 4.71805
25 9.41496
5 4.8577
6 5.43837
51 7.92163
39 4.27369
5 7.25765
4 3.39691
37 10.279
5 1.45927
11 3.73836
6 6.37452
6 9.58176
69 3.28546
4 4.44583
7 10.1013
10 4.86906
7 8.64699
4 9.77237
7 4.26821
5 9.10079
19 8.37994
5 18.0182
5 4.39684
4 4.80113
8 6.26875
5 19.2315
54 1.31136
33 6.49233
12 7.4871
9 4.54722
15 4.87368
8 8.14971
5 7.73712
11 4.88992
20 4.57845
4 7.51247
8 1.96748
19 6.29791
8 6.74184
9 9.57862
5 9.83782
7 11.3725
6 4.42387
10 7.06773
6 11.8456
32 5.79843
13 13.2486
13 7.0247
36 7.21733
6 12.7207
10 10.0221
12 6.14754
7 6.03929
40 8.57295
6 11.7657
38 7.99936
4 9.50411
4 14.496
35 8.14862
10 11.5238
4 9.42894
11 6.75271
8 12.3427
21 9.90872
5 19.3727
4 10.579
13 5.90007
4 11.4053
4 13.817
11 11.4348
4 11.6535
11 9.44845
4 6.04428
5 7.42656
4 5.17453
14 7.40451
5 1.69463
4 8.15573
9 9.86957
11 11.4412
7 9.51392
16 6.16268
29 11.407
18 6.67891
16 5.23633
16 14.3687
6 4.88755
53 6.12915
8 8.55567
16 6.76427
5 7.01236
13 9.59372
4 10.7199
5 15.8954
12 1.38346
19 9.7957
4 18.0668
10 12.7076
7 6.69635
10 8.19132
25 8.59163
5 24.893
4 12.0637
20 13.1037
5 13.2987
11 9.51644
9 4.72763
5 20.8506
6 11.21
8 8.19004
6 24.9133
13 10.3163
4 12.0486
4 5.05456
4 7.29071
6 2.54061
4 4.77332
7 6.60557
4 5.18637
7 3.80679
26 10.258
15 4.49815
4 18.1461
4 9.7945
4 10.1782
10 13.2135
13 10.0199
5 19.2617
5 3.9299
6 1.49049
19 11.7899
24 1.92224
6 4.27185
11 9.19738
4 22.0556
5 17.8052
32 6.4669
9 12.5228
4 2.9297
12 5.11769
4 8.19326
6 14.357
9 12.334
5 16.5749
10 4.01052
16 14.6244
19 5.51143
5 21.2266
20 3.81325
5 15.9669
4 2.68911
7 1.44358
4 2.87054
15 6.80376
4 1.82632
6 4.21866
7 15.0345
4 4.49516
5 8.39518
4 3.69212
7 9.75684
7 15.4615
10 18.8199
5 18.8258
10 3.36979
13 7.33419
6 17.0571
26 1.53175
9 9.9712
4 10.3649
38 0.740367
4 1.29965
8 2.09779
7 16.0598
7 3.41323
11 3.72058
6 4.00974
5 8.23234
4 6.61152
19 7.07587
7 11.4555
6 7.08395
4 10.972
23 0.917861
4 3.14026
7 4.22944
6 3.52015
13 5.96351
5 10.4885
8 1.23162
4 9.62736
8 5.63199
5 9.69307
4 6.09755
9 11.6543
4 1.27177
5 16.434
4 5.89135
11 7.45377
4 4.71543
8 7.15632
6 12.4733
4 7.30201
4 4.03364
4 12.2982
8 6.60874
18 8.95991
5 16.3711
6 8.3649
12 1.00274
28 7.70672
4 13.0514
8 7.89006
14 8.69344
13 5.71736
50 0.870717
6 5.6773
4 2.53118
12 9.18717
4 13.743
4 7.63913
8 6.71842
4 10.5403
5 9.84237
21 8.68478
4 7.18612
7 1.06827
8 0.751419
6 9.63001
4 9.1629
8 7.10638
4 3.78854
6 6.03218
5 4.09128
5 6.40873
8 5.67051
5 8.13922
7 9.8616
8 3.0221
22 6.18295
11 3.05981
16 4.56805
5 4.03661
66 6.15321
22 5.69653
28 13.8167
18 7.74219
4 2.93453
5 8.91556
6 3.67423
21 6.6657
4 4.82726
4 11.8191
4 9.79784
12 14.2138
25 5.97174
15 13.5428
8 6.80563
21 11.8932
6 14.6238
11 13.9869
52 4.2076
30 9.04408
4 12.8902
19 12.9141
8 2.48893
16 13.4498
4 17.5283
4 7.01612
11 8.76975
15 8.21731
4 7.88112
16 6.95465
6 4.00958
9 5.46003
4 8.24018
5 4.73601
22 7.0614
8 2.77676
13 3.16918
5 4.94642
24 12.0763
4 21.812
5 6.47157
7 17.0296
11 8.46085
13 10.8281
5 20.0598
10 6.53189
9 9.78346
6 9.00286
5 12.1462
32 6.18333
21 6.86122
6 5.47728
16 6.48787
21 6.24357
25 4.21758
8 0.806496
15 3.10368
12 10.4091
18 11.4366
9 6.2496
12 21.033
20 7.27648
8 10.7495
7 6.30541
4 1.25707
6 10.8126
21 11.6242
7 15.4946
9 9.24556
7 1.02894
4 10.1415
5 6.15597
4 7.75065
6 6.21703
5 2.95157
16 12.3517
24 6.18562
5 13.4244
19 8.34601
27 10.4223
4 5.66702
11 9.49525
5 15.7393
5 7.25596
6 3.7934
16 14.3714
11 17.4736
5 10.9433
4 8.8336
6 4.4724
9 4.94749
33 13.3733
4 9.9071
40 7.68868
7 15.1338
5 10.1261
10 13.2432
7 7.31594
6 3.65833
7 7.24156
5 4.34046
4 8.41281
4 5.21182
8 6.9173
4 13.2239
8 9.27886
8 7.46864
6 11.2666
10 5.00644
17 10.4805
4 6.07913
8 17.7638
4 2.60227
4 6.31402
7 7.84318
11 7.34386
7 5.68347
33 9.25309
6 12.2509
4 9.0981
14 5.07898
8 18.5004
12 15.2972
10 19.0073
4 6.60009
23 6.66052
4 11.1979
4 14.2767
4 4.65281
8 17.7194
6 7.71884
5 11.1417
8 16.0098
8 15.4062
4 1.79562
5 1.97934
5 2.50545
6 11.6727
4 5.20648
4 6.09141
4 9.60115
5 5.8942
10 8.69295
48 6.78206
7 16.3256
4 3.61786
5 3.96036
9 10.9963
10 5.13205
4 3.44912
6 7.54323
9 10.6859
5 3.75914
6 11.9806
4 12.0293
11 6.95981
9 10.7666
4 6.84392
4 3.65086
5 5.60636
10 10.64
5 13.3392
5 7.24023
5 3.13008
5 2.2695
9 13.159
4 6.40173
4 7.53556
4 5.24271
9 7.63686
4 8.55155
6 0.794395
12 0.774572
4 12.2137
4 5.07304
4 12.6834
6 2.51888
4 7.33227
4 0.986925
4 1.70198
16 9.28519
9 1.39987
17 2.91594
16 4.2964
7 8.99818
9 6.25679
25 8.93453
17 5.90314
16 6.80086
24 10.2752
16 6.28381
16 5.15422
5 18.1131
9 3.16139
6 8.04448
18 4.76119
9 6.81272
35 5.42931
21 18.2357
6 15.1372
7 6.99942
27 5.41456
21 5.28753
9 13.016
5 4.63557
30 5.15181
10 1.23659
4 9.02225
7 6.95968
9 9.88862
7 7.36041
13 6.98269
4 9.36711
25 10.8892
12 6.76683
44 5.07632
6 10.7961
29 8.59522
5 2.81338
4 6.28351
12 1.59647
5 5.16854
7 14.4913
8 3.97279
19 5.20089
4 1.96562
10 12.1537
4 15.3751
11 8.56482
33 6.64277
25 14.3409
7 16.7304
62 6.58747
4 2.42415
19 2.97408
20 3.60803
8 6.41198
12 5.37805
27 4.50585
4 21.1342
21 6.20593
5 31.3684
15 5.39253
5 5.47974
4 6.00669
47 5.71607
8 8.44368
49 5.7375
14 3.26521
10 6.21576
6 10.4837
7 5.24993
4 9.37565
22 5.34031
5 4.16423
5 2.28298
51 8.15904
5 19.6947
5 0.80783
8 8.06952
70 4.96765
6 14.0349
9 25.9748
4 3.7916
14 11.8718
5 7.06589
18 11.4422
8 5.59955
8 2.94877
10 10.992
31 8.48155
11 9.13858
5 5.24694
10 8.233
10 5.15933
5 5.13126
5 14.4717
4 3.0615
5 8.6877
9 10.4637
6 8.48307
11 5.84917
4 2.99477
6 8.59874
4 4.40055
10 6.35706
6 9.94606
8 0.977799
15 8.11636
10 3.25845
109 5.49411
8 2.32721
16 4.06833
19 2.10977
4 12.4984
7 36.9302
4 14.2224
4 6.87563
4 9.845
8 7.98671
9 8.97332
19 2.35613
7 7.49985
4 19.473
12 7.3042
11 4.04249
20 5.40606
7 3.74967
19 7.38808
17 4.79346
6 5.86213
38 10.4957
24 8.36563
18 2.84539
6 3.29085
8 4.9111
20 9.77446
5 14.169
17 4.13249
4 9.60407
18 7.58892
42 3.72497
9 6.91983
15 5.80645
4 18.5483
7 5.06625
42 5.59
29 12.5507
6 7.95824
11 11.4407
8 9.20384
4 6.70884
7 6.50464
5 4.37398
19 7.748
32 10.3775
6 6.63337
14 4.74974
7 15.4182
6 6.70726
5 6.69711
10 12.5341
4 21.0316
41 5.89733
21 2.41973
8 8.19385
26 8.1546
4 10.6347
16 5.8739
4 7.92148
9 9.24801
6 9.27338
31 5.60478
4 4.35284
8 10.701
8 4.36121
6 1.31401
4 19.5421
6 10.9634
4 6.13838
23 4.13052
5 13.2386
10 6.40519
53 6.63273
11 5.49061
6 3.9007
17 5.03859
5 8.30389
14 18.5018
28 8.8984
5 1.81496
48 8.91168
4 3.75839
21 2.90767
8 7.22742
33 8.15415
53 11.4095
8 18.7344
5 10.577
4 5.09826
5 2.07749
8 10.8144
7 10.2127
9 8.4347
10 10.0349
4 6.15027
6 2.28599
7 7.93572
9 7.34118
14 6.09031
7 7.3006
45 7.09423
4 1.33645
8 14.4526
4 7.08032
11 4.79024
4 12.0431
5 2.57087
16 4.86222
6 4.78049
6 3.96594
7 2.57981
41 3.7921
14 3.90545
24 3.87089
8 8.63085
12 19.1375
20 7.70843
47 7.20331
5 5.91793
5 9.86039
4 4.64594
10 5.77038
9 2.34492
38 7.51938
9 4.80303
12 14.0615
8 7.28326
19 9.66733
47 5.75326
5 3.38573
9 5.44017
24 6.87599
10 9.47107
12 6.79666
5 1.75299
38 7.69266
15 5.61026
7 13.5915
4 3.11537
12 6.39935
4 7.12639
8 1.2571
4 9.68493
18 3.9985
8 6.09214
9 13.1175
6 1.582
6 9.19583
56 5.94625
25 4.32547
4 6.1255
9 12.1471
9 3.53177
9 8.62854
22 10.6622
5 7.98789
7 9.76413
4 17.8281
65 5.71063
7 0.890085
29 3.80254
12 3.5459
20 6.15626
4 20.0254
5 8.32468
5 29.742
22 6.81443
4 2.47438
42 6.77664
18 10.6616
15 8.05075
4 9.02896
7 7.48956
6 2.91088
4 3.03396
10 7.53855
14 2.43732
10 9.00266
22 15.1172
4 2.10672
4 4.49333
7 3.15671
38 6.92405
8 13.0506
6 8.69566
10 7.03648
5 2.00527
6 0.954127
10 8.09208
4 4.96376
4 4.43958
14 5.58138
5 3.09601
11 3.5262
4 8.33322
6 11.5486
18 7.57425
5 2.83417
6 9.43153
4 0.918853
5 7.75331
4 3.19178
11 1.5561
13 8.39383
24 5.49064
5 4.88094
4 3.34757
4 5.12223
5 5.15522
25 8.43619
4 4.85253
4 35.8834
9 8.92566
5 16.3607
16 1.59487
7 23.8568
11 6.39793
4 13.6148
9 15.175
24 3.4752
8 17.818
18 11.6706
7 5.28669
33 4.91901
7 16.4209
7 11.1932
5 5.37852
8 6.68451
23 1.25288
4 11.253
16 14.2972
4 9.7042
11 6.75403
25 7.5279
5 20.385
15 14.051
5 13.0078
5 15.922
5 21.4825
8 2.09958
20 6.61385
4 1.41148
6 10.5498
7 3.93411
10 6.45799
15 8.61957
39 8.69927
6 10.745
13 5.41675
13 8.20857
6 14.7968
6 9.1487
4 13.636
5 21.4302
7 11.2109
5 6.34895
41 6.75438
4 8.76922
12 6.51222
4 26.8182
4 7.27081
4 8.40243
4 6.30526
5 7.61186
4 3.36989
4 2.92409
9 2.02215
7 2.40541
5 6.2039
16 5.02525
5 5.88596
7 4.80479
12 5.49408
10 8.70615
4 4.89584
11 6.04424
5 2.96187
4 5.31255
8 4.94725
8 14.5442
7 18.3454
9 15.0843
6 7.58322
10 4.6386
11 4.93008
10 6.58939
10 6.11791
4 7.74141
10 11.7516
4 12.3938
4 5.4703
6 2.49468
15 8.38246
4 8.3817
5 9.60288
22 11.7374
4 2.43527
5 10.3144
7 9.08492
4 3.18577
5 8.87359
4 10.5922
6 6.76757
5 5.52752
4 7.58328
5 4.05443
6 4.20954
8 14.4161
6 5.29189
29 5.52229
12 7.09305
16 6.37976
29 9.48505
5 19.3982
7 9.79444
5 8.1576
14 11.8035
5 8.43284
6 5.29265
11 7.02019
8 13.534
13 7.13543
27 5.71252
6 13.6545
8 8.04559
4 7.76255
4 8.08626
4 4.16599
8 3.77822
9 8.8568
23 10.7367
8 8.2191
5 1.74918
8 7.63465
13 9.32419
6 8.79706
5 15.4076
7 17.8238
34 10.2077
6 13.8557
12 8.07164
13 14.1308
12 17.0733
30 11.0101
13 10.0491
22 6.18584
9 4.20852
5 2.65289
17 8.94523
8 5.50482
6 9.53493
7 24.591
13 6.32331
19 11.3894
14 10.1854
4 12.3948
16 6.95055
13 6.17838
5 3.8309
13 8.73328
4 23.4474
14 13.4613
4 13.3889
20 9.6924
8 5.18591
8 17.0651
18 9.52647
4 6.32325
7 29.7699
6 27.8564
8 14.4558
8 5.96027
34 7.37892
6 6.57987
9 13.0858
19 10.6525
10 13.784
36 3.44008
10 4.98896
4 10.5359
4 5.80166
8 7.54147
21 1.14524
16 11.7418
8 3.41447
7 5.28356
10 5.5154
5 2.77046
9 6.69647
33 9.50621
14 6.7445
5 4.51881
8 11.509
4 16.4914
4 9.5769
8 4.15454
7 12.4834
39 7.09677
4 4.52977
9 16.6967
5 3.47655
9 8.80103
5 2.4592
40 8.09468
9 5.35303
8 6.09031
10 7.09897
21 3.46474
21 8.12347
11 11.7323
6 12.7858
7 7.12583
4 8.68419
5 8.87775
4 8.78327
28 7.22714
5 8.93233
19 7.91032
5 8.4773
5 10.0489
22 5.03526
5 9.64074
4 9.25775
4 17.4061
4 7.67731
8 6.36563
7 7.6863
6 2.00972
10 11.559
21 9.80919
19 4.61711
5 13.1991
4 16.1863
13 8.52632
13 11.713
4 6.39226
5 7.43297
17 3.0411
6 6.13122
6 6.64945
11 6.8625
5 16.6535
6 5.92925
9 9.09044
4 5.8015
4 11.7663
55 6.83198
6 17.7091
4 6.84847
10 6.96107
4 12.2952
7 15.7357
7 16.9079
5 4.77904
4 4.92745
4 12.5846
6 12.1043
15 9.16974
5 10.5594
4 9.85629
7 4.14754
5 5.73541
4 25.081
11 3.85567
5 2.75741
9 5.10188
7 8.81165
4 2.19676
5 14.0147
6 4.66927
15 5.60593
4 21.1329
6 16.578
8 5.60286
4 6.34807
13 11.3636
4 5.06866
7 13.8345
4 8.63026
5 12.6181
7 3.72891
14 11.4156
6 5.65498
4 2.00478
4 6.70047
4 2.74247
7 7.15954
6 6.97321
6 3.06345
7 5.16588
4 1.9815
7 5.71259
4 11.2309
8 13.7251
4 6.49702
17 11.5877
38 2.18001
8 4.10022
50 4.84289
41 12.7175
5 3.99481
39 4.3202
5 9.02036
18 8.49845
14 8.36378
45 7.68327
8 16.1262
47 7.72268
18 1.52906
7 6.55152
23 7.28533
17 7.63679
23 7.1983
20 9.68639
26 2.2919
10 9.31858
6 10.2451
14 10.0946
5 3.35212
13 7.92773
11 7.54864
47 2.69414
10 6.86291
9 5.20244
8 6.42733
4 10.8517
12 3.75473
33 3.27723
6 3.00593
4 15.6508
21 7.96871
12 8.75405
31 12.324
10 1.11662
7 2.15066
24 6.83173
7 3.96771
11 7.5619
4 0.785628
11 14.4666
6 5.11272
5 7.59668
5 9.04664
7 11.9044
5 5.61802
9 11.6637
4 13.5922
4 9.948
18 9.21967
8 11.062
4 14.0495
7 9.60146
47 3.41381
24 5.48075
7 9.34019
4 5.67435
11 3.40312
11 9.22323
37 5.44413
5 22.4815
6 7.20836
6 4.52437
12 10.1285
14 7.16214
20 7.26324
5 20.8528
35 5.39124
4 6.32888
5 7.0733
6 2.41672
21 11.0176
12 3.09272
6 7.32828
5 11.8614
4 13.5878
6 7.44575
37 5.42578
10 16.5581
7 12.1824
20 16.6054
20 18.147
25 4.2557
5 1.51733
4 14.4149
7 7.60474
7 7.05497
13 8.77247
47 7.80778
6 6.32124
5 1.04715
5 8.83554
22 9.06805
18 8.8941
17 2.18289
9 5.88177
9 14.1725
7 8.8699
8 4.18207
12 4.09766
21 8.31261
25 6.74651
21 6.39138
6 6.783
49 2.53901
5 26.7969
16 6.55789
9 17.4564
4 6.49943
5 5.53018
9 6.55773
8 8.22126
17 9.93245
7 10.1271
27 8.38053
4 14.6347
22 5.34149
14 11.3401
7 7.64656
15 4.97067
15 1.49902
7 6.81685
10 14.7443
26 5.19094
4 5.37994
11 9.79944
6 7.01459
8 7.79019
19 11.2786
6 5.355
5 1.26712
9 6.61735
12 7.72715
7 9.55011
67 2.79386
4 2.40071
8 7.30456
46 7.416
9 14.6428
9 7.2112
4 6.63294
7 6.79824
45 2.55119
12 7.82306
5 17.3145
14 11.1806
4 4.08319
7 14.3597
6 3.11201
7 10.0034
8 8.49861
5 11.1623
13 10.6563
6 7.88141
10 13.8632
6 4.04427
13 7.85135
39 5.87709
11 2.46188
25 5.91836
33 10.6482
25 2.37395
21 7.55021
6 6.77897
12 8.32483
7 6.37397
6 14.526
23 4.2424
5 7.73115
4 11.2847
5 13.3916
10 10.1114
5 6.17557
12 8.13141
12 9.6928
17 9.32123
4 3.97583
14 6.00034
9 3.42081
9 12.0713
50 3.79332
15 11.4132
27 5.64309
8 6.7623
7 7.22301
9 12.0606
9 4.29732
15 10.2285
5 17.1166
37 3.41162
6 8.42037
5 3.24976
5 10.4622
6 8.40845
6 1.81865
4 9.25185
23 3.93845
5 21.6799
12 10.6917
16 9.17709
5 31.985
15 17.0313
9 6.52704
7 3.83975
6 2.99867
6 4.39106
21 8.07896
5 6.83469
4 16.8171
6 3.72637
4 4.83495
44 6.07658
22 13.059
6 20.6309
12 4.32898
24 5.63681
4 12.7224
4 27.9618
79 2.83718
25 7.71501
14 7.70377
5 4.41151
10 6.56613
41 3.03904
4 9.90708
4 0.779275
76 4.11521
5 5.35364
10 9.24407
7 2.74147
6 16.4438
6 15.0684
15 2.0893
5 24.5116
5 15.2534
21 3.9638
5 9.42489
34 2.57225
4 7.94264
32 3.59779
8 6.8339
4 1.8738
26 2.59653
4 20.1512
4 3.10731
12 1.38828
4 5.32289
8 2.69905
4 6.26715
7 8.92059
13 4.86136
37 2.38795
5 8.40931
5 10.9193
5 9.2014
7 15.5844
4 10.3963
7 6.36847
16 4.29113
32 5.53641
33 10.2215
8 7.71818
7 14.1442
6 2.99928
4 4.21772
11 6.87553
12 1.34241
18 14.0353
47 2.96704
23 3.70181
7 8.57761
22 6.07572
8 3.50741
43 4.82435
66 6.11313
4 4.73957
14 3.21847
5 6.85557
11 6.90241
4 6.7317
15 2.17576
5 8.61794
5 6.015
4 11.7745
8 1.78445
21 5.67131
4 16.9644
31 0.708555
8 2.1312
16 7.07874
4 8.99811
6 5.80737
82 6.57077
15 9.81252
14 10.0203
11 8.51049
5 4.66913
14 5.1661
28 7.92444
4 4.87349
9 8.18891
4 9.17976
5 2.1189
16 8.0799
6 7.58069
7 4.17751
7 4.41031
13 5.13741
4 2.75857
4 6.82822
4 3.02871
9 13.6115
7 6.33422
7 2.90223
18 8.23854
9 3.79853
6 9.56885
6 9.86923
6 7.31371
6 5.71552
7 4.56533
4 10.0795
46 5.88529
4 18.5347
6 9.14274
9 9.26039
6 8.31048
5 9.35301
7 4.82153
4 3.88126
4 10.8491
4 2.9009
4 7.24601
5 8.66639
5 4.73344
6 5.41322
4 9.48994
14 7.08554
4 9.26597
5 3.9454
4 9.51764
9 4.53588
4 8.6098
7 4.80259
5 3.88361
5 7.41984
9 2.16905
4 6.00152
5 4.41005
19 9.51822
6 7.56169
6 8.70246
10 8.40757
6 5.65566
5 4.26509
21 8.19307
6 5.18222
12 9.12786
4 5.12812
5 14.599
10 5.49054
5 2.03264
6 4.32049
7 2.21361
6 7.26104
5 5.40368
8 2.92935
4 7.79551
4 9.65819
7 8.60384
5 1.68915
7 7.27015
6 6.23645
6 2.48142
11 6.32948
6 2.4595
7 4.95916
11 5.57612
11 4.11866
5 4.14263
10 5.33286
5 4.03639
4 7.61417
8 6.38952
8 4.65564
4 13.087
4 5.47296
4 5.76844
4 2.74936
拟合应该看起来像伽马分布拟合。
任何有关用于创建拟合然后使用 ggplot 绘图的代码的建议都会有很大帮助。就像是？？：
function(D) (((n/D)^n)*D^(n-1)*exp(-n*D/D))/(n-1)
我当前的分布图代码：
p <- ggplot(df) +
geom_histogram(aes(x = D, y = ..density..),
binwidth = 0.5, fill = "grey", color = "black")
