1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
|
<!DOCTYPE html>
<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This function uses saemix::saemix() as a backend for fitting nonlinear mixed
effects models created from mmkin row objects using the Stochastic Approximation
Expectation Maximisation algorithm (SAEM)."><title>Fit nonlinear mixed models with SAEM — saem • mkin</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.2.2/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.2.2/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Fit nonlinear mixed models with SAEM — saem"><meta property="og:description" content="This function uses saemix::saemix() as a backend for fitting nonlinear mixed
effects models created from mmkin row objects using the Stochastic Approximation
Expectation Maximisation algorithm (SAEM)."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
<![endif]--></head><body>
<a href="#main" class="visually-hidden-focusable">Skip to contents</a>
<nav class="navbar fixed-top navbar-default navbar-expand-lg bg-light"><div class="container">
<a class="navbar-brand me-2" href="../index.html">mkin</a>
<small class="nav-text text-default me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.2.6</small>
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
<span class="navbar-toggler-icon"></span>
</button>
<div id="navbar" class="collapse navbar-collapse ms-3">
<ul class="navbar-nav me-auto"><li class="active nav-item">
<a class="nav-link" href="../reference/index.html">Reference</a>
</li>
<li class="nav-item dropdown">
<a href="#" class="nav-link dropdown-toggle" data-bs-toggle="dropdown" role="button" aria-expanded="false" aria-haspopup="true" id="dropdown-articles">Articles</a>
<div class="dropdown-menu" aria-labelledby="dropdown-articles">
<a class="dropdown-item" href="../articles/mkin.html">Introduction to mkin</a>
<div class="dropdown-divider"></div>
<h6 class="dropdown-header" data-toc-skip>Example evaluations with (generalised) nonlinear least squares</h6>
<a class="dropdown-item" href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
<a class="dropdown-item" href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
<a class="dropdown-item" href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
<div class="dropdown-divider"></div>
<h6 class="dropdown-header" data-toc-skip>Example evaluations with hierarchical models (nonlinear mixed-effects models)</h6>
<a class="dropdown-item" href="../articles/prebuilt/2022_dmta_parent.html">Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P</a>
<a class="dropdown-item" href="../articles/prebuilt/2022_dmta_pathway.html">Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P</a>
<a class="dropdown-item" href="../articles/prebuilt/2023_mesotrione_parent.html">Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione</a>
<a class="dropdown-item" href="../articles/prebuilt/2022_cyan_pathway.html">Testing hierarchical pathway kinetics with residue data on cyantraniliprole</a>
<a class="dropdown-item" href="../articles/web_only/dimethenamid_2018.html">Comparison of saemix and nlme evaluations of dimethenamid data from 2018</a>
<a class="dropdown-item" href="../articles/web_only/multistart.html">Short demo of the multistart method</a>
<div class="dropdown-divider"></div>
<h6 class="dropdown-header" data-toc-skip>Performance</h6>
<a class="dropdown-item" href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
<a class="dropdown-item" href="../articles/web_only/benchmarks.html">Benchmark timings for mkin</a>
<a class="dropdown-item" href="../articles/web_only/saem_benchmarks.html">Benchmark timings for saem.mmkin</a>
<div class="dropdown-divider"></div>
<h6 class="dropdown-header" data-toc-skip>Miscellaneous</h6>
<a class="dropdown-item" href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
<a class="dropdown-item" href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
</div>
</li>
<li class="nav-item">
<a class="nav-link" href="../news/index.html">News</a>
</li>
</ul><form class="form-inline my-2 my-lg-0" role="search">
<input type="search" class="form-control me-sm-2" aria-label="Toggle navigation" name="search-input" data-search-index="../search.json" id="search-input" placeholder="Search for" autocomplete="off"></form>
<ul class="navbar-nav"><li class="nav-item">
<a class="external-link nav-link" href="https://github.com/jranke/mkin/" aria-label="github">
<span class="fab fa fab fa-github fa-lg"></span>
</a>
</li>
</ul></div>
</div>
</nav><div class="container template-reference-topic">
<div class="row">
<main id="main" class="col-md-9"><div class="page-header">
<img src="" class="logo" alt=""><h1>Fit nonlinear mixed models with SAEM</h1>
<small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/saem.R" class="external-link"><code>R/saem.R</code></a></small>
<div class="d-none name"><code>saem.Rd</code></div>
</div>
<div class="ref-description section level2">
<p>This function uses <code><a href="https://rdrr.io/pkg/saemix/man/saemix.html" class="external-link">saemix::saemix()</a></code> as a backend for fitting nonlinear mixed
effects models created from <a href="mmkin.html">mmkin</a> row objects using the Stochastic Approximation
Expectation Maximisation algorithm (SAEM).</p>
</div>
<div class="section level2">
<h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
<div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">saem</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></span>
<span></span>
<span><span class="co"># S3 method for mmkin</span></span>
<span><span class="fu">saem</span><span class="op">(</span></span>
<span> <span class="va">object</span>,</span>
<span> transformations <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"mkin"</span>, <span class="st">"saemix"</span><span class="op">)</span>,</span>
<span> error_model <span class="op">=</span> <span class="st">"auto"</span>,</span>
<span> degparms_start <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">numeric</a></span><span class="op">(</span><span class="op">)</span>,</span>
<span> test_log_parms <span class="op">=</span> <span class="cn">TRUE</span>,</span>
<span> conf.level <span class="op">=</span> <span class="fl">0.6</span>,</span>
<span> solution_type <span class="op">=</span> <span class="st">"auto"</span>,</span>
<span> covariance.model <span class="op">=</span> <span class="st">"auto"</span>,</span>
<span> omega.init <span class="op">=</span> <span class="st">"auto"</span>,</span>
<span> covariates <span class="op">=</span> <span class="cn">NULL</span>,</span>
<span> covariate_models <span class="op">=</span> <span class="cn">NULL</span>,</span>
<span> no_random_effect <span class="op">=</span> <span class="cn">NULL</span>,</span>
<span> error.init <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">)</span>,</span>
<span> nbiter.saemix <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">300</span>, <span class="fl">100</span><span class="op">)</span>,</span>
<span> control <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>displayProgress <span class="op">=</span> <span class="cn">FALSE</span>, print <span class="op">=</span> <span class="cn">FALSE</span>, nbiter.saemix <span class="op">=</span> <span class="va">nbiter.saemix</span>,</span>
<span> save <span class="op">=</span> <span class="cn">FALSE</span>, save.graphs <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>,</span>
<span> verbose <span class="op">=</span> <span class="cn">FALSE</span>,</span>
<span> quiet <span class="op">=</span> <span class="cn">FALSE</span>,</span>
<span> <span class="va">...</span></span>
<span><span class="op">)</span></span>
<span></span>
<span><span class="co"># S3 method for saem.mmkin</span></span>
<span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">x</span>, digits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="fl">3</span>, <span class="fu"><a href="https://rdrr.io/r/base/options.html" class="external-link">getOption</a></span><span class="op">(</span><span class="st">"digits"</span><span class="op">)</span> <span class="op">-</span> <span class="fl">3</span><span class="op">)</span>, <span class="va">...</span><span class="op">)</span></span>
<span></span>
<span><span class="fu">saemix_model</span><span class="op">(</span></span>
<span> <span class="va">object</span>,</span>
<span> solution_type <span class="op">=</span> <span class="st">"auto"</span>,</span>
<span> transformations <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"mkin"</span>, <span class="st">"saemix"</span><span class="op">)</span>,</span>
<span> error_model <span class="op">=</span> <span class="st">"auto"</span>,</span>
<span> degparms_start <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">numeric</a></span><span class="op">(</span><span class="op">)</span>,</span>
<span> covariance.model <span class="op">=</span> <span class="st">"auto"</span>,</span>
<span> no_random_effect <span class="op">=</span> <span class="cn">NULL</span>,</span>
<span> omega.init <span class="op">=</span> <span class="st">"auto"</span>,</span>
<span> covariates <span class="op">=</span> <span class="cn">NULL</span>,</span>
<span> covariate_models <span class="op">=</span> <span class="cn">NULL</span>,</span>
<span> error.init <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html" class="external-link">numeric</a></span><span class="op">(</span><span class="op">)</span>,</span>
<span> test_log_parms <span class="op">=</span> <span class="cn">FALSE</span>,</span>
<span> conf.level <span class="op">=</span> <span class="fl">0.6</span>,</span>
<span> verbose <span class="op">=</span> <span class="cn">FALSE</span>,</span>
<span> <span class="va">...</span></span>
<span><span class="op">)</span></span>
<span></span>
<span><span class="fu">saemix_data</span><span class="op">(</span><span class="va">object</span>, covariates <span class="op">=</span> <span class="cn">NULL</span>, verbose <span class="op">=</span> <span class="cn">FALSE</span>, <span class="va">...</span><span class="op">)</span></span></code></pre></div>
</div>
<div class="section level2">
<h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
<dl><dt>object</dt>
<dd><p>An <a href="mmkin.html">mmkin</a> row object containing several fits of the same
<a href="mkinmod.html">mkinmod</a> model to different datasets</p></dd>
<dt>...</dt>
<dd><p>Further parameters passed to <a href="https://rdrr.io/pkg/saemix/man/saemixModel.html" class="external-link">saemix::saemixModel</a>.</p></dd>
<dt>transformations</dt>
<dd><p>Per default, all parameter transformations are done
in mkin. If this argument is set to 'saemix', parameter transformations
are done in 'saemix' for the supported cases, i.e. (as of version 1.1.2)
SFO, FOMC, DFOP and HS without fixing <code>parent_0</code>, and SFO or DFOP with
one SFO metabolite.</p></dd>
<dt>error_model</dt>
<dd><p>Possibility to override the error model used in the mmkin object</p></dd>
<dt>degparms_start</dt>
<dd><p>Parameter values given as a named numeric vector will
be used to override the starting values obtained from the 'mmkin' object.</p></dd>
<dt>test_log_parms</dt>
<dd><p>If TRUE, an attempt is made to use more robust starting
values for population parameters fitted as log parameters in mkin (like
rate constants) by only considering rate constants that pass the t-test
when calculating mean degradation parameters using <a href="mean_degparms.html">mean_degparms</a>.</p></dd>
<dt>conf.level</dt>
<dd><p>Possibility to adjust the required confidence level
for parameter that are tested if requested by 'test_log_parms'.</p></dd>
<dt>solution_type</dt>
<dd><p>Possibility to specify the solution type in case the
automatic choice is not desired</p></dd>
<dt>covariance.model</dt>
<dd><p>Will be passed to <code><a href="https://rdrr.io/pkg/saemix/man/saemixModel.html" class="external-link">saemix::saemixModel()</a></code>. Per
default, uncorrelated random effects are specified for all degradation
parameters.</p></dd>
<dt>omega.init</dt>
<dd><p>Will be passed to <code><a href="https://rdrr.io/pkg/saemix/man/saemixModel.html" class="external-link">saemix::saemixModel()</a></code>. If using
mkin transformations and the default covariance model with optionally
excluded random effects, the variances of the degradation parameters
are estimated using <a href="mean_degparms.html">mean_degparms</a>, with testing of untransformed
log parameters for significant difference from zero. If not using
mkin transformations or a custom covariance model, the default
initialisation of <a href="https://rdrr.io/pkg/saemix/man/saemixModel.html" class="external-link">saemix::saemixModel</a> is used for omega.init.</p></dd>
<dt>covariates</dt>
<dd><p>A data frame with covariate data for use in
'covariate_models', with dataset names as row names.</p></dd>
<dt>covariate_models</dt>
<dd><p>A list containing linear model formulas with one explanatory
variable, i.e. of the type 'parameter ~ covariate'. Covariates must be available
in the 'covariates' data frame.</p></dd>
<dt>no_random_effect</dt>
<dd><p>Character vector of degradation parameters for
which there should be no variability over the groups. Only used
if the covariance model is not explicitly specified.</p></dd>
<dt>error.init</dt>
<dd><p>Will be passed to <code><a href="https://rdrr.io/pkg/saemix/man/saemixModel.html" class="external-link">saemix::saemixModel()</a></code>.</p></dd>
<dt>nbiter.saemix</dt>
<dd><p>Convenience option to increase the number of
iterations</p></dd>
<dt>control</dt>
<dd><p>Passed to <a href="https://rdrr.io/pkg/saemix/man/saemix.html" class="external-link">saemix::saemix</a>.</p></dd>
<dt>verbose</dt>
<dd><p>Should we print information about created objects of
type <a href="https://rdrr.io/pkg/saemix/man/SaemixModel-class.html" class="external-link">saemix::SaemixModel</a> and <a href="https://rdrr.io/pkg/saemix/man/SaemixData-class.html" class="external-link">saemix::SaemixData</a>?</p></dd>
<dt>quiet</dt>
<dd><p>Should we suppress the messages saemix prints at the beginning
and the end of the optimisation process?</p></dd>
<dt>x</dt>
<dd><p>An saem.mmkin object to print</p></dd>
<dt>digits</dt>
<dd><p>Number of digits to use for printing</p></dd>
</dl></div>
<div class="section level2">
<h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
<p>An S3 object of class 'saem.mmkin', containing the fitted
<a href="https://rdrr.io/pkg/saemix/man/SaemixObject-class.html" class="external-link">saemix::SaemixObject</a> as a list component named 'so'. The
object also inherits from 'mixed.mmkin'.</p>
<p>An <a href="https://rdrr.io/pkg/saemix/man/SaemixModel-class.html" class="external-link">saemix::SaemixModel</a> object.</p>
<p>An <a href="https://rdrr.io/pkg/saemix/man/SaemixData-class.html" class="external-link">saemix::SaemixData</a> object.</p>
</div>
<div class="section level2">
<h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
<p>An mmkin row object is essentially a list of mkinfit objects that have been
obtained by fitting the same model to a list of datasets using <a href="mkinfit.html">mkinfit</a>.</p>
<p>Starting values for the fixed effects (population mean parameters, argument
psi0 of <code><a href="https://rdrr.io/pkg/saemix/man/saemixModel.html" class="external-link">saemix::saemixModel()</a></code> are the mean values of the parameters found
using <a href="mmkin.html">mmkin</a>.</p>
</div>
<div class="section level2">
<h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
<div class="dont-index"><p><a href="summary.saem.mmkin.html">summary.saem.mmkin</a> <a href="plot.mixed.mmkin.html">plot.mixed.mmkin</a></p></div>
</div>
<div class="section level2">
<h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
<div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
<span class="r-in"><span><span class="va">ds</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">experimental_data_for_UBA_2019</span><span class="op">[</span><span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">]</span>,</span></span>
<span class="r-in"><span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">x</span><span class="op">$</span><span class="va">data</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"name"</span>, <span class="st">"time"</span>, <span class="st">"value"</span><span class="op">)</span><span class="op">]</span><span class="op">)</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">ds</span><span class="op">)</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="st">"Dataset"</span>, <span class="fl">6</span><span class="op">:</span><span class="fl">10</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="va">f_mmkin_parent_p0_fixed</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="va">ds</span>,</span></span>
<span class="r-in"><span> state.ini <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fl">100</span><span class="op">)</span>, fixed_initials <span class="op">=</span> <span class="st">"parent"</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="va">f_saem_p0_fixed</span> <span class="op"><-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin_parent_p0_fixed</span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="va">f_mmkin_parent</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"FOMC"</span>, <span class="st">"DFOP"</span><span class="op">)</span>, <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="va">f_saem_sfo</span> <span class="op"><-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"SFO"</span>, <span class="op">]</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="va">f_saem_fomc</span> <span class="op"><-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="va">f_saem_dfop</span> <span class="op"><-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin_parent</span><span class="op">[</span><span class="st">"DFOP"</span>, <span class="op">]</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_sfo</span>, <span class="va">f_saem_fomc</span>, <span class="va">f_saem_dfop</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#></span> Data: 90 observations of 1 variable(s) grouped in 5 datasets</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> npar AIC BIC Lik</span>
<span class="r-out co"><span class="r-pr">#></span> f_saem_sfo 5 624.33 622.38 -307.17</span>
<span class="r-out co"><span class="r-pr">#></span> f_saem_fomc 7 467.85 465.11 -226.92</span>
<span class="r-out co"><span class="r-pr">#></span> f_saem_dfop 9 493.76 490.24 -237.88</span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_sfo</span>, <span class="va">f_saem_dfop</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#></span> Data: 90 observations of 1 variable(s) grouped in 5 datasets</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> npar AIC BIC Lik Chisq Df Pr(>Chisq) </span>
<span class="r-out co"><span class="r-pr">#></span> f_saem_sfo 5 624.33 622.38 -307.17 </span>
<span class="r-out co"><span class="r-pr">#></span> f_saem_dfop 9 493.76 490.24 -237.88 138.57 4 < 2.2e-16 ***</span>
<span class="r-out co"><span class="r-pr">#></span> ---</span>
<span class="r-out co"><span class="r-pr">#></span> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span>
<span class="r-in"><span><span class="fu"><a href="illparms.html">illparms</a></span><span class="op">(</span><span class="va">f_saem_dfop</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#></span> [1] "sd(g_qlogis)"</span>
<span class="r-in"><span><span class="va">f_saem_dfop_red</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_saem_dfop</span>, no_random_effect <span class="op">=</span> <span class="st">"g_qlogis"</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_dfop</span>, <span class="va">f_saem_dfop_red</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#></span> Data: 90 observations of 1 variable(s) grouped in 5 datasets</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> npar AIC BIC Lik Chisq Df Pr(>Chisq)</span>
<span class="r-out co"><span class="r-pr">#></span> f_saem_dfop_red 8 488.68 485.55 -236.34 </span>
<span class="r-out co"><span class="r-pr">#></span> f_saem_dfop 9 493.76 490.24 -237.88 0 1 1</span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_sfo</span>, <span class="va">f_saem_fomc</span>, <span class="va">f_saem_dfop</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#></span> Data: 90 observations of 1 variable(s) grouped in 5 datasets</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> npar AIC BIC Lik</span>
<span class="r-out co"><span class="r-pr">#></span> f_saem_sfo 5 624.33 622.38 -307.17</span>
<span class="r-out co"><span class="r-pr">#></span> f_saem_fomc 7 467.85 465.11 -226.92</span>
<span class="r-out co"><span class="r-pr">#></span> f_saem_dfop 9 493.76 490.24 -237.88</span>
<span class="r-in"><span><span class="co"># The returned saem.mmkin object contains an SaemixObject, therefore we can use</span></span></span>
<span class="r-in"><span><span class="co"># functions from saemix</span></span></span>
<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">saemix</span><span class="op">)</span></span></span>
<span class="r-msg co"><span class="r-pr">#></span> Loading required package: npde</span>
<span class="r-msg co"><span class="r-pr">#></span> Package saemix, version 3.2</span>
<span class="r-msg co"><span class="r-pr">#></span> please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr</span>
<span class="r-msg co"><span class="r-pr">#></span> </span>
<span class="r-msg co"><span class="r-pr">#></span> Attaching package: ‘saemix’</span>
<span class="r-msg co"><span class="r-pr">#></span> The following objects are masked from ‘package:npde’:</span>
<span class="r-msg co"><span class="r-pr">#></span> </span>
<span class="r-msg co"><span class="r-pr">#></span> kurtosis, skewness</span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/compare.saemix.html" class="external-link">compare.saemix</a></span><span class="op">(</span><span class="va">f_saem_sfo</span><span class="op">$</span><span class="va">so</span>, <span class="va">f_saem_fomc</span><span class="op">$</span><span class="va">so</span>, <span class="va">f_saem_dfop</span><span class="op">$</span><span class="va">so</span><span class="op">)</span></span></span>
<span class="r-msg co"><span class="r-pr">#></span> Likelihoods calculated by importance sampling</span>
<span class="r-out co"><span class="r-pr">#></span> AIC BIC</span>
<span class="r-out co"><span class="r-pr">#></span> 1 624.3316 622.3788</span>
<span class="r-out co"><span class="r-pr">#></span> 2 467.8472 465.1132</span>
<span class="r-out co"><span class="r-pr">#></span> 3 493.7592 490.2441</span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_fomc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"convergence"</span><span class="op">)</span></span></span>
<span class="r-plt img"><img src="saem-1.png" alt="" width="700" height="433"></span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_fomc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"individual.fit"</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#></span> Simulating data using nsim = 1000 simulated datasets</span>
<span class="r-out co"><span class="r-pr">#></span> Computing WRES and npde .</span>
<span class="r-plt img"><img src="saem-2.png" alt="" width="700" height="433"></span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_fomc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"npde"</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#></span> Simulating data using nsim = 1000 simulated datasets</span>
<span class="r-out co"><span class="r-pr">#></span> Computing WRES and npde .</span>
<span class="r-msg co"><span class="r-pr">#></span> Please use npdeSaemix to obtain VPC and npde</span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_fomc</span><span class="op">$</span><span class="va">so</span>, plot.type <span class="op">=</span> <span class="st">"vpc"</span><span class="op">)</span></span></span>
<span class="r-plt img"><img src="saem-3.png" alt="" width="700" height="433"></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="va">f_mmkin_parent_tc</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/update.html" class="external-link">update</a></span><span class="op">(</span><span class="va">f_mmkin_parent</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="va">f_saem_fomc_tc</span> <span class="op"><-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin_parent_tc</span><span class="op">[</span><span class="st">"FOMC"</span>, <span class="op">]</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/stats/anova.html" class="external-link">anova</a></span><span class="op">(</span><span class="va">f_saem_fomc</span>, <span class="va">f_saem_fomc_tc</span>, test <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#></span> Data: 90 observations of 1 variable(s) grouped in 5 datasets</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> npar AIC BIC Lik Chisq Df Pr(>Chisq)</span>
<span class="r-out co"><span class="r-pr">#></span> f_saem_fomc 7 467.85 465.11 -226.92 </span>
<span class="r-out co"><span class="r-pr">#></span> f_saem_fomc_tc 8 469.90 466.77 -226.95 0 1 1</span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="va">sfo_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span></span>
<span class="r-in"><span> A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span>
<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span>
<span class="r-in"><span><span class="va">fomc_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"FOMC"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span></span>
<span class="r-in"><span> A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span>
<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span>
<span class="r-in"><span><span class="va">dfop_sfo</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="st">"A1"</span><span class="op">)</span>,</span></span>
<span class="r-in"><span> A1 <span class="op">=</span> <span class="fu"><a href="mkinmod.html">mkinsub</a></span><span class="op">(</span><span class="st">"SFO"</span><span class="op">)</span><span class="op">)</span></span></span>
<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span>
<span class="r-in"><span><span class="co"># The following fit uses analytical solutions for SFO-SFO and DFOP-SFO,</span></span></span>
<span class="r-in"><span><span class="co"># and compiled ODEs for FOMC that are much slower</span></span></span>
<span class="r-in"><span><span class="va">f_mmkin</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span></span>
<span class="r-in"><span> <span class="st">"SFO-SFO"</span> <span class="op">=</span> <span class="va">sfo_sfo</span>, <span class="st">"FOMC-SFO"</span> <span class="op">=</span> <span class="va">fomc_sfo</span>, <span class="st">"DFOP-SFO"</span> <span class="op">=</span> <span class="va">dfop_sfo</span><span class="op">)</span>,</span></span>
<span class="r-in"><span> <span class="va">ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="co"># saem fits of SFO-SFO and DFOP-SFO to these data take about five seconds</span></span></span>
<span class="r-in"><span><span class="co"># each on this system, as we use analytical solutions written for saemix.</span></span></span>
<span class="r-in"><span><span class="co"># When using the analytical solutions written for mkin this took around</span></span></span>
<span class="r-in"><span><span class="co"># four minutes</span></span></span>
<span class="r-in"><span><span class="va">f_saem_sfo_sfo</span> <span class="op"><-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">[</span><span class="st">"SFO-SFO"</span>, <span class="op">]</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="va">f_saem_dfop_sfo</span> <span class="op"><-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="co"># We can use print, plot and summary methods to check the results</span></span></span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#></span> Kinetic nonlinear mixed-effects model fit by SAEM</span>
<span class="r-out co"><span class="r-pr">#></span> Structural model:</span>
<span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span>
<span class="r-out co"><span class="r-pr">#></span> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span>
<span class="r-out co"><span class="r-pr">#></span> * parent</span>
<span class="r-out co"><span class="r-pr">#></span> d_A1/dt = + f_parent_to_A1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span>
<span class="r-out co"><span class="r-pr">#></span> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span>
<span class="r-out co"><span class="r-pr">#></span> exp(-k2 * time))) * parent - k_A1 * A1</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Data:</span>
<span class="r-out co"><span class="r-pr">#></span> 170 observations of 2 variable(s) grouped in 5 datasets</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Likelihood computed by importance sampling</span>
<span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span>
<span class="r-out co"><span class="r-pr">#></span> 839.2 834.1 -406.6</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Fitted parameters:</span>
<span class="r-out co"><span class="r-pr">#></span> estimate lower upper</span>
<span class="r-out co"><span class="r-pr">#></span> parent_0 93.70402 91.04104 96.3670</span>
<span class="r-out co"><span class="r-pr">#></span> log_k_A1 -5.83760 -7.66452 -4.0107</span>
<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.95718 -1.35955 -0.5548</span>
<span class="r-out co"><span class="r-pr">#></span> log_k1 -2.35514 -3.39402 -1.3163</span>
<span class="r-out co"><span class="r-pr">#></span> log_k2 -3.79634 -5.64009 -1.9526</span>
<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.02108 -0.66463 0.6225</span>
<span class="r-out co"><span class="r-pr">#></span> a.1 1.88191 1.66491 2.0989</span>
<span class="r-out co"><span class="r-pr">#></span> SD.parent_0 2.81628 0.78922 4.8433</span>
<span class="r-out co"><span class="r-pr">#></span> SD.log_k_A1 1.78751 0.42105 3.1540</span>
<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.45016 0.16116 0.7391</span>
<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 1.06923 0.31676 1.8217</span>
<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 2.03768 0.70938 3.3660</span>
<span class="r-out co"><span class="r-pr">#></span> SD.g_qlogis 0.44024 -0.09262 0.9731</span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/plot.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span><span class="op">)</span></span></span>
<span class="r-plt img"><img src="saem-4.png" alt="" width="700" height="433"></span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/pkg/saemix/man/summary-methods.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_saem_dfop_sfo</span>, data <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#></span> saemix version used for fitting: 3.2 </span>
<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.2.6 </span>
<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.3.2 </span>
<span class="r-out co"><span class="r-pr">#></span> Date of fit: Thu Nov 16 04:17:34 2023 </span>
<span class="r-out co"><span class="r-pr">#></span> Date of summary: Thu Nov 16 04:17:34 2023 </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Equations:</span>
<span class="r-out co"><span class="r-pr">#></span> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span>
<span class="r-out co"><span class="r-pr">#></span> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span>
<span class="r-out co"><span class="r-pr">#></span> * parent</span>
<span class="r-out co"><span class="r-pr">#></span> d_A1/dt = + f_parent_to_A1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span>
<span class="r-out co"><span class="r-pr">#></span> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span>
<span class="r-out co"><span class="r-pr">#></span> exp(-k2 * time))) * parent - k_A1 * A1</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Data:</span>
<span class="r-out co"><span class="r-pr">#></span> 170 observations of 2 variable(s) grouped in 5 datasets</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Model predictions using solution type analytical </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Fitted in 3.385 s</span>
<span class="r-out co"><span class="r-pr">#></span> Using 300, 100 iterations and 10 chains</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Variance model: Constant variance </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Starting values for degradation parameters:</span>
<span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_A1 f_parent_qlogis log_k1 log_k2 </span>
<span class="r-out co"><span class="r-pr">#></span> 93.8102 -5.3734 -0.9711 -1.8799 -4.2708 </span>
<span class="r-out co"><span class="r-pr">#></span> g_qlogis </span>
<span class="r-out co"><span class="r-pr">#></span> 0.1356 </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Fixed degradation parameter values:</span>
<span class="r-out co"><span class="r-pr">#></span> None</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Starting values for random effects (square root of initial entries in omega):</span>
<span class="r-out co"><span class="r-pr">#></span> parent_0 log_k_A1 f_parent_qlogis log_k1 log_k2 g_qlogis</span>
<span class="r-out co"><span class="r-pr">#></span> parent_0 4.941 0.000 0.0000 0.000 0.000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> log_k_A1 0.000 2.551 0.0000 0.000 0.000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis 0.000 0.000 0.7251 0.000 0.000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> log_k1 0.000 0.000 0.0000 1.449 0.000 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> log_k2 0.000 0.000 0.0000 0.000 2.228 0.0000</span>
<span class="r-out co"><span class="r-pr">#></span> g_qlogis 0.000 0.000 0.0000 0.000 0.000 0.7814</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Starting values for error model parameters:</span>
<span class="r-out co"><span class="r-pr">#></span> a.1 </span>
<span class="r-out co"><span class="r-pr">#></span> 1 </span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Results:</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Likelihood computed by importance sampling</span>
<span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span>
<span class="r-out co"><span class="r-pr">#></span> 839.2 834.1 -406.6</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Optimised parameters:</span>
<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span>
<span class="r-out co"><span class="r-pr">#></span> parent_0 93.70402 91.04104 96.3670</span>
<span class="r-out co"><span class="r-pr">#></span> log_k_A1 -5.83760 -7.66452 -4.0107</span>
<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.95718 -1.35955 -0.5548</span>
<span class="r-out co"><span class="r-pr">#></span> log_k1 -2.35514 -3.39402 -1.3163</span>
<span class="r-out co"><span class="r-pr">#></span> log_k2 -3.79634 -5.64009 -1.9526</span>
<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.02108 -0.66463 0.6225</span>
<span class="r-out co"><span class="r-pr">#></span> a.1 1.88191 1.66491 2.0989</span>
<span class="r-out co"><span class="r-pr">#></span> SD.parent_0 2.81628 0.78922 4.8433</span>
<span class="r-out co"><span class="r-pr">#></span> SD.log_k_A1 1.78751 0.42105 3.1540</span>
<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.45016 0.16116 0.7391</span>
<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 1.06923 0.31676 1.8217</span>
<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 2.03768 0.70938 3.3660</span>
<span class="r-out co"><span class="r-pr">#></span> SD.g_qlogis 0.44024 -0.09262 0.9731</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Correlation: </span>
<span class="r-out co"><span class="r-pr">#></span> parnt_0 lg_k_A1 f_prnt_ log_k1 log_k2 </span>
<span class="r-out co"><span class="r-pr">#></span> log_k_A1 -0.0147 </span>
<span class="r-out co"><span class="r-pr">#></span> f_parent_qlogis -0.0269 0.0573 </span>
<span class="r-out co"><span class="r-pr">#></span> log_k1 0.0263 -0.0011 -0.0040 </span>
<span class="r-out co"><span class="r-pr">#></span> log_k2 0.0020 0.0065 -0.0002 -0.0776 </span>
<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.0248 -0.0180 -0.0004 -0.0903 -0.0603</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Random effects:</span>
<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span>
<span class="r-out co"><span class="r-pr">#></span> SD.parent_0 2.8163 0.78922 4.8433</span>
<span class="r-out co"><span class="r-pr">#></span> SD.log_k_A1 1.7875 0.42105 3.1540</span>
<span class="r-out co"><span class="r-pr">#></span> SD.f_parent_qlogis 0.4502 0.16116 0.7391</span>
<span class="r-out co"><span class="r-pr">#></span> SD.log_k1 1.0692 0.31676 1.8217</span>
<span class="r-out co"><span class="r-pr">#></span> SD.log_k2 2.0377 0.70938 3.3660</span>
<span class="r-out co"><span class="r-pr">#></span> SD.g_qlogis 0.4402 -0.09262 0.9731</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Variance model:</span>
<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span>
<span class="r-out co"><span class="r-pr">#></span> a.1 1.882 1.665 2.099</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Backtransformed parameters:</span>
<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span>
<span class="r-out co"><span class="r-pr">#></span> parent_0 93.704015 9.104e+01 96.36699</span>
<span class="r-out co"><span class="r-pr">#></span> k_A1 0.002916 4.692e-04 0.01812</span>
<span class="r-out co"><span class="r-pr">#></span> f_parent_to_A1 0.277443 2.043e-01 0.36475</span>
<span class="r-out co"><span class="r-pr">#></span> k1 0.094880 3.357e-02 0.26813</span>
<span class="r-out co"><span class="r-pr">#></span> k2 0.022453 3.553e-03 0.14191</span>
<span class="r-out co"><span class="r-pr">#></span> g 0.494731 3.397e-01 0.65078</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Resulting formation fractions:</span>
<span class="r-out co"><span class="r-pr">#></span> ff</span>
<span class="r-out co"><span class="r-pr">#></span> parent_A1 0.2774</span>
<span class="r-out co"><span class="r-pr">#></span> parent_sink 0.7226</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Estimated disappearance times:</span>
<span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50back DT50_k1 DT50_k2</span>
<span class="r-out co"><span class="r-pr">#></span> parent 14.0 72.38 21.79 7.306 30.87</span>
<span class="r-out co"><span class="r-pr">#></span> A1 237.7 789.68 NA NA NA</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Data:</span>
<span class="r-out co"><span class="r-pr">#></span> ds name time observed predicted residual std standardized</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 0 97.2 95.70025 1.49975 1.882 0.79693</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 0 96.4 95.70025 0.69975 1.882 0.37183</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 3 71.1 71.44670 -0.34670 1.882 -0.18423</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 3 69.2 71.44670 -2.24670 1.882 -1.19384</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 6 58.1 56.59283 1.50717 1.882 0.80087</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 6 56.6 56.59283 0.00717 1.882 0.00381</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 10 44.4 44.56648 -0.16648 1.882 -0.08847</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 10 43.4 44.56648 -1.16648 1.882 -0.61984</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 20 33.3 29.76020 3.53980 1.882 1.88096</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 20 29.2 29.76020 -0.56020 1.882 -0.29767</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 34 17.6 19.39208 -1.79208 1.882 -0.95226</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 34 18.0 19.39208 -1.39208 1.882 -0.73971</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 55 10.5 10.55761 -0.05761 1.882 -0.03061</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 55 9.3 10.55761 -1.25761 1.882 -0.66826</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 90 4.5 3.84742 0.65258 1.882 0.34676</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 90 4.7 3.84742 0.85258 1.882 0.45304</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 112 3.0 2.03997 0.96003 1.882 0.51013</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 112 3.4 2.03997 1.36003 1.882 0.72268</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 132 2.3 1.14585 1.15415 1.882 0.61328</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 parent 132 2.7 1.14585 1.55415 1.882 0.82583</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 3 4.3 4.86054 -0.56054 1.882 -0.29786</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 3 4.6 4.86054 -0.26054 1.882 -0.13844</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 6 7.0 7.74179 -0.74179 1.882 -0.39417</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 6 7.2 7.74179 -0.54179 1.882 -0.28789</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 10 8.2 9.94048 -1.74048 1.882 -0.92485</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 10 8.0 9.94048 -1.94048 1.882 -1.03112</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 20 11.0 12.19109 -1.19109 1.882 -0.63291</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 20 13.7 12.19109 1.50891 1.882 0.80180</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 34 11.5 13.10706 -1.60706 1.882 -0.85395</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 34 12.7 13.10706 -0.40706 1.882 -0.21630</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 55 14.9 13.06131 1.83869 1.882 0.97703</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 55 14.5 13.06131 1.43869 1.882 0.76448</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 90 12.1 11.54495 0.55505 1.882 0.29494</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 90 12.3 11.54495 0.75505 1.882 0.40122</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 112 9.9 10.31533 -0.41533 1.882 -0.22070</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 112 10.2 10.31533 -0.11533 1.882 -0.06128</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 132 8.8 9.20222 -0.40222 1.882 -0.21373</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 6 A1 132 7.8 9.20222 -1.40222 1.882 -0.74510</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 0 93.6 90.82357 2.77643 1.882 1.47532</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 0 92.3 90.82357 1.47643 1.882 0.78453</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 3 87.0 84.73448 2.26552 1.882 1.20384</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 3 82.2 84.73448 -2.53448 1.882 -1.34675</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 7 74.0 77.65013 -3.65013 1.882 -1.93958</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 7 73.9 77.65013 -3.75013 1.882 -1.99272</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 14 64.2 67.60639 -3.40639 1.882 -1.81007</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 14 69.5 67.60639 1.89361 1.882 1.00621</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 30 54.0 52.53663 1.46337 1.882 0.77760</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 30 54.6 52.53663 2.06337 1.882 1.09642</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 60 41.1 39.42728 1.67272 1.882 0.88884</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 60 38.4 39.42728 -1.02728 1.882 -0.54587</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 90 32.5 33.76360 -1.26360 1.882 -0.67144</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 90 35.5 33.76360 1.73640 1.882 0.92268</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 120 28.1 30.39975 -2.29975 1.882 -1.22203</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 120 29.0 30.39975 -1.39975 1.882 -0.74379</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 180 26.5 25.62379 0.87621 1.882 0.46559</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 parent 180 27.6 25.62379 1.97621 1.882 1.05010</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 3 3.9 2.70005 1.19995 1.882 0.63762</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 3 3.1 2.70005 0.39995 1.882 0.21252</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 7 6.9 5.83475 1.06525 1.882 0.56605</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 7 6.6 5.83475 0.76525 1.882 0.40663</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 14 10.4 10.26142 0.13858 1.882 0.07364</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 14 8.3 10.26142 -1.96142 1.882 -1.04225</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 30 14.4 16.82999 -2.42999 1.882 -1.29123</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 30 13.7 16.82999 -3.12999 1.882 -1.66319</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 60 22.1 22.32486 -0.22486 1.882 -0.11949</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 60 22.3 22.32486 -0.02486 1.882 -0.01321</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 90 27.5 24.45927 3.04073 1.882 1.61576</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 90 25.4 24.45927 0.94073 1.882 0.49988</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 120 28.0 25.54862 2.45138 1.882 1.30260</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 120 26.6 25.54862 1.05138 1.882 0.55868</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 180 25.8 26.82277 -1.02277 1.882 -0.54347</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 7 A1 180 25.3 26.82277 -1.52277 1.882 -0.80916</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 0 91.9 91.16791 0.73209 1.882 0.38901</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 0 90.8 91.16791 -0.36791 1.882 -0.19550</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 1 64.9 67.58358 -2.68358 1.882 -1.42598</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 1 66.2 67.58358 -1.38358 1.882 -0.73520</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 3 43.5 41.62086 1.87914 1.882 0.99853</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 3 44.1 41.62086 2.47914 1.882 1.31735</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 8 18.3 19.60116 -1.30116 1.882 -0.69140</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 8 18.1 19.60116 -1.50116 1.882 -0.79768</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 14 10.2 10.63101 -0.43101 1.882 -0.22903</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 14 10.8 10.63101 0.16899 1.882 0.08980</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 27 4.9 3.12435 1.77565 1.882 0.94354</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 27 3.3 3.12435 0.17565 1.882 0.09334</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 48 1.6 0.43578 1.16422 1.882 0.61864</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 48 1.5 0.43578 1.06422 1.882 0.56550</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 70 1.1 0.05534 1.04466 1.882 0.55510</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 parent 70 0.9 0.05534 0.84466 1.882 0.44883</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 1 9.6 7.63450 1.96550 1.882 1.04442</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 1 7.7 7.63450 0.06550 1.882 0.03481</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 3 15.0 15.52593 -0.52593 1.882 -0.27947</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 3 15.1 15.52593 -0.42593 1.882 -0.22633</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 8 21.2 20.32192 0.87808 1.882 0.46659</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 8 21.1 20.32192 0.77808 1.882 0.41345</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 14 19.7 20.09721 -0.39721 1.882 -0.21107</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 14 18.9 20.09721 -1.19721 1.882 -0.63617</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 27 17.5 16.37477 1.12523 1.882 0.59792</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 27 15.9 16.37477 -0.47477 1.882 -0.25228</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 48 9.5 10.13141 -0.63141 1.882 -0.33551</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 48 9.8 10.13141 -0.33141 1.882 -0.17610</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 70 6.2 5.81827 0.38173 1.882 0.20284</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 8 A1 70 6.1 5.81827 0.28173 1.882 0.14970</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 0 99.8 97.48728 2.31272 1.882 1.22892</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 0 98.3 97.48728 0.81272 1.882 0.43186</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 1 77.1 79.29476 -2.19476 1.882 -1.16624</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 1 77.2 79.29476 -2.09476 1.882 -1.11310</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 3 59.0 55.67060 3.32940 1.882 1.76915</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 3 58.1 55.67060 2.42940 1.882 1.29092</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 8 27.4 31.57871 -4.17871 1.882 -2.22046</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 8 29.2 31.57871 -2.37871 1.882 -1.26398</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 14 19.1 22.51546 -3.41546 1.882 -1.81489</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 14 29.6 22.51546 7.08454 1.882 3.76454</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 27 10.1 14.09074 -3.99074 1.882 -2.12057</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 27 18.2 14.09074 4.10926 1.882 2.18355</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 48 4.5 6.95747 -2.45747 1.882 -1.30584</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 48 9.1 6.95747 2.14253 1.882 1.13848</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 70 2.3 3.32472 -1.02472 1.882 -0.54451</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 70 2.9 3.32472 -0.42472 1.882 -0.22569</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 91 2.0 1.64300 0.35700 1.882 0.18970</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 91 1.8 1.64300 0.15700 1.882 0.08343</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 120 2.0 0.62073 1.37927 1.882 0.73291</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 parent 120 2.2 0.62073 1.57927 1.882 0.83918</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 1 4.2 3.64568 0.55432 1.882 0.29455</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 1 3.9 3.64568 0.25432 1.882 0.13514</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 3 7.4 8.30173 -0.90173 1.882 -0.47916</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 3 7.9 8.30173 -0.40173 1.882 -0.21347</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 8 14.5 12.71589 1.78411 1.882 0.94803</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 8 13.7 12.71589 0.98411 1.882 0.52293</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 14 14.2 13.90452 0.29548 1.882 0.15701</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 14 12.2 13.90452 -1.70452 1.882 -0.90574</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 27 13.7 14.15523 -0.45523 1.882 -0.24190</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 27 13.2 14.15523 -0.95523 1.882 -0.50759</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 48 13.6 13.31038 0.28962 1.882 0.15389</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 48 15.4 13.31038 2.08962 1.882 1.11037</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 70 10.4 11.85965 -1.45965 1.882 -0.77562</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 70 11.6 11.85965 -0.25965 1.882 -0.13797</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 91 10.0 10.36294 -0.36294 1.882 -0.19286</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 91 9.5 10.36294 -0.86294 1.882 -0.45855</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 120 9.1 8.43003 0.66997 1.882 0.35601</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 9 A1 120 9.0 8.43003 0.56997 1.882 0.30287</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 0 96.1 93.95603 2.14397 1.882 1.13925</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 0 94.3 93.95603 0.34397 1.882 0.18278</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 8 73.9 77.70592 -3.80592 1.882 -2.02237</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 8 73.9 77.70592 -3.80592 1.882 -2.02237</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 14 69.4 70.04570 -0.64570 1.882 -0.34311</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 14 73.1 70.04570 3.05430 1.882 1.62298</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 21 65.6 64.01710 1.58290 1.882 0.84111</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 21 65.3 64.01710 1.28290 1.882 0.68170</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 41 55.9 54.98434 0.91566 1.882 0.48656</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 41 54.4 54.98434 -0.58434 1.882 -0.31050</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 63 47.0 49.87137 -2.87137 1.882 -1.52577</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 63 49.3 49.87137 -0.57137 1.882 -0.30361</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 91 44.7 45.06727 -0.36727 1.882 -0.19516</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 91 46.7 45.06727 1.63273 1.882 0.86759</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 120 42.1 40.76402 1.33598 1.882 0.70991</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 parent 120 41.3 40.76402 0.53598 1.882 0.28481</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 8 3.3 4.14599 -0.84599 1.882 -0.44954</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 8 3.4 4.14599 -0.74599 1.882 -0.39640</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 14 3.9 6.08478 -2.18478 1.882 -1.16093</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 14 2.9 6.08478 -3.18478 1.882 -1.69231</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 21 6.4 7.59411 -1.19411 1.882 -0.63452</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 21 7.2 7.59411 -0.39411 1.882 -0.20942</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 41 9.1 9.78292 -0.68292 1.882 -0.36289</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 41 8.5 9.78292 -1.28292 1.882 -0.68171</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 63 11.7 10.93274 0.76726 1.882 0.40770</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 63 12.0 10.93274 1.06726 1.882 0.56711</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 91 13.3 11.93986 1.36014 1.882 0.72274</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 91 13.2 11.93986 1.26014 1.882 0.66961</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 120 14.3 12.79238 1.50762 1.882 0.80111</span>
<span class="r-out co"><span class="r-pr">#></span> Dataset 10 A1 120 12.1 12.79238 -0.69238 1.882 -0.36791</span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># The following takes about 6 minutes</span></span></span>
<span class="r-in"><span><span class="va">f_saem_dfop_sfo_deSolve</span> <span class="op"><-</span> <span class="fu">saem</span><span class="op">(</span><span class="va">f_mmkin</span><span class="op">[</span><span class="st">"DFOP-SFO"</span>, <span class="op">]</span>, solution_type <span class="op">=</span> <span class="st">"deSolve"</span>,</span></span>
<span class="r-in"><span> nbiter.saemix <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">200</span>, <span class="fl">80</span><span class="op">)</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#></span> DINTDY- T (=R1) illegal </span>
<span class="r-out co"><span class="r-pr">#></span> In above message, R1 = 70</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> T not in interval TCUR - HU (= R1) to TCUR (=R2) </span>
<span class="r-out co"><span class="r-pr">#></span> In above message, R1 = 53.1042, R2 = 56.6326</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> DINTDY- T (=R1) illegal </span>
<span class="r-out co"><span class="r-pr">#></span> In above message, R1 = 91</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> T not in interval TCUR - HU (= R1) to TCUR (=R2) </span>
<span class="r-out co"><span class="r-pr">#></span> In above message, R1 = 53.1042, R2 = 56.6326</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> DLSODA- Trouble in DINTDY. ITASK = I1, TOUT = R1</span>
<span class="r-out co"><span class="r-pr">#></span> In above message, I1 = 1</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> In above message, R1 = 91</span>
<span class="r-out co"><span class="r-pr">#></span> </span>
<span class="r-out co"><span class="r-pr">#></span> Error in deSolve::lsoda(y = odeini, times = outtimes, func = lsoda_func, : </span>
<span class="r-out co"><span class="r-pr">#></span> illegal input detected before taking any integration steps - see written message</span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co">#anova(</span></span></span>
<span class="r-in"><span><span class="co"># f_saem_dfop_sfo,</span></span></span>
<span class="r-in"><span><span class="co"># f_saem_dfop_sfo_deSolve))</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># If the model supports it, we can also use eigenvalue based solutions, which</span></span></span>
<span class="r-in"><span><span class="co"># take a similar amount of time</span></span></span>
<span class="r-in"><span><span class="co">#f_saem_sfo_sfo_eigen <- saem(f_mmkin["SFO-SFO", ], solution_type = "eigen",</span></span></span>
<span class="r-in"><span><span class="co"># control = list(nbiter.saemix = c(200, 80), nbdisplay = 10))</span></span></span>
<span class="r-in"><span><span class="co"># }</span></span></span>
</code></pre></div>
</div>
</main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
</nav></aside></div>
<footer><div class="pkgdown-footer-left">
<p></p><p>Developed by Johannes Ranke.</p>
</div>
<div class="pkgdown-footer-right">
<p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
</div>
</footer></div>
</body></html>
|
