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
|
<!-- Generated by pkgdown: do not edit by hand -->
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014 • mkin</title>
<!-- jquery -->
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.3.1/jquery.min.js" integrity="sha256-FgpCb/KJQlLNfOu91ta32o/NMZxltwRo8QtmkMRdAu8=" crossorigin="anonymous"></script>
<!-- Bootstrap -->
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.3.7/css/bootstrap.min.css" integrity="sha256-916EbMg70RQy9LHiGkXzG8hSg9EdNy97GazNG/aiY1w=" crossorigin="anonymous" />
<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.3.7/js/bootstrap.min.js" integrity="sha256-U5ZEeKfGNOja007MMD3YBI0A3OSZOQbeG6z2f2Y0hu8=" crossorigin="anonymous"></script>
<!-- Font Awesome icons -->
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.7.1/css/all.min.css" integrity="sha256-nAmazAk6vS34Xqo0BSrTb+abbtFlgsFK7NKSi6o7Y78=" crossorigin="anonymous" />
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.7.1/css/v4-shims.min.css" integrity="sha256-6qHlizsOWFskGlwVOKuns+D1nB6ssZrHQrNj1wGplHc=" crossorigin="anonymous" />
<!-- clipboard.js -->
<script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.4/clipboard.min.js" integrity="sha256-FiZwavyI2V6+EXO1U+xzLG3IKldpiTFf3153ea9zikQ=" crossorigin="anonymous"></script>
<!-- headroom.js -->
<script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.9.4/headroom.min.js" integrity="sha256-DJFC1kqIhelURkuza0AvYal5RxMtpzLjFhsnVIeuk+U=" crossorigin="anonymous"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.9.4/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script>
<!-- pkgdown -->
<link href="../pkgdown.css" rel="stylesheet">
<script src="../pkgdown.js"></script>
<meta property="og:title" content="Synthetic datasets for one parent compound with two metabolites — synthetic_data_for_UBA_2014" />
<meta property="og:description" content="The 12 datasets were generated using four different models and three different
variance components. The four models are either the SFO or the DFOP model with either
two sequential or two parallel metabolites.
Variance component 'a' is based on a normal distribution with standard deviation of 3,
Variance component 'b' is also based on a normal distribution, but with a standard deviation of 7.
Variance component 'c' is based on the error model from Rocke and Lorenzato (1995), with the
minimum standard deviation (for small y values) of 0.5, and a proportionality constant of 0.07
for the increase of the standard deviation with y. Note that this is a simplified version
of the error model proposed by Rocke and Lorenzato (1995), as in their model the error of the
measured values approximates lognormal distribution for high values, whereas we are using
normally distributed error components all along.
Initial concentrations for metabolites and all values where adding the variance component resulted
in a value below the assumed limit of detection of 0.1 were set to NA.
As an example, the first dataset has the title SFO_lin_a and is based on the SFO model
with two sequential metabolites (linear pathway), with added variance component 'a'.
Compare also the code in the example section to see the degradation models." />
<meta name="twitter:card" content="summary" />
<!-- 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>
<div class="container template-reference-topic">
<header>
<div class="navbar navbar-default navbar-fixed-top" role="navigation">
<div class="container">
<div class="navbar-header">
<button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
<span class="sr-only">Toggle navigation</span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">mkin</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.9.49.6</span>
</span>
</div>
<div id="navbar" class="navbar-collapse collapse">
<ul class="nav navbar-nav">
<li>
<a href="../reference/index.html">Functions and data</a>
</li>
<li class="dropdown">
<a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
Articles
<span class="caret"></span>
</a>
<ul class="dropdown-menu" role="menu">
<li>
<a href="../articles/mkin.html">Introduction to mkin</a>
</li>
<li>
<a href="../articles/FOCUS_D.html">Example evaluation of FOCUS Example Dataset D</a>
</li>
<li>
<a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a>
</li>
<li>
<a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a>
</li>
<li>
<a href="../articles/web_only/compiled_models.html">Performance benefit by using compiled model definitions in mkin</a>
</li>
<li>
<a href="../articles/twa.html">Calculation of time weighted average concentrations with mkin</a>
</li>
<li>
<a href="../articles/web_only/NAFTA_examples.html">Example evaluation of NAFTA SOP Attachment examples</a>
</li>
</ul>
</li>
<li>
<a href="../news/index.html">News</a>
</li>
</ul>
<ul class="nav navbar-nav navbar-right">
</ul>
</div><!--/.nav-collapse -->
</div><!--/.container -->
</div><!--/.navbar -->
</header>
<div class="row">
<div class="col-md-9 contents">
<div class="page-header">
<h1>Synthetic datasets for one parent compound with two metabolites</h1>
<div class="hidden name"><code>synthetic_data_for_UBA_2014.Rd</code></div>
</div>
<div class="ref-description">
<p>The 12 datasets were generated using four different models and three different
variance components. The four models are either the SFO or the DFOP model with either
two sequential or two parallel metabolites.</p>
<p>Variance component 'a' is based on a normal distribution with standard deviation of 3,
Variance component 'b' is also based on a normal distribution, but with a standard deviation of 7.
Variance component 'c' is based on the error model from Rocke and Lorenzato (1995), with the
minimum standard deviation (for small y values) of 0.5, and a proportionality constant of 0.07
for the increase of the standard deviation with y. Note that this is a simplified version
of the error model proposed by Rocke and Lorenzato (1995), as in their model the error of the
measured values approximates lognormal distribution for high values, whereas we are using
normally distributed error components all along.</p>
<p>Initial concentrations for metabolites and all values where adding the variance component resulted
in a value below the assumed limit of detection of 0.1 were set to <code>NA</code>.</p>
<p>As an example, the first dataset has the title <code>SFO_lin_a</code> and is based on the SFO model
with two sequential metabolites (linear pathway), with added variance component 'a'.</p>
<p>Compare also the code in the example section to see the degradation models.</p>
</div>
<pre class="usage"><span class='no'>synthetic_data_for_UBA_2014</span></pre>
<h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>
<p>A list containing twelve datasets as an R6 class defined by <code><a href='mkinds.html'>mkinds</a></code>,
each containing, among others, the following components</p><dl class='dl-horizontal'>
<dt><code>title</code></dt><dd><p>The name of the dataset, e.g. <code>SFO_lin_a</code></p></dd>
<dt><code>data</code></dt><dd><p>A data frame with the data in the form expected by <code><a href='mkinfit.html'>mkinfit</a></code></p></dd>
</dl>
<h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>
<p>Ranke (2014) Prüfung und Validierung von Modellierungssoftware als Alternative
zu ModelMaker 4.0, Umweltbundesamt Projektnummer 27452</p>
<p>Rocke, David M. und Lorenzato, Stefan (1995) A two-component model for
measurement error in analytical chemistry. Technometrics 37(2), 176-184.</p>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'><span class='co'># The data have been generated using the following kinetic models</span>
<span class='no'>m_synth_SFO_lin</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"M1"</span>),
<span class='kw'>M1</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"M2"</span>),
<span class='kw'>M2</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'>
<span class='no'>m_synth_SFO_par</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"M1"</span>, <span class='st'>"M2"</span>),
<span class='kw'>sink</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>),
<span class='kw'>M1</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>),
<span class='kw'>M2</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'>
<span class='no'>m_synth_DFOP_lin</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"DFOP"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"M1"</span>),
<span class='kw'>M1</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"M2"</span>),
<span class='kw'>M2</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'>
<span class='no'>m_synth_DFOP_par</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"DFOP"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"M1"</span>, <span class='st'>"M2"</span>),
<span class='kw'>sink</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>),
<span class='kw'>M1</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>),
<span class='kw'>M2</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'>
<span class='co'># The model predictions without intentional error were generated as follows</span>
<span class='no'>sampling_times</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>3</span>, <span class='fl'>7</span>, <span class='fl'>14</span>, <span class='fl'>28</span>, <span class='fl'>60</span>, <span class='fl'>90</span>, <span class='fl'>120</span>)
<span class='no'>d_synth_SFO_lin</span> <span class='kw'><-</span> <span class='fu'><a href='mkinpredict.html'>mkinpredict</a></span>(<span class='no'>m_synth_SFO_lin</span>,
<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_parent</span> <span class='kw'>=</span> <span class='fl'>0.7</span>, <span class='kw'>f_parent_to_M1</span> <span class='kw'>=</span> <span class='fl'>0.8</span>,
<span class='kw'>k_M1</span> <span class='kw'>=</span> <span class='fl'>0.3</span>, <span class='kw'>f_M1_to_M2</span> <span class='kw'>=</span> <span class='fl'>0.7</span>,
<span class='kw'>k_M2</span> <span class='kw'>=</span> <span class='fl'>0.02</span>),
<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>M1</span> <span class='kw'>=</span> <span class='fl'>0</span>, <span class='kw'>M2</span> <span class='kw'>=</span> <span class='fl'>0</span>),
<span class='no'>sampling_times</span>)
<span class='no'>d_synth_DFOP_lin</span> <span class='kw'><-</span> <span class='fu'><a href='mkinpredict.html'>mkinpredict</a></span>(<span class='no'>m_synth_DFOP_lin</span>,
<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k1</span> <span class='kw'>=</span> <span class='fl'>0.2</span>, <span class='kw'>k2</span> <span class='kw'>=</span> <span class='fl'>0.02</span>, <span class='kw'>g</span> <span class='kw'>=</span> <span class='fl'>0.5</span>,
<span class='kw'>f_parent_to_M1</span> <span class='kw'>=</span> <span class='fl'>0.5</span>, <span class='kw'>k_M1</span> <span class='kw'>=</span> <span class='fl'>0.3</span>,
<span class='kw'>f_M1_to_M2</span> <span class='kw'>=</span> <span class='fl'>0.7</span>, <span class='kw'>k_M2</span> <span class='kw'>=</span> <span class='fl'>0.02</span>),
<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>M1</span> <span class='kw'>=</span> <span class='fl'>0</span>, <span class='kw'>M2</span> <span class='kw'>=</span> <span class='fl'>0</span>),
<span class='no'>sampling_times</span>)
<span class='no'>d_synth_SFO_par</span> <span class='kw'><-</span> <span class='fu'><a href='mkinpredict.html'>mkinpredict</a></span>(<span class='no'>m_synth_SFO_par</span>,
<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_parent</span> <span class='kw'>=</span> <span class='fl'>0.2</span>,
<span class='kw'>f_parent_to_M1</span> <span class='kw'>=</span> <span class='fl'>0.8</span>, <span class='kw'>k_M1</span> <span class='kw'>=</span> <span class='fl'>0.01</span>,
<span class='kw'>f_parent_to_M2</span> <span class='kw'>=</span> <span class='fl'>0.2</span>, <span class='kw'>k_M2</span> <span class='kw'>=</span> <span class='fl'>0.02</span>),
<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>M1</span> <span class='kw'>=</span> <span class='fl'>0</span>, <span class='kw'>M2</span> <span class='kw'>=</span> <span class='fl'>0</span>),
<span class='no'>sampling_times</span>)
<span class='no'>d_synth_DFOP_par</span> <span class='kw'><-</span> <span class='fu'><a href='mkinpredict.html'>mkinpredict</a></span>(<span class='no'>m_synth_DFOP_par</span>,
<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k1</span> <span class='kw'>=</span> <span class='fl'>0.3</span>, <span class='kw'>k2</span> <span class='kw'>=</span> <span class='fl'>0.02</span>, <span class='kw'>g</span> <span class='kw'>=</span> <span class='fl'>0.7</span>,
<span class='kw'>f_parent_to_M1</span> <span class='kw'>=</span> <span class='fl'>0.6</span>, <span class='kw'>k_M1</span> <span class='kw'>=</span> <span class='fl'>0.04</span>,
<span class='kw'>f_parent_to_M2</span> <span class='kw'>=</span> <span class='fl'>0.4</span>, <span class='kw'>k_M2</span> <span class='kw'>=</span> <span class='fl'>0.01</span>),
<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>M1</span> <span class='kw'>=</span> <span class='fl'>0</span>, <span class='kw'>M2</span> <span class='kw'>=</span> <span class='fl'>0</span>),
<span class='no'>sampling_times</span>)
<span class='co'># Construct names for datasets with errors</span>
<span class='no'>d_synth_names</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste0</a></span>(<span class='st'>"d_synth_"</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"SFO_lin"</span>, <span class='st'>"SFO_par"</span>,
<span class='st'>"DFOP_lin"</span>, <span class='st'>"DFOP_par"</span>))
<span class='co'># Original function used or adding errors. The add_err function now published</span>
<span class='co'># with this package is a slightly generalised version where the names of</span>
<span class='co'># secondary compartments that should have an initial value of zero (M1 and M2</span>
<span class='co'># in this case) are not hardcoded any more.</span>
<span class='co'># add_err = function(d, sdfunc, LOD = 0.1, reps = 2, seed = 123456789)</span>
<span class='co'># {</span>
<span class='co'># set.seed(seed)</span>
<span class='co'># d_long = mkin_wide_to_long(d, time = "time")</span>
<span class='co'># d_rep = data.frame(lapply(d_long, rep, each = 2))</span>
<span class='co'># d_rep$value = rnorm(length(d_rep$value), d_rep$value, sdfunc(d_rep$value))</span>
<span class='co'>#</span>
<span class='co'># d_rep[d_rep$time == 0 & match(d_rep$name, c("M1", "M2"), "value"] <- 0</span>
<span class='co'># d_NA <- transform(d_rep, value = ifelse(value < LOD, NA, value))</span>
<span class='co'># d_NA$value <- round(d_NA$value, 1)</span>
<span class='co'># return(d_NA)</span>
<span class='co'># }</span>
<span class='co'># The following is the simplified version of the two-component model of Rocke</span>
<span class='co'># and Lorenzato (1995)</span>
<span class='no'>sdfunc_twocomp</span> <span class='kw'>=</span> <span class='kw'>function</span>(<span class='no'>value</span>, <span class='no'>sd_low</span>, <span class='no'>rsd_high</span>) {
<span class='fu'><a href='https://rdrr.io/r/base/MathFun.html'>sqrt</a></span>(<span class='no'>sd_low</span>^<span class='fl'>2</span> + <span class='no'>value</span>^<span class='fl'>2</span> * <span class='no'>rsd_high</span>^<span class='fl'>2</span>)
}
<span class='co'># Add the errors.</span>
<span class='kw'>for</span> (<span class='no'>d_synth_name</span> <span class='kw'>in</span> <span class='no'>d_synth_names</span>)
{
<span class='no'>d_synth</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/get.html'>get</a></span>(<span class='no'>d_synth_name</span>)
<span class='fu'><a href='https://rdrr.io/r/base/assign.html'>assign</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste0</a></span>(<span class='no'>d_synth_name</span>, <span class='st'>"_a"</span>), <span class='fu'><a href='add_err.html'>add_err</a></span>(<span class='no'>d_synth</span>, <span class='kw'>function</span>(<span class='no'>value</span>) <span class='fl'>3</span>))
<span class='fu'><a href='https://rdrr.io/r/base/assign.html'>assign</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste0</a></span>(<span class='no'>d_synth_name</span>, <span class='st'>"_b"</span>), <span class='fu'><a href='add_err.html'>add_err</a></span>(<span class='no'>d_synth</span>, <span class='kw'>function</span>(<span class='no'>value</span>) <span class='fl'>7</span>))
<span class='fu'><a href='https://rdrr.io/r/base/assign.html'>assign</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste0</a></span>(<span class='no'>d_synth_name</span>, <span class='st'>"_c"</span>), <span class='fu'><a href='add_err.html'>add_err</a></span>(<span class='no'>d_synth</span>,
<span class='kw'>function</span>(<span class='no'>value</span>) <span class='fu'>sdfunc_twocomp</span>(<span class='no'>value</span>, <span class='fl'>0.5</span>, <span class='fl'>0.07</span>)))
}
<span class='no'>d_synth_err_names</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(
<span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/rep.html'>rep</a></span>(<span class='no'>d_synth_names</span>, <span class='kw'>each</span> <span class='kw'>=</span> <span class='fl'>3</span>), <span class='no'>letters</span>[<span class='fl'>1</span>:<span class='fl'>3</span>], <span class='kw'>sep</span> <span class='kw'>=</span> <span class='st'>"_"</span>)
)
<span class='co'># This is just one example of an evaluation using the kinetic model used for</span>
<span class='co'># the generation of the data</span>
<span class='no'>fit</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>m_synth_SFO_lin</span>, <span class='no'>synthetic_data_for_UBA_2014</span><span class='kw'>[[</span><span class='fl'>1</span>]]$<span class='no'>data</span>,
<span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)
<span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span>(<span class='no'>fit</span>)</div><div class='img'><img src='synthetic_data_for_UBA_2014-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#> mkin version used for fitting: 0.9.49.6
#> R version used for fitting: 3.6.1
#> Date of fit: Thu Sep 19 12:43:02 2019
#> Date of summary: Thu Sep 19 12:43:02 2019
#>
#> Equations:
#> d_parent/dt = - k_parent * parent
#> d_M1/dt = + f_parent_to_M1 * k_parent * parent - k_M1 * M1
#> d_M2/dt = + f_M1_to_M2 * k_M1 * M1 - k_M2 * M2
#>
#> Model predictions using solution type deSolve
#>
#> Fitted using 847 model solutions performed in 2.51 s
#>
#> Error model: Constant variance
#>
#> Error model algorithm: OLS
#>
#> Starting values for parameters to be optimised:
#> value type
#> parent_0 101.350000 state
#> k_parent 0.100000 deparm
#> k_M1 0.100100 deparm
#> k_M2 0.100200 deparm
#> f_parent_to_M1 0.500000 deparm
#> f_M1_to_M2 0.500000 deparm
#> sigma 2.273126 error
#>
#> Starting values for the transformed parameters actually optimised:
#> value lower upper
#> parent_0 101.350000 -Inf Inf
#> log_k_parent -2.302585 -Inf Inf
#> log_k_M1 -2.301586 -Inf Inf
#> log_k_M2 -2.300587 -Inf Inf
#> f_parent_ilr_1 0.000000 -Inf Inf
#> f_M1_ilr_1 0.000000 -Inf Inf
#> sigma 2.273126 0 Inf
#>
#> Fixed parameter values:
#> value type
#> M1_0 0 state
#> M2_0 0 state
#>
#> Optimised, transformed parameters with symmetric confidence intervals:
#> Estimate Std. Error Lower Upper
#> parent_0 102.1000 1.57000 98.8600 105.3000
#> log_k_parent -0.3020 0.03885 -0.3812 -0.2229
#> log_k_M1 -1.2070 0.07123 -1.3520 -1.0620
#> log_k_M2 -3.9010 0.06571 -4.0350 -3.7670
#> f_parent_ilr_1 0.8492 0.16640 0.5103 1.1880
#> f_M1_ilr_1 0.6780 0.17600 0.3196 1.0360
#> sigma 2.2730 0.25740 1.7490 2.7970
#>
#> Parameter correlation:
#> parent_0 log_k_parent log_k_M1 log_k_M2 f_parent_ilr_1
#> parent_0 1.000e+00 3.933e-01 -1.605e-01 2.819e-02 -4.624e-01
#> log_k_parent 3.933e-01 1.000e+00 -4.082e-01 7.166e-02 -5.682e-01
#> log_k_M1 -1.605e-01 -4.082e-01 1.000e+00 -3.929e-01 7.478e-01
#> log_k_M2 2.819e-02 7.166e-02 -3.929e-01 1.000e+00 -2.658e-01
#> f_parent_ilr_1 -4.624e-01 -5.682e-01 7.478e-01 -2.658e-01 1.000e+00
#> f_M1_ilr_1 1.614e-01 4.102e-01 -8.109e-01 5.419e-01 -8.605e-01
#> sigma -3.704e-09 -1.104e-08 5.922e-08 -3.673e-08 5.867e-08
#> f_M1_ilr_1 sigma
#> parent_0 1.614e-01 -3.704e-09
#> log_k_parent 4.102e-01 -1.104e-08
#> log_k_M1 -8.109e-01 5.922e-08
#> log_k_M2 5.419e-01 -3.673e-08
#> f_parent_ilr_1 -8.605e-01 5.867e-08
#> f_M1_ilr_1 1.000e+00 -8.075e-08
#> sigma -8.075e-08 1.000e+00
#>
#> Backtransformed parameters:
#> Confidence intervals for internally transformed parameters are asymmetric.
#> t-test (unrealistically) based on the assumption of normal distribution
#> for estimators of untransformed parameters.
#> Estimate t value Pr(>t) Lower Upper
#> parent_0 102.10000 65.000 7.281e-36 98.86000 105.30000
#> k_parent 0.73930 25.740 2.948e-23 0.68310 0.80020
#> k_M1 0.29920 14.040 1.577e-15 0.25880 0.34590
#> k_M2 0.02023 15.220 1.653e-16 0.01769 0.02312
#> f_parent_to_M1 0.76870 18.370 7.295e-19 0.67300 0.84290
#> f_M1_to_M2 0.72290 14.500 6.418e-16 0.61110 0.81240
#> sigma 2.27300 8.832 2.161e-10 1.74900 2.79700
#>
#> FOCUS Chi2 error levels in percent:
#> err.min n.optim df
#> All data 8.454 6 17
#> parent 8.660 2 6
#> M1 10.583 2 5
#> M2 3.586 2 6
#>
#> Resulting formation fractions:
#> ff
#> parent_M1 0.7687
#> parent_sink 0.2313
#> M1_M2 0.7229
#> M1_sink 0.2771
#>
#> Estimated disappearance times:
#> DT50 DT90
#> parent 0.9376 3.114
#> M1 2.3170 7.697
#> M2 34.2689 113.839
#>
#> Data:
#> time variable observed predicted residual
#> 0 parent 101.5 1.021e+02 -0.56248
#> 0 parent 101.2 1.021e+02 -0.86248
#> 1 parent 53.9 4.873e+01 5.17118
#> 1 parent 47.5 4.873e+01 -1.22882
#> 3 parent 10.4 1.111e+01 -0.70773
#> 3 parent 7.6 1.111e+01 -3.50773
#> 7 parent 1.1 5.772e-01 0.52283
#> 7 parent 0.3 5.772e-01 -0.27717
#> 14 parent 3.5 3.264e-03 3.49674
#> 28 parent 3.2 1.045e-07 3.20000
#> 90 parent 0.6 -1.875e-11 0.60000
#> 120 parent 3.5 -2.805e-11 3.50000
#> 1 M1 36.4 3.479e+01 1.61088
#> 1 M1 37.4 3.479e+01 2.61088
#> 3 M1 34.3 3.937e+01 -5.07027
#> 3 M1 39.8 3.937e+01 0.42973
#> 7 M1 15.1 1.549e+01 -0.38715
#> 7 M1 17.8 1.549e+01 2.31285
#> 14 M1 5.8 1.995e+00 3.80469
#> 14 M1 1.2 1.995e+00 -0.79531
#> 60 M1 0.5 2.111e-06 0.50000
#> 90 M1 3.2 2.913e-10 3.20000
#> 120 M1 1.5 3.625e-11 1.50000
#> 120 M1 0.6 3.625e-11 0.60000
#> 1 M2 4.8 4.455e+00 0.34517
#> 3 M2 20.9 2.153e+01 -0.62527
#> 3 M2 19.3 2.153e+01 -2.22527
#> 7 M2 42.0 4.192e+01 0.07941
#> 7 M2 43.1 4.192e+01 1.17941
#> 14 M2 49.4 4.557e+01 3.83353
#> 14 M2 44.3 4.557e+01 -1.26647
#> 28 M2 34.6 3.547e+01 -0.87275
#> 28 M2 33.0 3.547e+01 -2.47275
#> 60 M2 18.8 1.858e+01 0.21837
#> 60 M2 17.6 1.858e+01 -0.98163
#> 90 M2 10.6 1.013e+01 0.47130
#> 90 M2 10.8 1.013e+01 0.67130
#> 120 M2 9.8 5.521e+00 4.27893
#> 120 M2 3.3 5.521e+00 -2.22107</div></pre>
</div>
<div class="col-md-3 hidden-xs hidden-sm" id="sidebar">
<h2>Contents</h2>
<ul class="nav nav-pills nav-stacked">
<li><a href="#format">Format</a></li>
<li><a href="#source">Source</a></li>
<li><a href="#examples">Examples</a></li>
</ul>
</div>
</div>
<footer>
<div class="copyright">
<p>Developed by Johannes Ranke.</p>
</div>
<div class="pkgdown">
<p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.4.1.</p>
</div>
</footer>
</div>
</body>
</html>
|
