-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathnceas-examples.html
1209 lines (567 loc) · 36.5 KB
/
nceas-examples.html
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
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
<!DOCTYPE html PUBLIC
"-//W3C//DTD XHTML 1.0 Transitional//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en-us"
lang="en-us">
<head>
<meta http-equiv="Content-Type"
content="text/html;charset=utf-8" />
<meta name="generator" content="Plone - http://plone.org" />
<!-- Internet Explorer fix, forces IE8 into newest possible rendering
engine even if it's on an intranet. This has to be defined before any
script/style tags. -->
<meta http-equiv="X-UA-Compatible" content="IE=edge" />
<base href="https://groups.nceas.ucsb.edu/non-linear-modeling/projects/" /><!--[if lt IE 7]></base><![endif]-->
<link rel="kss-base-url" href="https://groups.nceas.ucsb.edu/non-linear-modeling/projects" />
<style type="text/css">@import url(https://groups.nceas.ucsb.edu/non-linear-modeling/portal_css/NCEAS%20Collaboration%20Theme/base-cachekey3841.css);</style>
<style type="text/css">@import url(https://groups.nceas.ucsb.edu/non-linear-modeling/portal_css/NCEAS%20Collaboration%20Theme/resourcenceas_collab.theme.stylesheetsmain-cachekey7656.css);</style>
<script type="text/javascript"
src="https://groups.nceas.ucsb.edu/non-linear-modeling/portal_javascripts/NCEAS%20Collaboration%20Theme/jquery-cachekey6104.js">
</script>
<script type="text/javascript"
src="https://groups.nceas.ucsb.edu/non-linear-modeling/portal_javascripts/NCEAS%20Collaboration%20Theme/resourcebase2-dom-fp-cachekey4258.js">
</script>
<script type="text/javascript"
src="https://groups.nceas.ucsb.edu/non-linear-modeling/portal_javascripts/NCEAS%20Collaboration%20Theme/resourcedropdown-menu-cachekey1938.js">
</script>
<script type="text/javascript"
src="https://groups.nceas.ucsb.edu/non-linear-modeling/portal_javascripts/NCEAS%20Collaboration%20Theme/clouseau_trigger-cachekey6757.js">
</script>
<link rel="kinetic-stylesheet" type="text/css"
href="https://groups.nceas.ucsb.edu/non-linear-modeling/portal_kss/NCEAS%20Collaboration%20Theme/at-cachekey8217.kss" />
<title>Projects — NCEAS Working Group</title>
<!-- IE6 workaround CSS/JS -->
<!--[if lte IE 7]>
<style type="text/css" media="all">@import url(https://groups.nceas.ucsb.edu/non-linear-modeling/IEFixes.css);</style>
<script type="text/javascript"
src="https://groups.nceas.ucsb.edu/non-linear-modeling/iefixes.js">
</script>
<![endif]-->
<link rel="shortcut icon" type="image/x-icon"
href="https://groups.nceas.ucsb.edu/non-linear-modeling/favicon.ico" />
<link rel="home"
href="https://groups.nceas.ucsb.edu/non-linear-modeling"
title="Front page" />
<link rel="contents"
href="https://groups.nceas.ucsb.edu/non-linear-modeling/sitemap"
title="Site Map" />
<link rel="search"
href="https://groups.nceas.ucsb.edu/non-linear-modeling/search_form"
title="Search this site" />
<!-- Disable IE6 image toolbar -->
<meta http-equiv="imagetoolbar" content="no" />
</head>
<body class="section-projects template-reflector_view"
dir="ltr">
<div id="visual-portal-wrapper">
<div id="portal-top">
<div id="portal-header">
<p class="hiddenStructure">
<a accesskey="2"
href="https://groups.nceas.ucsb.edu/non-linear-modeling/projects#documentContent">Skip to content.</a> |
<a accesskey="6"
href="https://groups.nceas.ucsb.edu/non-linear-modeling/projects#portlet-navigation-tree">Skip to navigation</a>
</p>
<ul id="portal-siteactions">
<li id="siteaction-sitemap"><a
href="https://groups.nceas.ucsb.edu/non-linear-modeling/sitemap"
accesskey="3" title="Site Map">Site Map</a></li>
<li id="siteaction-accessibility"><a
href="https://groups.nceas.ucsb.edu/non-linear-modeling/accessibility-info"
accesskey="0" title="Accessibility">Accessibility</a></li>
<li id="siteaction-contact"><a
href="https://groups.nceas.ucsb.edu/non-linear-modeling/contact-info"
accesskey="9" title="Contact">Contact</a></li>
</ul>
<div id="portal-searchbox">
<form name="searchform"
action="https://groups.nceas.ucsb.edu/non-linear-modeling/search">
<label for="searchGadget" class="hiddenStructure">Search Site</label>
<div class="LSBox">
<input name="SearchableText" type="text" size="18"
title="Search Site" accesskey="4"
class="inputLabel" id="searchGadget" />
<input class="searchButton" type="submit"
value="Search" />
<!--
<div class="searchSection">
<input id="searchbox_currentfolder_only"
class="noborder"
type="checkbox"
name="path"
tal:attributes="value view/folder_path"
/>
<label for="searchbox_currentfolder_only"
i18n:translate="label_searchbox_currentfolder_only"
style="cursor: pointer">
only in current section
</label>
</div>
-->
<div class="LSResult" id="LSResult" style=""><div class="LSShadow" id="LSShadow"></div></div>
</div>
</form>
<div id="portal-advanced-search" class="hiddenStructure">
<a href="https://groups.nceas.ucsb.edu/non-linear-modeling/search_form"
accesskey="5">
Advanced Search…
</a>
</div>
</div>
<a id="portal-logo" accesskey="1"
href="https://groups.nceas.ucsb.edu/non-linear-modeling">
<img src="https://groups.nceas.ucsb.edu/non-linear-modeling/logo.jpg" alt="" title="" height="70" width="520" /></a>
<h5 class="hiddenStructure">Sections</h5>
<div id="globalnav-wrapper">
<ul id="portal-globalnav">
<li id="portaltab-index_html" class="selected">
<a href="https://groups.nceas.ucsb.edu/non-linear-modeling"
class="mainTabContent" title="">Home</a>
</li>
</ul>
<div class="visualClear" id="post-sections-clear"><!-- --></div>
</div>
</div>
<div id="portal-personaltools-wrapper">
<h5 class="hiddenStructure">Personal tools</h5>
<ul id="portal-personaltools" class="visualInline">
<li>
<a href="https://groups.nceas.ucsb.edu/non-linear-modeling/login_form">Log in</a>
</li>
</ul>
</div>
<div id="portal-breadcrumbs">
<span id="breadcrumbs-you-are-here">You
are here:</span>
<a href="https://groups.nceas.ucsb.edu/non-linear-modeling">Home</a>
<span class="breadcrumbSeparator">
→
</span>
<span dir="ltr">
<span>Projects</span>
</span>
</div>
</div>
<div class="visualClear" id="clear-space-before-wrapper-table"><!-- --></div>
<table id="portal-columns">
<tbody>
<tr>
<td id="portal-column-one">
<div class="visualPadding">
<div id="portletwrapper-706c6f6e652e6c656674636f6c756d6e0a636f6e746578740a2f6e6f6e2d6c696e6561722d6d6f64656c696e670a6e617669676174696f6e"
class="portletWrapper kssattr-portlethash-706c6f6e652e6c656674636f6c756d6e0a636f6e746578740a2f6e6f6e2d6c696e6561722d6d6f64656c696e670a6e617669676174696f6e">
<dl class="portlet portletNavigationTree">
<dt class="portletHeader">
<span class="portletTopLeft"></span>
<a href="https://groups.nceas.ucsb.edu/non-linear-modeling/sitemap"
class="tile">Navigation</a>
<span class="portletTopRight"></span>
</dt>
<dd class="portletItem lastItem">
<ul class="navTree navTreeLevel0">
<li class="navTreeItem visualNoMarker navTreeCurrentNode navTreeFolderish">
<div>
<a href="https://groups.nceas.ucsb.edu/non-linear-modeling/projects"
class="state-published navTreeCurrentItem navTreeCurrentNode navTreeFolderish"
title="">
<img alt="" width="16" height="16" src="https://groups.nceas.ucsb.edu/non-linear-modeling/folder_icon.gif" class="icon-Reflector" />
<span>Projects</span>
</a>
</div>
</li>
<li class="navTreeItem visualNoMarker navTreeFolderish">
<div>
<a href="https://groups.nceas.ucsb.edu/non-linear-modeling/documents"
class="state-published navTreeFolderish" title="">
<img alt="" width="16" height="16" src="https://groups.nceas.ucsb.edu/non-linear-modeling/folder_icon.gif" class="icon-Folder" />
<span>Documents</span>
</a>
</div>
</li>
<li class="navTreeItem visualNoMarker navTreeFolderish">
<div>
<a href="https://groups.nceas.ucsb.edu/non-linear-modeling/applications"
class="state-visible navTreeFolderish" title="">
<img alt="" width="16" height="16" src="https://groups.nceas.ucsb.edu/non-linear-modeling/folder_icon.gif" class="icon-Folder" />
<span>Applications</span>
</a>
</div>
</li>
<li class="navTreeItem visualNoMarker navTreeFolderish">
<div>
<a href="https://groups.nceas.ucsb.edu/non-linear-modeling/tools"
class="state-visible navTreeFolderish"
title="Tools and other helpful information for the project">
<img alt="" width="16" height="16" src="https://groups.nceas.ucsb.edu/non-linear-modeling/folder_icon.gif" class="icon-Folder" />
<span>Tools</span>
</a>
</div>
</li>
<li class="navTreeItem visualNoMarker">
<div>
<a href="https://groups.nceas.ucsb.edu/non-linear-modeling/agenda"
class="state-visible" title="Workshop agenda">
<img alt="" width="16" height="16" src="https://groups.nceas.ucsb.edu/non-linear-modeling/document_icon.gif" class="icon-Page" />
<span>Agenda</span>
</a>
</div>
</li>
<li class="navTreeItem visualNoMarker">
<div>
<a href="https://groups.nceas.ucsb.edu/non-linear-modeling/detailed-project-description"
class="state-visible"
title="A detailed description of the working groups objectives and activities">
<img alt="" width="16" height="16" src="https://groups.nceas.ucsb.edu/non-linear-modeling/document_icon.gif" class="icon-Page" />
<span>project description</span>
</a>
</div>
</li>
<li class="navTreeItem visualNoMarker navTreeFolderish">
<div>
<a href="https://groups.nceas.ucsb.edu/non-linear-modeling/discuss"
class="state-published navTreeFolderish" title="">
<img alt="" width="16" height="16" src="https://groups.nceas.ucsb.edu/non-linear-modeling/ploneboard_icon.gif" class="icon-Message Board" />
<span>Discuss</span>
</a>
</div>
</li>
</ul>
<span class="portletBottomLeft"></span>
<span class="portletBottomRight"></span>
</dd>
</dl>
</div>
<div id="portletwrapper-706c6f6e652e6c656674636f6c756d6e0a636f6e746578740a2f6e6f6e2d6c696e6561722d6d6f64656c696e670a6c6f67696e"
class="portletWrapper kssattr-portlethash-706c6f6e652e6c656674636f6c756d6e0a636f6e746578740a2f6e6f6e2d6c696e6561722d6d6f64656c696e670a6c6f67696e"><dl class="portlet portletLogin">
<dt class="portletHeader">
<span class="portletTopLeft"></span>
<a href="https://groups.nceas.ucsb.edu/non-linear-modeling/login_form"
class="tile">Log in</a>
<span class="portletTopRight"></span>
</dt>
<dd class="portletItem odd">
<form action="https://groups.nceas.ucsb.edu/non-linear-modeling/login_form"
name="loginform" method="post">
<input type="hidden" name="form.submitted" value="1" />
<input type="hidden" name="came_from"
value="https://groups.nceas.ucsb.edu/non-linear-modeling/projects" />
<input type="hidden" name="js_enabled" id="js_enabled" value="0" />
<input type="hidden" name="cookies_enabled" id="cookies_enabled" value="" />
<input type="hidden" name="login_name" id="login_name" value="" />
<input type="hidden" name="pwd_empty" id="pwd_empty" value="0" />
<div class="field">
<label for="__ac_name">Login Name</label>
<br />
<input type="text" size="10"
alt="Login Name" name="__ac_name"
value="" id="__ac_name" />
</div>
<div class="field">
<label for="__ac_password">Password</label>
<br />
<input type="password" size="10"
alt="Password" name="__ac_password"
id="__ac_password" />
</div>
<div class="field" style="margin-bottom: 8px">
<input type="checkbox" class="noborder formRememberName" style="margin-bottom: 3px" name="__ac_persistent" value="1" id="cb_remember" />
<label for="cb_remember">Remember me.</label>
</div>
<div class="cookiesMessage portalMessage"
style="display:none">
Cookies are not enabled. You must enable cookies before you
can log in.
</div>
<div class="formControls">
<input class="context" type="submit"
name="submit" value="Log in"
alt="Log in" />
</div>
</form>
</dd>
<dd class="portletItem even">
<a href="https://groups.nceas.ucsb.edu/non-linear-modeling/mail_password_form"
class="tile">
<img src="https://groups.nceas.ucsb.edu/non-linear-modeling/info_icon.gif" alt="" title="" height="16" width="16" />
Forgot your password?
</a>
</dd>
</dl>
</div>
</div>
</td>
<td id="portal-column-content">
<div class="">
<div id="region-content" class="documentContent">
<span id="contentTopLeft"></span>
<span id="contentTopRight"></span>
<a name="documentContent"></a>
<dl class="portalMessage info" id="kssPortalMessage" style="display:none">
<dt>Info</dt>
<dd></dd>
</dl>
<div id="viewlet-above-content"></div>
<div id="content">
<div>
<h1 class="documentFirstHeading">Projects</h1>
<div class="documentByLine" id="plone-document-byline">
</div>
<a href="https://groups.nceas.ucsb.edu/non-linear-modeling"
class="link-parent">
Up one level
</a>
<div>
<ul>
<span><h1>Orange tree growth</h1>
<p>The orange tree growth data, originally taken from Draper & Smith (1981) and reproduced in Draper & Smith (1998), p. 559, was used by Pinheiro & Bates (2000, Ch. 8.2) to illustrate how a logistic growth curve model with random effects can be implemented with the S-Plus function nlme. The data contain measurements of trunk circumferences (mm) made at seven occasions for each of five orange trees. The data is available within R in "datasets::Orange".
</p>
<p>The errors within trees are assumed to be normally distributed and independent; the data can be straightforwardly analyzed either by standard nonlinear regression (assuming each tree follows an independent growth curve) or by nonlinear mixed-effects models (allowing the growth parameters to be random variables from an underlying population distribution).
</p></span>
<dl>
<dt>Project report:</dt>
<dd><a href="https://groups.nceas.ucsb.edu/non-linear-modeling/projects/OrangeTree/WRITEUP/OrangeTree.pdf">OrangeTree.pdf</a></dd>
<dd><a href="https://groups.nceas.ucsb.edu/non-linear-modeling/projects/OrangeTree/WRITEUP/sim-bias.pdf">sim-bias.pdf</a></dd>
<dd><a href="https://groups.nceas.ucsb.edu/non-linear-modeling/projects/OrangeTree/WRITEUP/sim-ci.pdf">sim-ci.pdf</a></dd>
</dl>
<p>[<a href="https://groups.nceas.ucsb.edu/non-linear-modeling/projects/OrangeTree">Source code</a>]</p>
<hr>
<!-- <div class="documentActions"><ul><li><a href="#"
tal:attributes="href python:item.absolute_url()">Source code</a></li></ul></div> -->
<span><h1>Mineralization of terbuthylazine</h1>
<p>Terbuthylazine is a herbicide used in agriculture. It is a so-called s-triazin like
atrazine, which has been banned in Denmark after suspicion of causing cancer.<br />
Terbuthylazine can be bound to the soil, but free terbuthylazine can be washed into
the drinking water. Some bacteria can mineralize it. This data is part of a larger
experiment to determine the ability of certain bacteria to mineralize terbuthylazine,
and to estimate the mineralization rate.
</p>
<p>This is a fairly straightforward nonlinear least-squares problem, with normally distributed residuals and no random effects or latent variables.
The deterministic part of the model
is the solution to a set of coupled ordinary differential equations (ODEs)
for the concentrations in different compartments.
Because the ODEs are linear, the deterministic solution can be found directly
in terms of a matrix exponential, for which functions exist in all
three of ADMB, BUGS, and R. From there it is simply a matter of defining
a normal likelihood, or equivalently a least-squares expression, and
minimizing it. The main differences appear in the speed and robustness
of the matrix exponential formulations in different software tools.
</p>
<p>author: Anders Nielsen
</p></span>
<dl>
<dt>Project report:</dt>
<dd><a href="https://groups.nceas.ucsb.edu/non-linear-modeling/projects/min/WRITEUP/min.pdf">min.pdf</a></dd>
</dl>
<p>[<a href="https://groups.nceas.ucsb.edu/non-linear-modeling/projects/min">Source code</a>]</p>
<hr>
<!-- <div class="documentActions"><ul><li><a href="#"
tal:attributes="href python:item.absolute_url()">Source code</a></li></ul></div> -->
<span><h1>Fitting N-mixture models with random observer effects</h1>
<p>Most protocols for estimating abundance while taking detectability into account require that individuals can be individually identified, a condition which often requires capturing and marking of animals. This is costly and therefore the N-mixture, or binomial mixture, model of Royle (2004) is an appealing alternative: this model yields estimates of abundance from spatially and temporally replicated counts of unmarked animals alone. Typical applications of the Nmix model
require the assumption of some effects as random, for instance, to
account for intrinsic differences in the ability of field ornithologists to
detect and identify birds.
</p>
<p>This example shows worked BUGS and ADMB solutions to estimating an N-mixture model and demonstrates their use on simulated (pseudo-)data. (1) The BUGS and ADMB estimates of the fixed effects and variances are very similar. (2) Differences are more pronounced in the estimates of the random effects. The posterior means from WinBUGS were more accurate than the ADMB estimates (RMSE = 0.22 vs 0.43). (3) The BUGS estimates were more precise than ADMB estimates of the random effects. On average, the posterior standard deviations obtained by BUGS were 47% smaller than the standard deviations estimated by ADMB. ADMB is much faster (4 vs. 12 minutes per estimate).
</p>
<ul>
<li>
authors: Richard Chandler, Marc Kery, and Hans Skaug
</li>
</ul></span>
<dl>
<dt>Project report:</dt>
<dd><a href="https://groups.nceas.ucsb.edu/non-linear-modeling/projects/nmix/WRITEUP/nmix.pdf">nmix.pdf</a></dd>
</dl>
<p>[<a href="https://groups.nceas.ucsb.edu/non-linear-modeling/projects/nmix">Source code</a>]</p>
<hr>
<!-- <div class="documentActions"><ul><li><a href="#"
tal:attributes="href python:item.absolute_url()">Source code</a></li></ul></div> -->
<span><h1>Owl nestling negotiation</h1>
<p>The data for this example, taken from Zuur et al. (2009) and
ultimately from Roulin and Bersier (2007), quantify the number of
vocalizations (sibling negotiations) by owl chicks in different nests as a
function of food treatment (deprived or satiated), the sex of the
parent, and arrival time of the parent at the nest.
</p>
<p>This problem is basically a zero-inflated generalized linear mixed model, where numbers of negotiations are the response variable, food treatment/arrival time/parental sex are the fixed-effect predictors, and sites are a random effect. The presence of zero-inflation puts the problem beyond standard GLMM implementations. In R, the MCMCglmm package allows for zero-inflation, or one can implement an expectation-maximization function. The problem is relatively straightforward in JAGS, or in ADMB, and one can also use the glmmADMB package in R.
</p>
<p>authors: Ben Bolker, Mollie Brooks, Beth Gardner, Cleridy Lennert, Mihoko Minami
</p></span>
<dl>
<dt>Project report:</dt>
<dd><a href="https://groups.nceas.ucsb.edu/non-linear-modeling/projects/owls/WRITEUP/owls.pdf">owls.pdf</a></dd>
</dl>
<p>[<a href="https://groups.nceas.ucsb.edu/non-linear-modeling/projects/owls">Source code</a>]</p>
<hr>
<!-- <div class="documentActions"><ul><li><a href="#"
tal:attributes="href python:item.absolute_url()">Source code</a></li></ul></div> -->
<span><h1>Skate mortality: Bayesian state-space model</h1>
<p>The goal of the model was to obtain decadal mortality estimates of three different size classes of winter skates (<em>Leucoraja ocellata</em>) on the eastern Scotian Shelf. The time series are largely non-informative for several of the model parameters (catchability, recruitment rate, and stage transition probability), so informative Bayesian priors are used.
</p>
<p>The model described here is a Bayesian state-space model implemented in both JAGS and AD Model Builder. The model description and alternative model formulations are fully described in Swain et al. (2009)
</p>
<p>authors: Trevor Davies and Steve Martell
</p></span>
<dl>
<dt>Project report:</dt>
<dd><a href="https://groups.nceas.ucsb.edu/non-linear-modeling/projects/skate/WRITEUP/skate.pdf">skate.pdf</a></dd>
</dl>
<p>[<a href="https://groups.nceas.ucsb.edu/non-linear-modeling/projects/skate">Source code</a>]</p>
<hr>
<!-- <div class="documentActions"><ul><li><a href="#"
tal:attributes="href python:item.absolute_url()">Source code</a></li></ul></div> -->
<span><h1>Tadpole mortality as a function of size</h1>
<p>The data are originally from Vonesh and Bolker (2005), describing the
numbers of reed frog (<em>Hyperolius spinigularis</em>) tadpoles killed by
predators as a function of size in a small-scale field trial. Our
main interest is in a quantitative description of the "window of
vulnerability", defined as the unimodal pattern of proportion killed as a
function of size. In various contexts, we can use this description
either to describe and test differences among treatments (e.g., does
the window of vulnerability differ by predator size, or with tadpoles
exposed to different predator cues?) or to project the effects of
growth and mortality rates through a life stage. See the reference above
and McCoy et al. (2011) for more details and examples.
</p>
<p>This basic example is
essentially a maximum likelihood estimation problem
with a binomial response variable. The data set is small, there
are no random effects or latent variables, and the problem
is low-dimensional, with only a single predictor and a single
response variable and only three parameters in the statistical
model used.
</p>
<ul>
<li>
author: Ben Bolker
</li>
</ul></span>
<dl>
<dt>Project report:</dt>
<dd><a href="https://groups.nceas.ucsb.edu/non-linear-modeling/projects/tadpole/WRITEUP/tadpole.pdf">tadpole.pdf</a></dd>
</dl>
<p>[<a href="https://groups.nceas.ucsb.edu/non-linear-modeling/projects/tadpole">Source code</a>]</p>
<hr>
<!-- <div class="documentActions"><ul><li><a href="#"
tal:attributes="href python:item.absolute_url()">Source code</a></li></ul></div> -->
<span><h1>Theta-logistic population growth model</h1>
<p>The example is a theta-logistic nonlinear state-space population model. The population size is modelled as a nonlinear function of its previous size, with a discrete-time theta-logistic process model: <em>N</em>(t+1)=theta-logistic(<em>N</em>(t)) plus a normally distributed process error, and the observation error is also normally distributed. This example uses simulated data from the same model to test it. More details are available in Pedersen et al. (2011).
</p>
<ul>
<li>
AD Model Builder is fastest, but requires the most code.
</li>
<li>
JAGS is slower, but not too bad for this relatively simple problem (and produces much wider credible/confidence intervals).
</li>
<li>
A hidden Markov model can be implemented in R, but takes some effort and is quite slow.
</li>
</ul>
<p>author: Casper W. Berg
</p></span>
<dl>
<dt>Project report:</dt>
<dd><a href="https://groups.nceas.ucsb.edu/non-linear-modeling/projects/theta/WRITEUP/theta.pdf">theta.pdf</a></dd>
</dl>
<p>[<a href="https://groups.nceas.ucsb.edu/non-linear-modeling/projects/theta">Source code</a>]</p>
<hr>
<!-- <div class="documentActions"><ul><li><a href="#"
tal:attributes="href python:item.absolute_url()">Source code</a></li></ul></div> -->
<span><h1>Weeds: Modeling weed density over time</h1>
<p>The goal of this problem is to model weed density from 12 years of
data in the form of an S-shaped curve. The data are simply 12 densities
at equispaced index times. The suggested model was a three-parameter
logistic function, though an extension to estimate the variance
around the model is also of interest.
</p>
<p>The problem is relatively difficult, especially in its original presentation,
as it is badly scaled and there are nearly flat areas of the sum of squares or
likelihood surface. Moreover, the Hessian at the solution is effectively
singular, so methods based on Newton's iteration do rather badly, while
crude approaches such as Nelder-Mead may do better if they can be scaled
appropriately.
</p>
<p>author: John C. Nash and Anders Nielsen and Ben Bolker
</p></span>
<dl>
<dt>Project report:</dt>