-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathrosetta_moderation.html
697 lines (608 loc) · 21.8 KB
/
rosetta_moderation.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
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<meta name="generator" content="pandoc" />
<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
<meta name="author" content="(Marcello Gallucci)" />
<title>Rosetta store: conditional mediation</title>
<script src="site_libs/header-attrs-2.25/header-attrs.js"></script>
<script src="site_libs/jquery-3.6.0/jquery-3.6.0.min.js"></script>
<meta name="viewport" content="width=device-width, initial-scale=1" />
<link href="site_libs/bootstrap-3.3.5/css/flatly.min.css" rel="stylesheet" />
<script src="site_libs/bootstrap-3.3.5/js/bootstrap.min.js"></script>
<script src="site_libs/bootstrap-3.3.5/shim/html5shiv.min.js"></script>
<script src="site_libs/bootstrap-3.3.5/shim/respond.min.js"></script>
<style>h1 {font-size: 34px;}
h1.title {font-size: 38px;}
h2 {font-size: 30px;}
h3 {font-size: 24px;}
h4 {font-size: 18px;}
h5 {font-size: 16px;}
h6 {font-size: 12px;}
code {color: inherit; background-color: rgba(0, 0, 0, 0.04);}
pre:not([class]) { background-color: white }</style>
<script src="site_libs/jqueryui-1.13.2/jquery-ui.min.js"></script>
<link href="site_libs/tocify-1.9.1/jquery.tocify.css" rel="stylesheet" />
<script src="site_libs/tocify-1.9.1/jquery.tocify.js"></script>
<script src="site_libs/navigation-1.1/tabsets.js"></script>
<link href="site_libs/highlightjs-9.12.0/default.css" rel="stylesheet" />
<script src="site_libs/highlightjs-9.12.0/highlight.js"></script>
<!-- Global site tag (gtag.js) - Google Analytics -->
<script async src="https://www.googletagmanager.com/gtag/js?id=UA-129366054-1"></script>
<script>
window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);}
gtag('js', new Date());
gtag('config', 'UA-129366054-1');
</script>
<style type="text/css">
code{white-space: pre-wrap;}
span.smallcaps{font-variant: small-caps;}
span.underline{text-decoration: underline;}
div.column{display: inline-block; vertical-align: top; width: 50%;}
div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
ul.task-list{list-style: none;}
</style>
<style type="text/css">code{white-space: pre;}</style>
<script type="text/javascript">
if (window.hljs) {
hljs.configure({languages: []});
hljs.initHighlightingOnLoad();
if (document.readyState && document.readyState === "complete") {
window.setTimeout(function() { hljs.initHighlighting(); }, 0);
}
}
</script>
<link rel="stylesheet" href="style.css" type="text/css" />
<style type = "text/css">
.main-container {
max-width: 940px;
margin-left: auto;
margin-right: auto;
}
img {
max-width:100%;
}
.tabbed-pane {
padding-top: 12px;
}
.html-widget {
margin-bottom: 20px;
}
button.code-folding-btn:focus {
outline: none;
}
summary {
display: list-item;
}
details > summary > p:only-child {
display: inline;
}
pre code {
padding: 0;
}
</style>
<style type="text/css">
.dropdown-submenu {
position: relative;
}
.dropdown-submenu>.dropdown-menu {
top: 0;
left: 100%;
margin-top: -6px;
margin-left: -1px;
border-radius: 0 6px 6px 6px;
}
.dropdown-submenu:hover>.dropdown-menu {
display: block;
}
.dropdown-submenu>a:after {
display: block;
content: " ";
float: right;
width: 0;
height: 0;
border-color: transparent;
border-style: solid;
border-width: 5px 0 5px 5px;
border-left-color: #cccccc;
margin-top: 5px;
margin-right: -10px;
}
.dropdown-submenu:hover>a:after {
border-left-color: #adb5bd;
}
.dropdown-submenu.pull-left {
float: none;
}
.dropdown-submenu.pull-left>.dropdown-menu {
left: -100%;
margin-left: 10px;
border-radius: 6px 0 6px 6px;
}
</style>
<script type="text/javascript">
// manage active state of menu based on current page
$(document).ready(function () {
// active menu anchor
href = window.location.pathname
href = href.substr(href.lastIndexOf('/') + 1)
if (href === "")
href = "index.html";
var menuAnchor = $('a[href="' + href + '"]');
// mark the anchor link active (and if it's in a dropdown, also mark that active)
var dropdown = menuAnchor.closest('li.dropdown');
if (window.bootstrap) { // Bootstrap 4+
menuAnchor.addClass('active');
dropdown.find('> .dropdown-toggle').addClass('active');
} else { // Bootstrap 3
menuAnchor.parent().addClass('active');
dropdown.addClass('active');
}
// Navbar adjustments
var navHeight = $(".navbar").first().height() + 15;
var style = document.createElement('style');
var pt = "padding-top: " + navHeight + "px; ";
var mt = "margin-top: -" + navHeight + "px; ";
var css = "";
// offset scroll position for anchor links (for fixed navbar)
for (var i = 1; i <= 6; i++) {
css += ".section h" + i + "{ " + pt + mt + "}\n";
}
style.innerHTML = "body {" + pt + "padding-bottom: 40px; }\n" + css;
document.head.appendChild(style);
});
</script>
<!-- tabsets -->
<style type="text/css">
.tabset-dropdown > .nav-tabs {
display: inline-table;
max-height: 500px;
min-height: 44px;
overflow-y: auto;
border: 1px solid #ddd;
border-radius: 4px;
}
.tabset-dropdown > .nav-tabs > li.active:before, .tabset-dropdown > .nav-tabs.nav-tabs-open:before {
content: "\e259";
font-family: 'Glyphicons Halflings';
display: inline-block;
padding: 10px;
border-right: 1px solid #ddd;
}
.tabset-dropdown > .nav-tabs.nav-tabs-open > li.active:before {
content: "\e258";
font-family: 'Glyphicons Halflings';
border: none;
}
.tabset-dropdown > .nav-tabs > li.active {
display: block;
}
.tabset-dropdown > .nav-tabs > li > a,
.tabset-dropdown > .nav-tabs > li > a:focus,
.tabset-dropdown > .nav-tabs > li > a:hover {
border: none;
display: inline-block;
border-radius: 4px;
background-color: transparent;
}
.tabset-dropdown > .nav-tabs.nav-tabs-open > li {
display: block;
float: none;
}
.tabset-dropdown > .nav-tabs > li {
display: none;
}
</style>
<!-- code folding -->
<style type="text/css">
#TOC {
margin: 25px 0px 20px 0px;
}
@media (max-width: 768px) {
#TOC {
position: relative;
width: 100%;
}
}
@media print {
.toc-content {
/* see https://github.com/w3c/csswg-drafts/issues/4434 */
float: right;
}
}
.toc-content {
padding-left: 30px;
padding-right: 40px;
}
div.main-container {
max-width: 1200px;
}
div.tocify {
width: 20%;
max-width: 260px;
max-height: 85%;
}
@media (min-width: 768px) and (max-width: 991px) {
div.tocify {
width: 25%;
}
}
@media (max-width: 767px) {
div.tocify {
width: 100%;
max-width: none;
}
}
.tocify ul, .tocify li {
line-height: 20px;
}
.tocify-subheader .tocify-item {
font-size: 0.90em;
}
.tocify .list-group-item {
border-radius: 0px;
}
</style>
</head>
<body>
<div class="container-fluid main-container">
<!-- setup 3col/9col grid for toc_float and main content -->
<div class="row">
<div class="col-xs-12 col-sm-4 col-md-3">
<div id="TOC" class="tocify">
</div>
</div>
<div class="toc-content col-xs-12 col-sm-8 col-md-9">
<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-bs-toggle="collapse" data-target="#navbar" data-bs-target="#navbar">
<span class="icon-bar"></span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<a class="navbar-brand" href="index.html">jAMM</a>
</div>
<div id="navbar" class="navbar-collapse collapse">
<ul class="nav navbar-nav">
<li>
<a href="index.html">Home</a>
</li>
<li>
<a href="glm.html">Docs</a>
</li>
<li>
<a href="examples.html">Examples</a>
</li>
<li>
<a href="rosetta.html">Rosetta store</a>
</li>
<li>
<a href="release_notes.html">Release notes</a>
</li>
</ul>
<ul class="nav navbar-nav navbar-right">
<li>
<a href="https://github.com/jamovi-amm/jamm">View on Github</a>
</li>
</ul>
</div><!--/.nav-collapse -->
</div><!--/.container -->
</div><!--/.navbar -->
<div id="header">
<h1 class="title toc-ignore">Rosetta store: conditional mediation</h1>
<h4 class="author">(Marcello Gallucci)</h4>
</div>
<p><span class="keywords"> <span class="keytitle"> keywords </span>
jamovi, SPSS, R, PROCESS, mediation </span></p>
<p><span class="version"> <span class="versiontitle"> jAMM version ≥
</span> 0.0.4 </span></p>
<div id="introduction" class="section level1">
<h1>Introduction</h1>
<p>Here you can find comparisons of results obtained in jamovi jAMM,
jamovi (jmv), pure R, and SPSS. When not explicitely discussed, the code
of different software is written with the aim of obtaing equivalent
results across software packages.</p>
</div>
<div id="example" class="section level1">
<h1>Example</h1>
<p>This example shows how to estimate a conditional (moderated)
mediation model with four variables in jAMM. Data come from <a
href="https://www.ncbi.nlm.nih.gov/pubmed/16393020">Muller, Judd,
Yzerbyt, 2005</a> and contain variables related to a social dilemma
experiment. The dataset can be downloaed <a
href="https://github.com/mcfanda/jamm/blob/master/data/coopmedmod.csv">here</a></p>
<p>A long version of the analyses in jAMM is presented in
<a href="glm_example2.html">jAMM: conditional mediation</a>.</p>
</div>
<div id="variables-and-model" class="section level1">
<h1>Variables and model</h1>
<p>There are four variables:</p>
<ul>
<li><code>prime</code>: a two-group experimental condition</li>
<li><code>EXP</code>: expectations about the other cooperation</li>
<li><code>SVO</code>: continuous measure of social value orientation
(higher levels mean more cooperative attitude)</li>
<li><code>BEH</code>: behavior, the amount of experimental tokens given
to the public good by the participant.</li>
</ul>
<p>The model we estimated is:</p>
<p><img src="examples/muller/moderator2.png" class="img-responsive" alt=""></p>
<p>Thus, * <code>prime</code>: is the independent variable (IV) *
<code>EXP</code>: is the mediator (ME) * <code>SVO</code>: is the
moderator (MO) * <code>BEH</code>: is the dependent variable (DV)</p>
<p>All continuous predictors are centered to their means.</p>
</div>
<div id="jamm" class="section level1">
<h1>jAMM</h1>
<p>Results are composed by 4 tables. The first table reports the
interaction involved in the mediation model, namely, the interaction
between the IV and the moderator ( <code>SVO</code> X
<code>prime</code>-><code>EXP</code>) in predicting the mediator, and
between the mediator and the moderator in predicting the DV (
<code>SVO</code> X <code>EXP</code>-><code>BEH</code>).</p>
<p><img src="examples/muller/interactions.png" class="img-responsive" alt=""></p>
<p>The other three tables report the indirect, direct, and total effect
of the IV on the DV at three different levels of the moderator. In this
example the three levels of the moderator were
<code>mean+1 SD</code>,<code>mean</code>,<code>mean-1 SD</code>.</p>
<p><img src="examples/muller/results0_b.png" class="img-responsive" alt=""></p>
<p>Confidence intervals where estimated with bootstrap percentile method
(keep in mind that jAMM bootstrap the full mediation model for each
level of the moderator, so the bootstrap can be quite slow).</p>
</div>
<div id="r-and-mediation-package" class="section level1">
<h1>R and mediation package</h1>
<p>First, we estimate the two model needed to obtain the model
parameters using <code>lm()</code></p>
<pre class="r"><code># I happened to have the spss version of the data
library(foreign)
data<-read.spss("../data/muller_mediation.sav",to.data.frame = T)
# set the contrast for dichotomous variable `prime`. Using contr.sum() the variable will be `centered`
# I use the "minus" before the contr.sum() such that the first group in prime is coded -1, like in jAMM.
data$cprime<-factor(data$prime)
contrasts(data$cprime)<--contr.sum(2)
# Mediator model
mod1<-lm(EXP~cprime*SVO,data=data)
summary(mod1)</code></pre>
<pre><code>##
## Call:
## lm(formula = EXP ~ cprime * SVO, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -22.9279 -5.5947 0.1731 4.8448 20.1030
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0004583 0.7549153 -0.001 0.999517
## cprime1 2.6923752 0.7549153 3.566 0.000566 ***
## SVO -0.0852214 0.5471027 -0.156 0.876543
## cprime1:SVO 0.0889286 0.5471027 0.163 0.871219
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.549 on 96 degrees of freedom
## Multiple R-squared: 0.1174, Adjusted R-squared: 0.08985
## F-statistic: 4.258 on 3 and 96 DF, p-value: 0.007201</code></pre>
<pre class="r"><code>mod2<-lm(BEH~cprime*SVO+EXP*SVO,data=data)
summary(mod2)</code></pre>
<pre><code>##
## Call:
## lm(formula = BEH ~ cprime * SVO + EXP * SVO, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.146 -8.200 1.764 6.070 24.325
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 58.30247 0.99771 58.436 < 2e-16 ***
## cprime1 2.16926 1.06691 2.033 0.044850 *
## SVO 2.56868 0.72554 3.540 0.000624 ***
## EXP 0.83976 0.13874 6.053 2.89e-08 ***
## cprime1:SVO 0.04145 0.78822 0.053 0.958178
## SVO:EXP 0.76472 0.09674 7.905 5.04e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.975 on 94 degrees of freedom
## Multiple R-squared: 0.5622, Adjusted R-squared: 0.539
## F-statistic: 24.15 on 5 and 94 DF, p-value: 1.497e-15</code></pre>
<pre class="r"><code>## average indirect effect
mod1$coefficients[2]*mod2$coefficients[4]</code></pre>
<pre><code>## cprime1
## 2.260951</code></pre>
<p>We can see that the estimates of the interactions are identical to
the ones obtained with jAMM (cf <code>Moderator effects</code> table in
jAMM). The standard errors and the z-tests are slightly different
because jAMM is based on <code>R lavaan package</code>, that uses the <a
href="http://lavaan.ugent.be/tutorial/est.html">expected information
matrix</a> to obtain the standard errors.</p>
<p>As regards the mediated effects, from the models we just estimated we
can compute the average mediated effect, because the IV and the MO
variables are centered to their means. We can use <a
href="https://cran.r-project.org/web/packages/mediation/mediation.pdf">R
package mediation</a>. Confidence intervals are computed with the
bootstrap percentile method. Because we have a moderation, we should
tell the <code>mediate()</code> command that we want the estimates of
mediated effect for SVO equal to its mean. We do that with the
<code>covariate</code> option.</p>
<pre class="r"><code>library(mediation, quietly = T)
nsim =1000 # number of bootstrap samples to draw
modValue<-mean(data$SVO)
med<-mediate(mod1,mod2,treat="cprime",mediator = "EXP",covariates=list("SVO"=modValue),sims = nsim,boot = TRUE ,boot.ci.type = "perc")</code></pre>
<pre><code>## Running nonparametric bootstrap</code></pre>
<pre class="r"><code>summary(med)</code></pre>
<pre><code>##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## (Inference Conditional on the Covariate Values Specified in `covariates')
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 4.522 2.087 7.56 <2e-16 ***
## ADE 4.339 -0.311 8.92 0.066 .
## Total Effect 8.860 4.119 13.44 <2e-16 ***
## Prop. Mediated 0.510 0.258 1.08 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 100
##
##
## Simulations: 1000</code></pre>
<p><code>mediation</code> package calls the indirect (mediated) effect
“ACME” and the direct effect “ADE”. We can compare the estimates with
the jAMM table
<code>Indirect and direct effects computed for SVO=mean</code>. We can
see that the mediated effect is not exactly equal to the one estimated
by jAMM. It is exctely twice as large. <span
class="math inline">\(4.5219/2=2.2609\)</span>. The reason is in the way
<code>mediation</code> package treats the categorical variable.
Nontheless, it is just a scaling issue, the remaing of the results are
perfectly in line.</p>
<p>Then, we can test the mediated effect at different levels of the
moderator SVO.</p>
<pre class="r"><code>## moderator at 1 SD below the average
modValue<-mean(data$SVO)-sd(data$SVO)
med<-mediate(mod1,mod2,treat="cprime",mediator = "EXP",covariates=list("SVO"=modValue),sims = nsim,boot = TRUE ,boot.ci.type = "perc")</code></pre>
<pre><code>## Running nonparametric bootstrap</code></pre>
<pre class="r"><code>summary(med)</code></pre>
<pre><code>##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## (Inference Conditional on the Covariate Values Specified in `covariates')
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME -1.178 -4.046 0.73 0.27
## ADE 4.223 -1.674 9.92 0.20
## Total Effect 3.045 -2.949 8.26 0.29
## Prop. Mediated -0.387 -4.492 3.50 0.50
##
## Sample Size Used: 100
##
##
## Simulations: 1000</code></pre>
<pre class="r"><code>## moderator at 1 SD above the average
modValue<-mean(data$SVO)+sd(data$SVO)
med<-mediate(mod1,mod2,treat="cprime",mediator = "EXP",covariates=list("SVO"=modValue),sims = nsim,boot = TRUE ,boot.ci.type = "perc")</code></pre>
<pre><code>## Running nonparametric bootstrap</code></pre>
<pre class="r"><code>summary(med)</code></pre>
<pre><code>##
## Causal Mediation Analysis
##
## Nonparametric Bootstrap Confidence Intervals with the Percentile Method
##
## (Inference Conditional on the Covariate Values Specified in `covariates')
##
## Estimate 95% CI Lower 95% CI Upper p-value
## ACME 10.753 3.353 19.13 0.004 **
## ADE 4.454 -1.824 10.57 0.162
## Total Effect 15.208 6.164 24.24 <2e-16 ***
## Prop. Mediated 0.707 0.367 1.18 0.004 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Sample Size Used: 100
##
##
## Simulations: 1000</code></pre>
</div>
<div id="spss-process" class="section level1">
<h1>SPSS PROCESS</h1>
<p>To compare jAMM results with <a
href="http://www.processmacro.org/download.html">PROCESS macro</a>
results, we should set up PROCESS to estimate <a
href="http://www.personal.psu.edu/jxb14/M554/specreg/templates.pdf">model
59</a>. In PROCESS, this model is in fact the one with the moderator
interacting with both the independent variable and the mediator. WE also
need to ask for simple mediated effects computed at the mean, one SD
plus, and one SD minus the mean (PROCESS 3 default is to condition the
effects at selected percentile values of the moderator).</p>
<p>After that, we obtain the following results. First we look at the
interaction table produced by PROCESS and compare it with jAMM
<code>Moderator effect</code> table.</p>
<p>Interaction in predicting the mediator <code>EXP</code>.
<img src="examples/muller/process1.png" class="img-responsive" alt="">
Interactions in predicting the dependent variable <code>BEH</code>.
<img src="examples/muller/process2.png" class="img-responsive" alt="">
PROCESS gives the F-test, but we can see that it is equivalent to the
(square of) z-test in jAMM, to the second significant digit.</p>
<p>Then we look at the simple mediated effects and direct effects. Also
here, we found a very close match.
<img src="examples/muller/process3.png" class="img-responsive" alt=""></p>
<h1>
Comments?
</h1>
<p>
Got comments, issues or spotted a bug? Please open an issue on
<a href=" https://github.com/mcfanda/gamlj/issues "> GAMLj at
github“</a> or <a href="mailto:[email protected]">send me an email</a>
</p>
</div>
</div>
</div>
</div>
<script>
// add bootstrap table styles to pandoc tables
function bootstrapStylePandocTables() {
$('tr.odd').parent('tbody').parent('table').addClass('table table-condensed');
}
$(document).ready(function () {
bootstrapStylePandocTables();
});
</script>
<!-- tabsets -->
<script>
$(document).ready(function () {
window.buildTabsets("TOC");
});
$(document).ready(function () {
$('.tabset-dropdown > .nav-tabs > li').click(function () {
$(this).parent().toggleClass('nav-tabs-open');
});
});
</script>
<!-- code folding -->
<script>
$(document).ready(function () {
// temporarily add toc-ignore selector to headers for the consistency with Pandoc
$('.unlisted.unnumbered').addClass('toc-ignore')
// move toc-ignore selectors from section div to header
$('div.section.toc-ignore')
.removeClass('toc-ignore')
.children('h1,h2,h3,h4,h5').addClass('toc-ignore');
// establish options
var options = {
selectors: "h1,h2,h3",
theme: "bootstrap3",
context: '.toc-content',
hashGenerator: function (text) {
return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_');
},
ignoreSelector: ".toc-ignore",
scrollTo: 0
};
options.showAndHide = true;
options.smoothScroll = true;
// tocify
var toc = $("#TOC").tocify(options).data("toc-tocify");
});
</script>
<!-- dynamically load mathjax for compatibility with self-contained -->
<script>
(function () {
var script = document.createElement("script");
script.type = "text/javascript";
script.src = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
document.getElementsByTagName("head")[0].appendChild(script);
})();
</script>
</body>
</html>