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@JoseAlanis
JoseAlanis committed Mar 17, 2024
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2 changes: 1 addition & 1 deletion about/about_MLM_sose23.html
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Expand Up @@ -306,7 +306,7 @@ <h1 class="title">Seminar <strong>Fortgeschrittene statistische Methoden II</str
<div>
<div class="quarto-title-meta-heading">Veröffentlichungsdatum</div>
<div class="quarto-title-meta-contents">
<p class="date">10 März 2024</p>
<p class="date">17 März 2024</p>
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</div>

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2 changes: 1 addition & 1 deletion about/about_cogmod.html
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Expand Up @@ -335,7 +335,7 @@ <h1 class="title">Seminar <strong>Fortgeschrittene statistische Methoden II</str
<div>
<div class="quarto-title-meta-heading">Veröffentlichungsdatum</div>
<div class="quarto-title-meta-contents">
<p class="date">10 März 2024</p>
<p class="date">17 März 2024</p>
</div>
</div>

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4 changes: 2 additions & 2 deletions index.html
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Expand Up @@ -362,7 +362,7 @@ <h1>Willkommen!</h1>
<h1>Aktuelle Seminare</h1>
<div id="listing-aktuell" class="quarto-listing quarto-listing-container-grid">
<div class="list grid quarto-listing-cols-3">
<div class="g-col-1" data-index="0" data-categories="SoSe 2023" data-listing-date-sort="NaN" data-listing-file-modified-sort="1710031107559" data-listing-date-modified-sort="NaN" data-listing-reading-time-sort="4" data-listing-word-count-sort="632">
<div class="g-col-1" data-index="0" data-categories="SoSe 2023" data-listing-date-sort="NaN" data-listing-file-modified-sort="1710635907394" data-listing-date-modified-sort="NaN" data-listing-reading-time-sort="4" data-listing-word-count-sort="632">
<a href="./about/about_MLM_sose23.html" class="quarto-grid-link">
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<p class="card-img-top">
Expand All @@ -384,7 +384,7 @@ <h5 class="no-anchor card-title listing-title" data-anchor-id="aktuelle-seminare
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</a>
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<div class="g-col-1" data-index="1" data-categories="WiSe 2023 / 2024" data-listing-date-sort="NaN" data-listing-file-modified-sort="1710031107559" data-listing-date-modified-sort="NaN" data-listing-reading-time-sort="16" data-listing-word-count-sort="3062">
<div class="g-col-1" data-index="1" data-categories="WiSe 2023 / 2024" data-listing-date-sort="NaN" data-listing-file-modified-sort="1710635907394" data-listing-date-modified-sort="NaN" data-listing-reading-time-sort="16" data-listing-word-count-sort="3062">
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6 changes: 3 additions & 3 deletions mlm_seminar_sose23/hierarchische_daten.html
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Expand Up @@ -304,7 +304,7 @@ <h1 class="title">Seminar <strong>Fortgeschrittene statistische Methoden II</str
<div>
<div class="quarto-title-meta-heading">Veröffentlichungsdatum</div>
<div class="quarto-title-meta-contents">
<p class="date">10 März 2024</p>
<p class="date">17 März 2024</p>
</div>
</div>

Expand Down Expand Up @@ -370,7 +370,7 @@ <h2 class="anchored" data-anchor-id="ihre-aufgabe">Ihre Aufgabe</h2>
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<div class="countdown" id="timer_c0724476" data-update-every="1" tabindex="0" style="right:0;bottom:0;position: relative; width: min-content;">
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<code class="countdown-time"><span class="countdown-digits minutes">20</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code>
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Expand All @@ -393,7 +393,7 @@ <h1>Konsequenzen hierarchischer Daten</h1>
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<code class="countdown-time"><span class="countdown-digits minutes">05</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code>
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2 changes: 1 addition & 1 deletion mlm_seminar_sose23/lineare_regression.html
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Expand Up @@ -372,7 +372,7 @@ <h1 class="title">Seminar <strong>Fortgeschrittene statistische Methoden II</str
<div>
<div class="quarto-title-meta-heading">Veröffentlichungsdatum</div>
<div class="quarto-title-meta-contents">
<p class="date">10 März 2024</p>
<p class="date">17 März 2024</p>
</div>
</div>

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6 changes: 3 additions & 3 deletions mlm_seminar_sose23/lineare_regression_parameters.html
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26 changes: 13 additions & 13 deletions scripts/mpt-workshop/mpt-workshop.html
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Expand Up @@ -584,9 +584,9 @@ <h2 data-number="5" class="anchored" data-anchor-id="fit-the-model"><span class=
<pre><code>Presenting the best result out of 10 minimization runs.</code></pre>
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<div class="cell-output cell-output-stdout">
<pre><code>[1] "Model fitting begins at 2024-03-10 00:43:11.501465"
[1] "Model fitting stopped at 2024-03-10 00:43:15.851589"
Time difference of 4.350124 secs</code></pre>
<pre><code>[1] "Model fitting begins at 2024-03-17 00:43:56.328691"
[1] "Model fitting stopped at 2024-03-17 00:44:00.682752"
Time difference of 4.354061 secs</code></pre>
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<p>Nun können verschiedene Informationen mit Hilfe des <code>$</code> Operator aus dem <code>fit</code> Objekt ausgegeben werden, hierzu benutzen wir den <code>$</code> Operator. Zunächst sehen wir uns den Mittelwert der unterschiedlichen individuellen Parameter nach Gruppen/Bäumen an, sowie die Parameter für die aggregierten Daten (also für die aufsummierten Hits und Misses über alle Personen):<br>
Expand Down Expand Up @@ -714,25 +714,25 @@ <h2 data-number="7" class="anchored" data-anchor-id="modelfit-und-identifizierba
1 -34.12942 0.000000e+00 0 1
2 -24.34660 -7.105427e-15 0 1
3 -16.25804 -7.105427e-15 0 1
4 -17.59374 -7.105427e-15 0 1
4 -17.59374 -1.421085e-14 0 1
5 -34.75840 0.000000e+00 0 1
6 -25.31044 -7.105427e-15 0 1
7 -20.27958 -7.105427e-15 0 1
8 -39.81650 0.000000e+00 0 1
7 -20.27958 0.000000e+00 0 1
8 -39.81650 -1.421085e-14 0 1
9 -22.50232 -7.105427e-15 0 1
10 -30.00681 0.000000e+00 0 1</code></pre>
10 -30.00681 -7.105427e-15 0 1</code></pre>
</div>
<div class="sourceCode cell-code" id="cb27"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Summierter Fit</span></span>
<span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>fit<span class="sc">$</span>goodness.of.fit<span class="sc">$</span>sum</span></code><button title="In die Zwischenablage kopieren" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code> Log.Likelihood G.Squared df p.value
1 -3528.429 -4.902745e-13 0 1</code></pre>
1 -3528.429 -5.506706e-13 0 1</code></pre>
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<div class="sourceCode cell-code" id="cb29"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Gesamtmodelfit (wird immer im Text angegben)</span></span>
<span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a><span class="fu">print</span>(model_fit)</span></code><button title="In die Zwischenablage kopieren" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<pre><code> Log.Likelihood G.Squared df p.value
1 -3895.856 -9.094947e-13 0 1</code></pre>
<pre><code> Log.Likelihood G.Squared df p.value
1 -3895.856 0 0 1</code></pre>
</div>
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<p>Die für den Modelfit wichtige Statistik ist die G²-Statistik. G² wird in der Schätzung der MPT Modelle als Diskrepanzfunktion minimiert und bildet das Äquivalent zur Maximierung der Likelihood-Funktion bei kontinuierlichen Daten:</p>
Expand All @@ -746,8 +746,8 @@ <h2 data-number="7" class="anchored" data-anchor-id="modelfit-und-identifizierba
<div class="cell">
<div class="sourceCode cell-code" id="cb31"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a>model_fit</span></code><button title="In die Zwischenablage kopieren" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code> Log.Likelihood G.Squared df p.value
1 -3895.856 -9.094947e-13 0 1</code></pre>
<pre><code> Log.Likelihood G.Squared df p.value
1 -3895.856 0 0 1</code></pre>
</div>
</div>
<p>Dies bedeutet das Modell fittet perfekt, was an der eindeutigen Identifizierung des Modells liegt, da</p>
Expand Down Expand Up @@ -807,7 +807,7 @@ <h2 data-number="8" class="anchored" data-anchor-id="grafische-darstellung-des-m
[3,] -0.03338640 -0.1754023 1.00000000 -0.26177232 -0.04594584 -0.2079016
[4,] -0.08907613 0.2897716 -0.26177232 1.00000000 -0.04378506 0.4128029
[5,] 0.38012618 -0.2585228 -0.04594584 -0.04378506 1.00000000 -0.2525995
[6,] -0.21029684 0.4867141 -0.20790165 0.41280286 -0.25259949 1.0000000
[6,] -0.21029684 0.4867141 -0.20790165 0.41280286 -0.25259950 1.0000000
[7,] 0.09289815 -0.2397399 0.03873533 -0.19819066 0.07890132 -0.3150341
[8,] -0.24401067 0.4790734 -0.38629078 0.17119394 -0.21022623 0.5079492
hits_bg miss_bg
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