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2 changes: 1 addition & 1 deletion man/gamlss2.html
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Expand Up @@ -629,7 +629,7 @@ <h3 class="anchored" data-anchor-id="examples">Examples</h3>
*--------
n = 610 df = 13.12 res.df = 596.88
Deviance = 4770.1554 Null Dev. Red. = 33.39%
AIC = 4796.3966 elapsed = 0.77sec</code></pre>
AIC = 4796.3966 elapsed = 0.79sec</code></pre>
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<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="do">## plot estimated effects</span></span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(b, <span class="at">which =</span> <span class="st">"effects"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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8 changes: 4 additions & 4 deletions man/prodist.gamlss2.html
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Expand Up @@ -537,10 +537,10 @@ <h3 class="anchored" data-anchor-id="examples">Examples</h3>
<div class="sourceCode cell-code" id="cb16"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="do">## simulate random numbers</span></span>
<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a><span class="fu">random</span>(d, <span class="dv">5</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
<div class="cell-output cell-output-stdout">
<pre><code> r_1 r_2 r_3 r_4 r_5
1 29.15083 28.25206 37.54823 22.39143 19.20124
2 79.74554 43.36832 71.38394 32.39768 52.17705
3 78.58056 169.09076 172.99766 106.80344 82.23454</code></pre>
<pre><code> r_1 r_2 r_3 r_4 r_5
1 40.6677 26.10823 10.90194 18.92221 14.46748
2 74.9387 57.83739 53.99314 58.34789 25.23325
3 113.9330 86.63150 131.73381 135.57277 143.28351</code></pre>
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<div class="sourceCode cell-code" id="cb18"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="do">## density and distribution</span></span>
<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a><span class="fu">pdf</span>(d, <span class="dv">50</span> <span class="sc">*</span> <span class="sc">-</span><span class="dv">2</span><span class="sc">:</span><span class="dv">2</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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12 changes: 6 additions & 6 deletions search.json
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26 changes: 16 additions & 10 deletions vignettes/s_pb.html
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Expand Up @@ -393,26 +393,32 @@ <h1 class="title display-7">Smooth Terms using s() and pb()</h1>


<div id="quarto-appendix" class="default"><section class="quarto-appendix-contents" role="doc-bibliography" id="quarto-bibliography"><h2 class="anchored quarto-appendix-heading">References</h2><div id="refs" class="references csl-bib-body hanging-indent" data-entry-spacing="0" role="list">
<div id="ref-CarterDavis2015" class="csl-entry" role="listitem">
Davis, Carter. 2015. <em>Sgt: Skewed Generalized t Distribution Tree</em>. <a href="https://CRAN.R-project.org/package=sgt">https://CRAN.R-project.org/package=sgt</a>.
</div>
<div id="ref-eilers2021practical" class="csl-entry" role="listitem">
Eilers, P. H. C., and B. D. Marx. 2021. <em>Practical Smoothing: The Joys of p-Splines</em>. Cambridge University Press.
</div>
<div id="ref-Fahrmeir+Kneib+Lang+Marx:2013" class="csl-entry" role="listitem">
Fahrmeir, Ludwig, Thomas Kneib, Stefan Lang, and Brian Marx. 2021. <em>Regression – Models, Methods and Applications</em>. 2nd ed. Berlin: Springer-Verlag. <a href="https://doi.org/10.1007/978-3-662-63882-8">https://doi.org/10.1007/978-3-662-63882-8</a>.
</div>
<div id="ref-Hofneretal2014" class="csl-entry" role="listitem">
Hofner, B., A. Mayr, N. Robinzonov, and M. Schmid. 2014. <span>“Model-Based Boosting in r: A Hands-on Tutorial Using the r Package Mboost.”</span> <em>Computational Statistics</em> 29: 3–35.
Hofner, B., A. Mayr, N. Robinzonov, and M. Schmid. 2014. <span>“Model-Based Boosting in <span>R</span>: A Hands-on Tutorial Using the r Package Mboost.”</span> <em>Computational Statistics</em> 29: 3–35. <a href="https://doi.org/10.1007/s00180-012-0382-5">https://doi.org/10.1007/s00180-012-0382-5</a>.
</div>
<div id="ref-hofner2015" class="csl-entry" role="listitem">
Hofner, B., A. Mayr, and M. Schmid. 2016. <span>“<span class="nocase">gamboostLSS</span>: An <span>R</span> Package for Model Building and Variable Selection in the <span>GAMLSS</span> Framework.”</span> <em>Journal of Statistical Software</em> 74 (1): 1–31.
Hofner, B., A. Mayr, and M. Schmid. 2016. <span>“<span class="nocase">gamboostLSS</span>: An <span>R</span> Package for Model Building and Variable Selection in the <span>GAMLSS</span> Framework.”</span> <em>Journal of Statistical Software</em> 74 (1): 1–31. <a href="https://doi.org/10.18637/jss.v074.i01">https://doi.org/10.18637/jss.v074.i01</a>.
</div>
<div id="ref-KleiberZeileis2016" class="csl-entry" role="listitem">
Kleiber, C., and A. Zeileis. 2016. <span>“Visualizing Count Data Regressions Using Rootograms.”</span> <em>The American Statistician</em> 70 (3): 296–303.
Kleiber, C., and A. Zeileis. 2016. <span>“Visualizing Count Data Regressions Using Rootograms.”</span> <em>The American Statistician</em> 70 (3): 296–303. <a href="https://doi.org/10.1080/00031305.2016.1173590">https://doi.org/10.1080/00031305.2016.1173590</a>.
</div>
<div id="ref-Lang+Umlauf+Wechselberger+Harttgen+Kneib:2012" class="csl-entry" role="listitem">
Lang, Stefan, Nikolaus Umlauf, Peter Wechselberger, Kenneth Harttgen, and Thomas Kneib. 2012. <span>“Multilevel Structured Additive Regression.”</span> <em>Statistics and Computing</em> 24 (2): 223–38. <a href="https://doi.org/10.1007/s11222-012-9366-0">https://doi.org/10.1007/s11222-012-9366-0</a>.
</div>
<div id="ref-Mayretal2012" class="csl-entry" role="listitem">
Mayr, A., N. Fenske, B. Hofner, T. Kneib, and M. Schmid. 2012. <span>“Generalized Additive Models for Location, Scale and Shape for High Dimensional Data, a Flexible Approach Based on Boosting.”</span> <em>J. R. Statist. Soc. Series C</em> 61: 403–27.
Mayr, A., N. Fenske, B. Hofner, T. Kneib, and M. Schmid. 2012. <span>“Generalized Additive Models for Location, Scale and Shape for High Dimensional Data, a Flexible Approach Based on Boosting.”</span> <em>J. R. Statist. Soc. Series C</em> 61: 403–27. <a href="https://doi.org/10.1111/j.1467-9876.2011.01033">https://doi.org/10.1111/j.1467-9876.2011.01033</a>.
</div>
<div id="ref-R:Pinheiro+Bates:2000" class="csl-entry" role="listitem">
Pinheiro, J. C., and D. M. Bates. 2000. <em>Mixed-Effects Models in <span>S</span> and <span>S-Plus</span></em>. Springer. <a href="https://link.springer.com/book/10.1007/b98882">https://link.springer.com/book/10.1007/b98882</a>.
</div>
<div id="ref-Rigby+Stasinopoulos:2005" class="csl-entry" role="listitem">
Rigby, R. A., and D. M. Stasinopoulos. 2005. <span>“Generalized Additive Models for Location, Scale and Shape.”</span> <em>Journal of the Royal Statistical Society C</em> 54 (3): 507–54. <a href="https://doi.org/10.1111/j.1467-9876.2005.00510.x">https://doi.org/10.1111/j.1467-9876.2005.00510.x</a>.
Expand All @@ -424,19 +430,19 @@ <h1 class="title display-7">Smooth Terms using s() and pb()</h1>
Stadlmann, S. 2019. <em>Distreg.vis: Framework for the Visualization of Distributional Regression Models</em>. <a href="https://CRAN.R-project.org/package=distreg.vis">https://CRAN.R-project.org/package=distreg.vis</a>.
</div>
<div id="ref-Stasinopoulos2018" class="csl-entry" role="listitem">
Stasinopoulos, D. M., R. A. Rigby, and F. De Bastiani. 2018. <span>“<span>GAMLSS</span>: A Distributional Regression Approach.”</span> <em>Statistical Modelling</em> 18 (3-4): 248–73.
Stasinopoulos, D. M., R. A. Rigby, and F. De Bastiani. 2018. <span>“<span>GAMLSS</span>: A Distributional Regression Approach.”</span> <em>Statistical Modelling</em> 18 (3-4): 248–73. <a href="https://doi.org/10.1177/1471082X18759144">https://doi.org/10.1177/1471082X18759144</a>.
</div>
<div id="ref-Stasinopoulosetal2021" class="csl-entry" role="listitem">
Stasinopoulos, D. M., R. A. Rigby, N. Giorgikopoulos, and F. De Bastiani. 2022. <span>“Principal Component Regression in GAMLSS Applied to Greek-German Government Bond Yield Spreads.”</span> <em>Statistical Modelling</em> 22 (1-2): 127–45. <a href="https://doi.org/10.1177/1471082x211022980">https://doi.org/10.1177/1471082x211022980</a>.
</div>
<div id="ref-Stasinopoulosetal2017" class="csl-entry" role="listitem">
Stasinopoulos, D. M., R. A. Rigby, G. Z. Heller, V. Voudouris, and F. De Bastiani. 2017. <em>Flexible Regression and Smoothing: Using GAMLSS in r</em>. Boca Raton: Chapman &amp; Hall/CRC.
</div>
<div id="ref-stasinopoulos2023p" class="csl-entry" role="listitem">
Stasinopoulos, Dimitrios M, Robert A Rigby, Gillian Z Heller, and Fernanda De Bastiani. 2023. <span>“P-Splines and GAMLSS: A Powerful Combination, with an Application to Zero-Adjusted Distributions.”</span> <em>Statistical Modelling</em> 23 (5-6): 510–24.
Stasinopoulos, D. M., R. A. Rigby, G. Z. Heller, and F. De Bastiani. 2023. <span>“P-Splines and GAMLSS: A Powerful Combination, with an Application to Zero-Adjusted Distributions.”</span> <em>Statistical Modelling</em> 23 (5-6): 510–24. <a href="https://doi.org/10.1177/1471082X231176635">https://doi.org/10.1177/1471082X231176635</a>.
</div>
<div id="ref-Stasinopoulosetal2017" class="csl-entry" role="listitem">
Stasinopoulos, D. M., R. A. Rigby, G. Z. Heller, V. Voudouris, and F. De Bastiani. 2017. <em>Flexible Regression and Smoothing: Using <span>GAMLSS</span> in <span>R</span></em>. Boca Raton: Chapman &amp; Hall/CRC. <a href="https://doi.org/10.1201/b21973">https://doi.org/10.1201/b21973</a>.
</div>
<div id="ref-stasinopoulos2024generalized" class="csl-entry" role="listitem">
Stasinopoulos, M. D., T. Kneib, N. Klein, A. Mayr, and G. Z Heller. 2024. <em>Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications</em>. Vol. 56. Cambridge University Press.
Stasinopoulos, M. D., T. Kneib, N. Klein, A. Mayr, and G. Z Heller. 2024. <em>Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications</em>. Vol. 56. Cambridge University Press. <a href="https://doi.org/10.1017/9781009410076">https://doi.org/10.1017/9781009410076</a>.
</div>
<div id="ref-bamlss2018" class="csl-entry" role="listitem">
Umlauf, N., N. Klein, and A. Zeileis. 2018. <span>“<span>BAMLSS</span>: <span>B</span>ayesian Additive Models for Location, Scale and Shape (and Beyond).”</span> <em>Journal of Computational and Graphical Statistics</em> 27 (3): 612–27. <a href="https://doi.org/10.1080/10618600.2017.1407325">https://doi.org/10.1080/10618600.2017.1407325</a>.
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Expand Up @@ -595,7 +595,7 @@ <h2 data-number="5" class="anchored" data-anchor-id="estimating-the-model"><span
*--------
n = 3494 df = 98.97 res.df = 3395.03
Deviance = 16484.181 Null Dev. Red. = 14.46%
AIC = 16682.1271 elapsed = 4.35sec</code></pre>
AIC = 16682.1271 elapsed = 4.46sec</code></pre>
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<p>Model calibration is checked using histogram, Q-Q plot, wormplot etc.</p>
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