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<div>This is not the mailing list you are looking for. </div>
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<div>Datatable-help is for help about datatable. </div>
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<div>That being said</div>
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<span style="background-color: rgba(255, 255, 255, 0);"><b>Ngo, L. and Wand, M.P. (2004).</b><br>
Smoothing with Mixed Model Software.<br>
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<div><span style="background-color: rgba(255, 255, 255, 0);">Volume 9, 2004, Issue 1 of <i>Journal of Statistical Software</i>, 1-54. </span></div>
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<div><span style="background-color: rgba(255, 255, 255, 0);">Provide a great walk through on how additive models can be fitted using mixed models and random effects. </span></div>
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<div><span style="background-color: rgba(255, 255, 255, 0);"> </span></div>
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On 12 Feb 2016, at 12:57 PM, Julian Chen <<a href="mailto:power.julian.chen@gmail.com">power.julian.chen@gmail.com</a>> wrote:<br>
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<div><span>I am now trying to use random effects in GAMs developed by Professor Simon</span><br>
<span>Wood. Prof Wood uses s(...,bs="re") to account for the random effects.</span><br>
<span>Random intercepts models or random slopes models are two different types of</span><br>
<span>mixed linear models or general random effects model (Cameron and Trivedi,</span><br>
<span>2005). I wonder if the Wood's method includes both random intercepts and</span><br>
<span>random slopes. Based on my understanding, this method does? Anyone can help</span><br>
<span>me clarify this method? The following is the link of Random effects in GAM</span><br>
<span>developed by Prof Wood.</span><br>
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<span><a href="https://stat.ethz.ch/R-manual/R-devel/library/mgcv/html/random.effects.html">https://stat.ethz.ch/R-manual/R-devel/library/mgcv/html/random.effects.html</a></span><br>
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<span>--</span><br>
<span>View this message in context: <a href="http://r.789695.n4.nabble.com/Random-effects-in-GAMs-tp4717371.html">
http://r.789695.n4.nabble.com/Random-effects-in-GAMs-tp4717371.html</a></span><br>
<span>Sent from the datatable-help mailing list archive at <a href="http://nabble.com">
Nabble.com</a>.</span><br>
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