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GLM_2_Optional_Dolphins_extension.html
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GLM_2_Optional_Dolphins_extension.html
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<h1 class="title toc-ignore">Exercise</h1>
</div>
<p> </p>
<div
id="optional-extension-to-binomial-bernoulli-glm---dolphin-behavioural-plasticity"
class="section level1">
<h1>Optional extension to Binomial (Bernoulli) GLM - dolphin behavioural
plasticity</h1>
<p> </p>
<p>This extension to the dolphin data analysis is for those who may be a
bit ahead and would like to :</p>
<ul>
<li>get some experience dealing with interactions between categorical
predictors (valid with LMs or GLMs)</li>
<li>test your understanding of the binomial GLMs for the analysis of
Bernoulli data, by repeating the approach of the previous exercise with
a slightly more advanced model:
<ul>
<li>model specification</li>
<li>model validation</li>
<li>interpreting a model graphically</li>
</ul></li>
<li>get more experience with model selection in GLMs (using AIC)</li>
</ul>
<p>I recommend you continue with your previous R script for the GLM_2
exercise, in your RStudio Project.</p>
<p> </p>
<p>13. To address the limitations of the previous analysis, fit a new
model with categorical predictors only, and interactions between them,
two by two:
<code>fTide4 + fMonth + fTime6 + fTide4:fMonth + fTide4:fTime6 + fMonth:fTime6</code>.
What hypotheses do these interactions correspond to?</p>
<p> </p>
<p> </p>
<p>14. Perform model selection “by hand” using the AIC, and construct an
AIC table, using the example in the lecture. “By hand” means without
using <code>drop1</code> or <code>step</code> functions. You can get the
AIC value from the model summary <code>summary(YourModel)</code> or by
typing <code>AIC(YourModel)</code>. There are 18 possible models in
total, including the full model above. You can choose to evaluate all 18
models in a completely exploratory (the “ignorant and brave”) approach,
or only a selection of models based on more specific research questions
or predictions of your own (the “clear thinker” approach).</p>
<ul>
<li>Example: <em>“We know/expect that dolphins are affected by A, B and
C, but I suspect that the effect of B varies depending on the value of A
whereas my colleague thinks that the effect of B depends on the value of
C”</em>, which would suggest comparing the following models to find out
which hypothesis is best supported by the data:
<ul>
<li>A + B + C + A:B</li>
<li>A + B + C + B:C</li>
<li>A + B + C</li>
</ul></li>
</ul>
<p> </p>
<p> </p>
<p>15. Do the validation of your best model, using the approach taken in
question 7.</p>
<p> </p>
<p> </p>
<p>16. Interpret the model, using plots of the predictions (use the
approach taken in question 9) .</p>
<p> </p>
<p> </p>
<p><strong>End of the optional extension to Binomial (Bernoulli) GLM
exercise</strong></p>
<p> </p>
<p><strong>For info,</strong> the publication here offers a different
approach to analysing these data, using slightly fancier GLMs with
smooth terms (called GAMs, for Generalized Additive Models), and a few
additional refinements: [<a
href="https://www.nature.com/articles/s41598-019-38900-4"
class="uri">https://www.nature.com/articles/s41598-019-38900-4</a>].
What assumptions differ between this and your approach?</p>
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