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(This article was first published on <strong><a href="https://www.r-exercises.com/2018/07/05/basic-generalised-additive-model-in-ecology-exercise/"> R-exercises</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>Generalised Additive Models (GAM) are non-parametric models that add smoother to the data. On this exercise, we will look at GAMs using cubic spline using the <code>mgcv</code> package. Dataset used can be downloaded <a rel="nofollow" href="https://www.dropbox.com/s/lann9lthjcftm74/Vegetation2.csv?dl=0" target="_blank">here</a>. The dataset is the experiment result at grassland richness over time in Yellowstone National Park (Skkink et al. 2007).<br>
Answers to these exercises are available <a rel="nofollow" href="https://www.r-bloggers.com/www.r-exercises.com/2018/07/05/basic-generalised-additive-model-in-ecology-solutions/" target="_blank">here</a>. If you obtained a different (correct) answer than those listed on the solutions page, please feel free to post your answer as a comment on that page. Load dataset and required package before running the exercise.</p>
<p><strong>Exercise 1</strong><br>
observe the dataset and try to classify the response and explanatory variables. We will focus on ROCK as an explanatory variable. </p>
<p><strong>Exercise 2</strong><br>
do some scatter plot</p>
<p><strong>Exercise 3</strong><br>
since it is not linear, try to do GAM with ROCK variables</p>
<p><strong>Exercise 4</strong><br>
check the result. what can be inferred</p>
<p><strong>Exercise 5</strong><br>
do some validation plots</p>
<p><strong>Exercise 6</strong><br>
plot base graph</p>
<p><strong>Exercise 7</strong><br>
add predict across the data and add some lines</p>
<p><strong>Exercise 8</strong><br>
plot the fitted values </p>
<p>Why we only use ROCK variables? Because it is proofed to give the most fitted data without incorporation all the explanatory variables. Try to play around with other explanatory variables to see the difference. </p>
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