Abstract
When the data under investigation do not show a clear linear relationship, then additive modelling is a suitable alternative to linear regression. Figure 7.1 shows a scatterplot for two variables of the RIKZ data: species richness and grain size. See Chapter 27 for details on these data. A first look at the graph suggests there is a non-linear relationship between richness and grain size. Sites with large grain sizes seem to have a fairly constant but low species richness, with richness increasing as grain size decreases. Applying a linear regression model with richness as the response variable and grain size as the explanatory variable gives residuals showing a clear pattern, indicating a serious model misspecification.
Keywords
- Explanatory Variable
- Linear Regression Model
- Generalise Additive Modelling
- Smoothing Function
- Smoothing Spline
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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(2007). Additive and generalised additive modelling. In: Analysing Ecological Data. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/978-0-387-45972-1_7
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DOI: https://doi.org/10.1007/978-0-387-45972-1_7
Publisher Name: Springer, New York, NY
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