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Biodiversity data analysis with R






L08: Local regression model

“Well, here goes nothing.”

Dr. Lora Baines, Tron

Things we cover in this session

  • Local regression models for non-linear fitting between two samples
  • Predicting variable values using simple local regression models

Things you need for this session

Things to take home from this session

At the end of this session you should be able to

  • compute a local regression between two variables
  • predict values based on local regression models

Local regression models

While the linear regression models assume a linear relationship between a dependent (e.g. y) and one ore more independent variables (e.g. x), non-linear models do not have this restriction. As a drawback, non-linear models can be quite complicated to define if one is looking for a non-linear model which describes the entire data set but local regression models do not have this drawback.

Non-parametric local regression models (loess) use simple linear or quadratic models (i.e. polynomial functions of first or second degree) which are used for a local weighted fit on the data set using a moving window. For example, if the span of this window is set to 7, then only the neighboring 3 lower and higher values are considered for the fit of the central value. A cubic (i.e. x3) weighing function ensures that the actual central value has the largest influence on the fit with decreasing weights towards the end of each sides span.

Regarding validation of such models, a look at its residual standard error gives you a first idea. However, to get a better idea of the reliability of the model when it comes to prediction, the leave-one-out approach from L06 is a better starting point.

Time for practice

Note on data used for illustrating analysis The analysis used for illustration on this site are based on data from a field survey of areas in the Fogo natural park in 2007 by K. Mauer. For more information, please refer to this report.

en/learning/schools/s01/lecture-notes/ba-ln-08.txt · Last modified: 2017/10/30 10:22 by aziegler