Curve Fitting Toolbox

Sectioning

Sectioning involves specifying a range of response data or a range of predictor data to exclude. You might want to section a data set because different parts of the data set are described by different models or many contiguous data points are corrupted by noise, large systematic errors, and so on.

Sectioning data with the Curve Fitting Toolbox follows these rules:

• If you are only sectioning data and not excluding individual data points, then you can create an exclusion rule without specifying a data set name.

• Note that you can associate the exclusion rule with any data set provided that the range or domain of the exclusion rule overlaps with the range or domain of the data set. This is useful if you have multiple data sets from which you want to exclude data points using the same range or domain.

• Using the Exclude GUI, you specify a range or domain of data to include. The excluded data lies outside this specification.

• Additionally, you can specify only a single range, domain, or box (range and domain) of included data points. Therefore, at most, you can define two vertical strips, two horizontal strips, or a border of excluded data. Refer to Example: Excluding and Sectioning Data for an example.

  To exclude multiple sections of data, you can use the excludedata function from the MATLAB command line.

Two examples of sectioning by domain are shown below for generated data.

Plot (a) shows the data set sectioned by fit type. The left section is fit with a linear polynomial, while the right section is fit with a cubic polynomial. Plot (b) shows the data set sectioned by fit type and by valid data. Here, the rightmost section is not part of any fit because the data is corrupted by noise. Note that reproducing these plots using the toolbox is a multistep process. For example, to reproduce plot (a), the steps are

1. Create an exclusion rule by sectioning the predictor data such that the data points described by the linear polynomial are excluded.
2. Create a different exclusion rule by sectioning the predictor data such that the data points described by the cubic polynomial are excluded.
3. Fit the data twice (once for each exclusion rule) using the appropriate model.

Marking Outliers

Example: Excluding and Sectioning Data