Use Lowess models to fit smooth surfaces to your data. The names “lowess” and “loess” are derived from the term “locally weighted scatter plot smooth,” as both methods use locally weighted linear regression to smooth data. The process is weighted because the toolbox defines a regression weight function for the data points contained within the span. In addition to the regression weight function, the Robust option is a weight function that can make the process resistant to outliers.
For more information on these two types of smoothing fit, see Local Regression Smoothing.
In the Curve Fitting app, select
the model type list.
You can use the
Lowess model type
to fit smooth surfaces to your data with either
Lowess fits use locally weighted linear
regression to smooth data.
You can specify the following options:
the list to specify the type of Polynomial model
to use in the regression. In Curve Fitting Toolbox™,
uses a linear polynomial, while
uses a quadratic polynomial.
Use Span to specify the span as a percentage of the total number of data points in the data set. The toolbox uses neighboring data points defined within the span to determine each smoothed value. This role of neighboring points is the reason why the smoothing process is called “local.”
Increase the span to make the surface smoother. Reduce the span to make the surface follow the data more closely.
The Robust linear least-squares
fitting method you want to use (
Bisquare). The local regression uses the Robust option.
Using the Robust weight function can make the
process resistant to outliers. For details, see
fitoptions reference page.
If your input variables have very different scales, turn the Center and scale option on and off to see the difference in the surface fit. Normalizing the inputs can strongly influence the results of a Lowess fitting.
For an interactive example using Lowess, see Surface Fitting to Franke Data.
This example shows how to use the
fit function to fit a Lowess model to data.
Load some data and fit a Lowess model by specifying
'lowess' when calling the fit function.
load franke f = fit([x y],z,'lowess')
Locally weighted smoothing linear regression: f(x,y) = lowess (linear) smoothing regression computed from p Coefficients: p = coefficient structure
For a command-line example using Lowess, see Fit Smooth Surfaces To Investigate Fuel Efficiency.