The SegReg computer program (model) is designed to perform a segmented (piecewise) linear
regression (in splines) of one dependent variable (Y, e.g. plant growth, crop yield) on
one (X) or two (X and Z) independent (explanatory) variables (predictors), e.g. crop
growth factors like depth of water table and soil salinity.
The segmentation is done by introducing a breakpoint
(break-point, threshold, switching point). Thus one can obtain a broken, discontinuous, line.
Seven types of functions (0 to 6) are used. Examples are given.
The selection of the best function type and breakpoint is based on
maximizing the statistical coefficient of explanation (determination) and
performing the test of significance.
The 90% confidence interval (belt) is given as well as an Anova table for variance analysis.
In December 2008, an amplified version (SegRegA) was made permitting the use of weight factors, preferred
regression type or type exclusion. Although it can lead to manipulation, it is available on request.
More details are found in the program itself.
The mathematical model starts clicking on SegReg.Exe.
A paper on the statistical principles of segmented regression with break-point,
including the determination of its confidence interval, can be inspected
The construction of confidence intervals of the regression segments separated by the breakpoint, and of the breakpoint itself, is described in this confidence paper, which also gives an example.
The intervals are made with Student's t-distribution, see the
The principles of regression analysis in general can be found in this
Furter, the analysis of variance (Anova) and the F-test for segmented linear regression
with break-point, as used in SegReg, is briefly discussed in
A lecture note on statistical analysis with examples of SegReg applications is found
in a document called Data Analysis.
In September 2010, the SegReg program was provided with new functionalities thanks to a request by
In March 2011 the confidence belts were improved thanks to questions raised by Linda Jung.
In October 2012 the confidence block of the breakpoint for type 2 functions was improved thanks to
questions raised by John Schukman.
In March 2013 the use of a second independent variable was updated thanks to comments made by Barbara Mahler.
In November 2013 the calculation of the standard error and confidence interval of the breakpoint (BP), as well as of the Y value at BP, was standardized for the different types of segmented regression. A description of the mathematics involved, with examples, can be seen in this confidence paper. These changes
were motivated by suggestions put forward by Dawn Noren and Wenhuai Li.
In January 2014 the conditions for Type 2, 3, 4 and 5 were made more strict thanks to an example provided by John Shukman.
reports & cases
reports & cases