The data for figure 1 are generated by fitting models with the change point successively incremented from -0.5 to 1.0, probably by a step size of 0.01, and keeping the RSS in a dataset for future plotting vs. change point values. At each possible change point (-0.50, -0.49, -0.48...0.98, 0.99, 1.00) the model is fit and a single residual sum of squares is obtained. Since it appears that the model is linear with normal residuals, this is the same as plotting the residual standard deviation against the change point. It is a lot of work for one graph, and a maximum likelihood optimizer (as in NLIN or NLMIXED) should find a similar value (unless the starting value was around 0.66 where there is a local minimum).
SteveDenham
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