Charles Jekel

Here is the commit that added the changes: https://github.com/cjekel/piecewise_linear_fit_py/commit/ef58aaa93d1f07f643456bfdbc5f523ab751ee47

@cyriltw You'll need to install that experimental branch first. ``` git clone [email protected]:cjekel/piecewise_linear_fit_py.git cd piecewise_linear_fit_py git checkout mixed_degree pip install . --force-reinstall --no-deps ```

I haven't seen this before. It's possible something I use from scipy has changed. Can you post or send me a full stack trace of the error? Just so I...

Can you tell me which version of python and scipy are you using? On Thu, Apr 14, 2022, 07:23 Charles Jekel ***@***.***> wrote: > I haven't seen this before. It's...

What do you think about this approach in your case? First perform an initial fit, to all of your datasets (combined as one single large dataset) to find the break...

Do you know where the breakpoints should be ahead of time, or do you need to find them first? I'll code an example shortly of what I'm thinking.

This jupyter notebook describes what I was thinking https://github.com/cjekel/piecewise_linear_fit_py/blob/master/examples/experiment_with_batch_process.py.ipynb

@vkhodygo Sorry for the delay. Since the breakpoints may vary from set to set (non linear regression), it's not easy to apply that multi level model (linear regression). What you...

Is this what you want? https://github.com/cjekel/piecewise_linear_fit_py/blob/master/examples/prediction_variance.py That plot should give the prediction with +- error bars. What do you want a confidence interval of?

The model parameters we fit are `beta` parameters, in the form of `Aβ=y`. For these `beta` parameters, it is straight forward to calculate a standard error for each. It is...