Hi

Is it possible to fit a distribution with distfit library for a discrete variable? For example, let's say I have a survey that has 10 questions with possible values that go from 1 (poor) to 5 (excellent), and 100 persons take the survey.

Best regards

Here I used distfit to get a distribution that is the closest to my data，but not exactly。When I use the kstest from the scipy library to calculate the p-value to see if I can trust the distribution, the p-value is not ideal.Can I still use distfit to get a distribution to describe my data ?

Thanks for this great library.

Purpose of this modification: I have been using it with a multivariate time series dataset. Each dimension gets its own plot and wanted to make use of subplots to see all the dimensions at the same time (in a grid for e.g.)

Notes: a) I have added fig as the parameter to the plotting API as well. Generally, it is not required. I have done it so as to not create a situation where the number of return values is 1. This way your function always return 2 values (the tuple).

b) Instead of using plt.xlim and plt.ylim, I am using ax.set_xlim & ax.set_ylim. This should work for previous version and for this modification as well.

c) For now if the method is 'discrete' then passed fig and axes are ignored since the plot_binom function creates subplots internally.

I have a problem where loggamma fits best. I ran your script and my own custom script, they agree on beta parameters but the loggamma seemed much more natural. If it's not too much trouble, please consider adding this. If you are using scipy.stats, then it's the same API as others.

Cool project.

This project looks really great, thank you. I have two questions:

• How do you set loc = 0 if you know that is the right value for it? I am trying to fit to a symmetric distribution.
• When I try distfit with distr='full' it gets stuck at levy_l. Is this expected?

Hi,

Both dist.plot() and dist.plot_summary() do not generate plots for me. I am using the bare version of Python (i.e no Conda etc.)

Am I missing somethings?

Regards,

Danish

We are using distfit to try to determine if some data we have can be modelled parametrically. For some of the data, the best fitting distribution was a t. Scale and loc are clearly documented, and that is great. There is one remaining parameter to fit a t distribution, and that is degrees of freedom. Except, the one parameter in the distfit output that isn't a scale or loc value is less than one. Obviously, degrees of freedom can't be less than one. So what is that parameter and why isn't degrees of freedom included in the output? It would be helpful for automating our process.

Hello, your package really useful, thanks a lot!

I have a question: If I want to print the best parameters, what's the syntax? For example, I want to print the best n and p for binomial distribution for the following work.

thanks a lot

Thank you for your time spent making this package.

When you call plt.show(), you've rendered the plot and it can no longer be modified by the user, making it pointless to return the figure and axes objects.

For example, try:

fig, ax = dist.plot()
ax.axvline(x=0)
plt.title("Blarg!")

Unlike sns plots and dataframe.plot() calls that many are familiar with, the plots of distfit cannot be modified after called. This is surprising to the user (at least it was to me 😀)

The distr parameter in your core distfit class should accept a custom list of distributions that the user wants to run fitting on. Is there a specific reason you have not allowed it to accept a list?

Do you plan to have a generate or rvs method added to a fitted dist class to generate a given number (chosen by a size parameter) of new points with the best-fitted distribution? Here is the imagined code (say I have a dataset called dataset)

dist = distfit(todf=True)
dist.fit_transform(dataset)

# Newly generated 1000 points from the best-fitted distribution (based on some score criteria)
new_data = dist.generate(size=1000)

Good day!

Guys, I have found your package really cool) Thanks a lot)

I have a question:

Our incoming data can be with anomalies, noise. So, quality of our results is vulnerable to strong/weak outliers. Work with outliers is key feature of your package. Consequently, the quality of predictions based on our data model can be severely compromised. In a sense, we are training and predicting from the same data.

What is your advice?

I understand that, it is largely dependent on and provided by the nature of one or another theoretical distribution of data.

But, better to know, your personal opinion as authors...

What a really awesome repository !

By the way, K distribution is widely used in the filed of Radar and sonar. It is necessary to estimate the parameters of the K distribution.

Please consider adding this distribution if possible.

Hello everyone,

I noticed in the code erdogant/distfit/distfit.py that whenever you use the KS statistical test (stats=ks), you call the scipy.stats.ks_2samp to test your data against the distribution you estimated through MLE (maximum likelihood estimation). Is that true? If so, this is wrong, because now the KS statistic depends on your data and the test is no longer valid. In such a case, I would recommend you to have a look at parametric/non-parametric bootstrapping to solve the issue. This reference could be useful https://ui.adsabs.harvard.edu/abs/2006ASPC..351..127B/abstract