[Feature Request]: Increase Distribution Fitting Capabilities

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Introduction

The distributions platform in JASP is a powerful tool for analyzing and understanding continuous data. However, it currently lacks certain features that would make it more versatile and user-friendly. This feature request aims to address these limitations and enhance the distribution fitting capabilities in JASP.

Description of the Issue

The current distributions platform in JASP allows users to fit various continuous distributions and determine their scale and shape parameters. However, it does not support fitting distributions with a third parameter, such as an offset or bias. Additionally, the platform lacks the capability to try multiple distributions and select the one with the best goodness of fit. Furthermore, it does not support grouping and censoring capabilities, which are essential for analyzing complex data sets.

Purpose of the Feature Request

The primary purpose of this feature request is to enhance the distribution fitting capabilities in JASP, making it more suitable for analyzing real-world reliability data. Specifically, we need to fit a 3-parameter Weibull distribution to censored data to determine the best fitting distribution and predict the failure probability after a certain lifetime.

Use Case

The use case for this feature request is to analyze the reliability of components from multiple suppliers. We have timestamps of fails from each supplier, and we want to determine if there is a difference in reliability between them. We also want to predict the failure probability after 10 years of lifetime.

Is this Feature Request Related to a Problem?

Yes, this feature request is related to a problem. The data contains timestamps of fails from multiple suppliers, and we need to determine the best fitting distribution to analyze the reliability of each supplier. The current limitations of the distributions platform in JASP make it challenging to achieve this goal.

Is this Feature Request Related to a JASP Module?

Yes, this feature request is related to the Distributions module in JASP.

Desired Solution

To address the limitations of the current distributions platform in JASP, we propose the following solutions:

  1. Introduce 3-parameter Weibull distribution: We need to introduce the 3-parameter Weibull distribution, which includes the scale, shape, and location parameters. This will enable us to fit the distribution to censored data and analyze the reliability of components.
  2. Implement censoring capabilities: We need to implement left, right, and interval censoring capabilities, which will allow us to handle censored data and analyze the reliability of components more accurately.
  3. Batch capabilities: We need to implement batch capabilities, which will enable us to try multiple distributions and select the one with the best goodness of fit. This will help us to determine the best fitting distribution for our data.
  4. Goodness of fit metrics: We need to implement goodness of fit metrics, such as AICc, BICc, and p-values, to evaluate the fit of each distribution and select the best one.

Alternatives Considered

We have considered the following alternatives to address the limitations of the current distributions platform in JASP:

  1. Split the dataset: We could split the dataset into two parts one with censored data and the other without. However, this approach may introduce bias and is not ideal.
  2. Use Weibayes: We could use Weibayes, which is a Bayesian approach to fitting distributions. However, Weibayes is not available in JASP, and we need to find an alternative solution.

Additional Context

We have not received any response to this feature request, and we hope that this article will raise awareness about the limitations of the current distributions platform in JASP and the need for enhancements.

Conclusion

In conclusion, the current distributions platform in JASP lacks certain features that would make it more versatile and user-friendly. We propose introducing the 3-parameter Weibull distribution, implementing censoring capabilities, batch capabilities, and goodness of fit metrics to enhance the distribution fitting capabilities in JASP. We hope that this feature request will be considered, and we will be able to analyze complex data sets more accurately and efficiently.

Future Work

We plan to continue working on this feature request and explore alternative solutions to address the limitations of the current distributions platform in JASP. We will also provide updates on the progress of this feature request and any changes to the proposed solutions.

References

  • [1] Weibull, W. (1951). A statistical distribution function of wide applicability. Journal of Applied Mechanics, 18(3), 293-297.
  • [2] Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187-220.
  • [3] Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrov & F. Csaki (Eds.), Proceedings of the Second International Symposium on Information Theory (pp. 267-281). Budapest: Akadémiai Kiadó.

Appendix

The following appendix provides additional information on the proposed solutions and the alternatives considered.

Proposed Solutions

1. Introduce 3-parameter Weibull distribution

The 3-parameter Weibull distribution is a generalization of the 2-parameter Weibull distribution, which includes the scale, shape, and location parameters. This distribution is widely used in reliability engineering and survival analysis.

2. Implement censoring capabilities

Censoring is a common phenomenon in reliability engineering and survival analysis, where the failure time is not observed. We need to implement left, right, and interval censoring capabilities to handle censored data and analyze the reliability of components more accurately.

3. Batch capabilities

Batch capabilities will enable us to try multiple distributions and select the one with the best goodness of fit. This will help us to determine the best fitting distribution for our data.

4. Goodness of fit metrics

Goodness of fit metrics, such as AICc, BICc, and p-values, will be used to evaluate the fit of each distribution and select the best one.

Alternatives Considered

1. Split the dataset

We could split the dataset into two parts, one with censored data and the other without. However, this approach may introduce bias and is not.

2. Use Weibayes

Introduction

In our previous article, we discussed the limitations of the current distributions platform in JASP and proposed enhancements to improve its distribution fitting capabilities. In this article, we will address some of the frequently asked questions (FAQs) related to this feature request.

Q: What is the current limitation of the distributions platform in JASP?

A: The current distributions platform in JASP allows users to fit various continuous distributions and determine their scale and shape parameters. However, it does not support fitting distributions with a third parameter, such as an offset or bias. Additionally, the platform lacks the capability to try multiple distributions and select the one with the best goodness of fit. Furthermore, it does not support grouping and censoring capabilities, which are essential for analyzing complex data sets.

Q: Why is it necessary to introduce the 3-parameter Weibull distribution?

A: The 3-parameter Weibull distribution is a generalization of the 2-parameter Weibull distribution, which includes the scale, shape, and location parameters. This distribution is widely used in reliability engineering and survival analysis. Introducing the 3-parameter Weibull distribution will enable users to fit this distribution to censored data and analyze the reliability of components more accurately.

Q: What is censoring, and why is it necessary to implement censoring capabilities?

A: Censoring is a common phenomenon in reliability engineering and survival analysis, where the failure time is not observed. Implementing censoring capabilities will enable users to handle censored data and analyze the reliability of components more accurately.

Q: What are batch capabilities, and why are they necessary?

A: Batch capabilities will enable users to try multiple distributions and select the one with the best goodness of fit. This will help users to determine the best fitting distribution for their data.

Q: What are goodness of fit metrics, and why are they necessary?

A: Goodness of fit metrics, such as AICc, BICc, and p-values, will be used to evaluate the fit of each distribution and select the best one.

Q: What are the alternatives to addressing the limitations of the current distributions platform in JASP?

A: We have considered the following alternatives:

  1. Split the dataset: We could split the dataset into two parts, one with censored data and the other without. However, this approach may introduce bias and is not ideal.
  2. Use Weibayes: We could use Weibayes, which is a Bayesian approach to fitting distributions. However, Weibayes is not available in JASP, and we need to find an alternative solution.

Q: What is the expected outcome of implementing these enhancements?

A: The expected outcome of implementing these enhancements is to improve the distribution fitting capabilities in JASP, making it more suitable for analyzing complex data sets. This will enable users to determine the best fitting distribution for their data and analyze the reliability of components more accurately.

Q: What is the next step in implementing these enhancements?

A: We plan to continue working on this feature request and explore alternative solutions to address the limitations of the current distributions platform in JASP. We will also provide updates on the progress of this feature request and any changes to the proposed solutions.

Q: How can users provide feedback on this feature request?

A: Users can provide feedback on this feature request by commenting on this article or by contacting us directly. We value user feedback and will take it into consideration when implementing these enhancements.

Conclusion

In conclusion, the current distributions platform in JASP lacks certain features that would make it more versatile and user-friendly. We have proposed enhancements to improve its distribution fitting capabilities, including introducing the 3-parameter Weibull distribution, implementing censoring capabilities, batch capabilities, and goodness of fit metrics. We hope that this feature request will be considered, and we will be able to analyze complex data sets more accurately and efficiently.

References

  • [1] Weibull, W. (1951). A statistical distribution function of wide applicability. Journal of Applied Mechanics, 18(3), 293-297.
  • [2] Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187-220.
  • [3] Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrov & F. Csaki (Eds.), Proceedings of the Second International Symposium on Information Theory (pp. 267-281). Budapest: Akadémiai Kiadó.

Appendix

The following appendix provides additional information on the proposed solutions and the alternatives considered.

Proposed Solutions

1. Introduce 3-parameter Weibull distribution

The 3-parameter Weibull distribution is a generalization of the 2-parameter Weibull distribution, which includes the scale, shape, and location parameters. This distribution is widely used in reliability engineering and survival analysis.

2. Implement censoring capabilities

Censoring is a common phenomenon in reliability engineering and survival analysis, where the failure time is not observed. We need to implement left, right, and interval censoring capabilities to handle censored data and analyze the reliability of components more accurately.

3. Batch capabilities

Batch capabilities will enable us to try multiple distributions and select the one with the best goodness of fit. This will help us to determine the best fitting distribution for our data.

4. Goodness of fit metrics

Goodness of fit metrics, such as AICc, BICc, and p-values, will be used to evaluate the fit of each distribution and select the best one.

Alternatives Considered

1. Split the dataset

We could split the dataset into two parts, one with censored data and the other without. However, this approach may introduce bias and is not ideal.

2. Use Weibayes

We could use Weibayes, which is a Bayesian approach to fitting distributions. However, Weibayes is not available in JASP, and we need to find an alternative solution.