JMbayes | Joint Models for Longitudinal and Survival Data using MCMC | Machine Learning library

by drizopoulos R Version: v0.8.70 License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | JMbayes Summary

JMbayes is a R library typically used in Artificial Intelligence, Machine Learning applications. JMbayes has no vulnerabilities and it has low support. However JMbayes has 6 bugs. You can download it from GitHub.

JMbayes: Joint Models for Longitudinal and Survival Data under the Bayesian Approach.

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Quality

Security

License

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Support

JMbayes has a low active ecosystem.

It has 33 star(s) with 21 fork(s). There are 9 watchers for this library.

It had no major release in the last 12 months.

There are 8 open issues and 60 have been closed. On average issues are closed in 227 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of JMbayes is v0.8.70

Quality

JMbayes has 6 bugs (0 blocker, 0 critical, 2 major, 4 minor) and 0 code smells.

Security

JMbayes has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

JMbayes code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

JMbayes does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

JMbayes releases are available to install and integrate.

Installation instructions are not available. Examples and code snippets are available.

It has 707 lines of code, 0 functions and 3 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

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JMbayes Key Features

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JMbayes Examples and Code Snippets

No Code Snippets are available at this moment for JMbayes.

Community Discussions

Trending Discussions on JMbayes

How to fix incompatible matrix dimensions using mvJointModelBayes()?

QUESTION

How to fix incompatible matrix dimensions using mvJointModelBayes()?

Asked 2019-Oct-14 at 13:03

I'm trying to fit a joint model of longitudinal and time-to-event data using the JMbayes package, to predict risk of cardiac arrest as more symptom data becomes available over time. To start, I am fitting a univariate model, but I aim to incorporate a number of longitudinal outcomes once I have the code running, which is why I'm using the mvJointModelBayes() function.

However, when I run I try to run the function I come across the error below.

Error in { : task 1 failed - "addition: incompatible matrix dimensions: 500x1 and 3000x1"

I have used the same code as provided in the mvJMBayes vignette using pbc2 data, adapted to my dataset, but keep encountering the error. I can't find any obvious way in which my dataframes differ to the pbc2 dataset to be causing the error.

...

ANSWER

Answered 2019-Oct-14 at 13:03

I found out that it works if id is a numeric variable instead of a factor and if id occurs in the same order in both data sets. Running the following code before model fitting solves the issue:

Source https://stackoverflow.com/questions/57960686

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install JMbayes

You can download it from GitHub.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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