modelbased | :chart_with_upwards_trend: Estimate effects, contrasts and means based on statistical models | Machine Learning library

by easystats R Version: v0.3.0 License: GPL-3.0

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kandi X-RAY | modelbased Summary

modelbased is a R library typically used in Artificial Intelligence, Machine Learning, Pytorch, Tensorflow applications. modelbased has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

modelbased is a package helping with model-based estimations, to easily compute of marginal means, contrast analysis and model predictions.

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modelbased has a low active ecosystem.

It has 216 star(s) with 18 fork(s). There are 14 watchers for this library.

It had no major release in the last 12 months.

There are 43 open issues and 98 have been closed. On average issues are closed in 35 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of modelbased is v0.3.0

Quality

modelbased has no bugs reported.

Security

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

License

modelbased is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

modelbased releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

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modelbased Key Features

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modelbased Examples and Code Snippets

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Community Discussions

Trending Discussions on modelbased

ggplot: fill area between confidence interval area and 0

QUESTION

ggplot: fill area between confidence interval area and 0

Asked 2020-Jul-03 at 07:05

I would like to nicely fill the area between the bounds of confidence intervals (between the upper or lower bound of an area) and 0 using different colours for positive or negative (below or above 0).

I managed to get close to what I expect, but there are some artefacts at the bounds of each area. Any idea on how to address these issues appearing at the edges? Thanks!

...

ANSWER

Answered 2020-Jul-03 at 07:05

I think that those artefacts are caused by the definitions of Positive and Negative variables. More precisely, I think you should specify Negative = ifelse(CI_high < 0, -Limit, NA) instead of Negative = ifelse(CI_low < 0, -Limit, NA). In that case, you obtain:

Source https://stackoverflow.com/questions/62707779

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