tidybayes | Bayesian analysis + tidy data + geoms | Data Visualization library

by mjskay R Version: v3.0.4 License: GPL-3.0

X-Ray Key Features Code Snippets Community Discussions(5)Vulnerabilities Install Support

kandi X-RAY | tidybayes Summary

tidybayes is a R library typically used in Analytics, Data Visualization applications. tidybayes has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

tidybayes is an R package that aims to make it easy to integrate popular Bayesian modeling methods into a tidy data + ggplot workflow. It builds on top of (and re-exports) several functions for visualizing uncertainty from its sister package, ggdist.

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Support

tidybayes has a low active ecosystem.

It has 685 star(s) with 58 fork(s). There are 24 watchers for this library.

It had no major release in the last 12 months.

There are 41 open issues and 248 have been closed. On average issues are closed in 313 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of tidybayes is v3.0.4

Quality

tidybayes has 0 bugs and 0 code smells.

Security

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

tidybayes code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tidybayes 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

tidybayes releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

It has 12503 lines of code, 0 functions and 57 files.

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

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tidybayes Key Features

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tidybayes Examples and Code Snippets

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Community Discussions

Trending Discussions on tidybayes

Extracting draws from posterior after using emmeans and hpd.summary

Request to help understand an apparent discrepancy between tidybayes::add_predicted_draws and brms::posterior_predict

Caterpillar plot of posterior brms samples: Order factors in a ggdist plot (stat_slab)

Area within ROPE region does not have homogeneous color fill when using stat_slab

pass a string to tidybayes::spread_draws()

QUESTION

Extracting draws from posterior after using emmeans and hpd.summary

Asked 2022-Feb-14 at 22:54

I have a dataset from participants that provided liking ratings (on a scale from 0-100) of stimuli associated with rewards of different magnitudes (factor pval, with levels small/medium/large) and delay (factor time, with levels delayed/immediate). A subset of the data looks like this:

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ANSWER

Answered 2022-Feb-14 at 22:54

Regarding the first question: As is true of most summary methods, the returned object is just a summary, and it doesn't contain the information to convert it back to an object like the one that was summarized. However, the original emmGrid object does have all the needed content.

The other barrier is trying to work from the contrasts you don't want rather than getting the ones you do want. It is usually best to do the means and contrasts in two separate steps. It is quite simple to do:

Source https://stackoverflow.com/questions/71114243

QUESTION

Request to help understand an apparent discrepancy between tidybayes::add_predicted_draws and brms::posterior_predict

Asked 2021-Jan-21 at 11:38

I am using the Howell1 dataset from rethinking package.

...

ANSWER

Answered 2021-Jan-21 at 11:38

It turned out that the discrepancy was arising due to seed setting not being honored by brms::posterior_predict

Upon discussing with developer of brms package in github, he root caused the issue to be the following:

If you have set options(mc.cores = ), posterior_predict will evaluate in parallel by default, unless you change the core argument. On windows, parallel execution is done via parallel::parLapply and I don't know how that function respects seeds, if at all. When executing the code in serial (with 1 core) the results are reproducible.

Once I set the mc.cores to 1, I no longer see the discrepancy between posterior_predict and add_predicted_draws.

Hence I am marking the issue as resolved.

The relevant github links are:

Source https://stackoverflow.com/questions/65756978

QUESTION

Caterpillar plot of posterior brms samples: Order factors in a ggdist plot (stat_slab)

Asked 2020-Sep-10 at 02:52

I ran a bayesian linear mixed model with brms and can plot the estimates nicely but I can't figure out how to order the single-subject estimates based on the mean of the posterior samples (so as to get a caterpillar plot). This is what I've done.

Toy data:

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ANSWER

Answered 2020-Sep-10 at 02:52

Two points for consideration:

Ungroup the result from spread_draws, otherwise you won't be able to reorder the levels of subject;
Use fct_reorder from the forcats package in tidyverse. It's designed for this exact purpose.

Source https://stackoverflow.com/questions/63813576

QUESTION

Area within ROPE region does not have homogeneous color fill when using stat_slab

Asked 2020-Aug-30 at 05:42

I ran some multilevel models using brms and am following the examples here to plot draws from the posterior for each parameter.

Here's a toy dataset to reproduce the problem

...

ANSWER

Answered 2020-Aug-28 at 02:53

Your plot looks fine and gives me the following output. I am running R 4.0.2 and RStudio version 1.2.5 on windows. However, my output looks like yours if I expand it horizontally.

Source https://stackoverflow.com/questions/63622108

QUESTION

pass a string to tidybayes::spread_draws()

Asked 2020-Jan-18 at 19:49

How could I pass the variable name typical_r to tidybayes::spread_draws() programmatically? In my use case typical_r comes in as a string, but I can't pass a string to tidybayes::spread_draws().

...

ANSWER

Answered 2020-Jan-18 at 19:49

Use !!sym() to convert from string into a symbol

Source https://stackoverflow.com/questions/59797434

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

Vulnerabilities

No vulnerabilities reported

Install tidybayes

You can install the currently-released version from CRAN with this R command:.

Support

tidybayes aims to support a variety of models with a uniform interface. Currently supported models include rstan, brms, rstanarm, runjags, rjags, jagsUI, coda::mcmc and coda::mcmc.list, MCMCglmm, and anything with its own as.mcmc.list implementation. If you install the tidybayes.rethinking package, models from the rethinking package are also supported.

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