rstanarm | rstanarm R package for Bayesian applied regression modeling | Analytics library

by stan-dev R Version: v2.21.3 License: GPL-3.0

X-Ray Key Features Code Snippets Community Discussions(7)Vulnerabilities Install Support

kandi X-RAY | rstanarm Summary

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

rstanarm R package for Bayesian applied regression modeling

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rstanarm has a low active ecosystem.

It has 346 star(s) with 129 fork(s). There are 43 watchers for this library.

It had no major release in the last 12 months.

There are 144 open issues and 271 have been closed. On average issues are closed in 321 days. There are 7 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of rstanarm is v2.21.3

Quality

rstanarm has 0 bugs and 0 code smells.

Security

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

rstanarm code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

rstanarm 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

rstanarm releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

It has 523 lines of code, 0 functions and 2 files.

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

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rstanarm Key Features

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rstanarm Examples and Code Snippets

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Community Discussions

Trending Discussions on rstanarm

How to specify lower bound as 0 on Bayesian priors

bayestestR for Bayesian Logistic Regression

browseURL function is not working in GCP AI notebook

Removing atypical internal lines from the chain convergence graph using a traceplot function

How to specify informative prior only on one specific predictor in rstanarm

How do I use tidyeval in R to create a small prediction dataset that has all levels of factors?

Why does tab_model (sjPlot) re-run MCMC with rstanarm model?

QUESTION

How to specify lower bound as 0 on Bayesian priors

Asked 2021-Oct-07 at 15:02

I am using rstanarm and want to create priors that are bound to be positive, so lower>0. How can I do this?

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ANSWER

Answered 2021-Oct-07 at 15:02

That is not currently possible with the rstanarm package. It is possible if you use the brms package or write your own Stan code.

Source https://stackoverflow.com/questions/69479063

QUESTION

bayestestR for Bayesian Logistic Regression

Asked 2021-Jun-29 at 12:23

I would like to perform Bayesian Logistic Regression using the bayestestR and rstanarm in R. The output, I believe, is in the log(odds ratio). Do you know of a way in which I can convert everything, i.e. the centrality, uncertainty, existence and significance indices into odds ratio instead. I know tbl_summary function from gtsummary package has an argument, exponentiate = TRUE that returns everything in OR.

Code:

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ANSWER

Answered 2021-Jun-29 at 12:23

I'd recommend using the parameters package which uses bayestestR internally but is more flexible:

Source https://stackoverflow.com/questions/68176311

QUESTION

browseURL function is not working in GCP AI notebook

Asked 2021-Feb-04 at 12:06

I am trying to run GLM function of rstanarm library. I am using rpy2 library to connect python with R packages. Following is the code:

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ANSWER

Answered 2021-Jan-21 at 14:47

browseURL() is an R function trying to open a web browser to show the content at an URL. The GCP AI notebook is running on the cloud, and the system it runs on probably does not have a web browser set.

You'll need to find a way to call the R function stan_glm() in a way that does not make it require a web browser.

Source https://stackoverflow.com/questions/65827343

QUESTION

Removing atypical internal lines from the chain convergence graph using a traceplot function

Asked 2021-Jan-28 at 23:21

I am making the convergence graph of the chains generated using the traceplot function. However, see what unusual lines are appearing on the chart. How would you go about removing them?

data: https://drive.google.com/file/d/1iOuGbjNI_caLWBIz4s7hZX5GlfhLrwr9/view?usp=sharing

Below are the codes.

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ANSWER

Answered 2021-Jan-28 at 23:21

By setting col="black" you have removed the information ggplot needs to keep the traces for each chain separate. Adding aes(group=chain) as below appears to work (although I would consider whether you really want to make the chains indistinguishable from each other: part of the point of showing a trace plot is to verify that the different chains have similar behaviour ...)

Source https://stackoverflow.com/questions/65943693

QUESTION

How to specify informative prior only on one specific predictor in rstanarm

Asked 2021-Jan-13 at 03:50

I'm currently trying to fit a Bayesian multilevel model using rstanarm. I expect - and there's evidence in literature - that the coefficient of my main predictor lies between 0.15 and 0.65. Therefore, I'd like to set an informative prior but only for this variable and keeping the weakly informative default for the others. Up to now I have:

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ANSWER

Answered 2021-Jan-13 at 03:50

Yes, it is but you need to pass a vector of prior locations and / or scales whose size(s) are equal to the number of coefficients (excluding the intercept). So, in your example it might be something like:

Source https://stackoverflow.com/questions/65687100

QUESTION

How do I use tidyeval in R to create a small prediction dataset that has all levels of factors?

Asked 2020-Oct-29 at 15:06

I am trying to obtain a prediction grid using the modelr::data_grid() function that has all levels for one factor of a model and only the "typical" level for the other factor in a model. The wrinkle is that the call levels() on the factor held constant in the prediction grid should return all levels present in the original data. Otherwise, some prediction functions can fail and return the infamous

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ANSWER

Answered 2020-Oct-29 at 15:06

The problem is that the . inside the across is the actual column not the name of the column. Sadly there is no way to get the names inside an across call however using purrr::reduce we can make a workaround:

Source https://stackoverflow.com/questions/64509090

QUESTION

Why does tab_model (sjPlot) re-run MCMC with rstanarm model?

Asked 2020-Oct-14 at 00:22

I am creating a table with tab_model from the package sjPlot (https://cran.r-project.org/web/packages/sjPlot/vignettes/tab_model_estimates.html).

However, when I use a negative binomial rstanarm model object, tab_model re-runs MCMC chains. My actual model takes many hours to run, so this is not ideal for tab_model to be doing this, but it doesn't seem to do it for other models (such as with glmer in lme4).

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ANSWER

Answered 2020-Oct-14 at 00:22

tl;dr I think this is going to be hard to avoid without hacking both the insight package and this one, or asking the package maintainer for an edit, unless you want to forgo printing the ICC, R^2, and the random-effects variance. Here, tab_model() calls insight::get_variance(), which tries to compute variances for the null model so it can compute the ICC and R^2. Computing these variances requires re-running the model. (When it does it for the glmer.nb, it goes via lme4:::update.merMod() and is quick enough that you don't notice the computation time.)

Source https://stackoverflow.com/questions/64344283

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