bife | Binary Choice Models with Fixed Effects | Development Tools library

by amrei-stammann R Version: Current License: No License

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kandi X-RAY | bife Summary

bife is a R library typically used in Utilities, Development Tools applications. bife has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

Binary Choice Models with Fixed Effects. An R-package to estimate fixed effects binary choice models (logit and probit) with potentially many individual fixed effects and computes average partial effects. Incidental parameter bias can be reduced with an asymptotic bias-correction proposed by Fernandez-Val (2009). bife can be used to fit fixed effects binary choice models (logit and probit) based on an unconditional maximum likelihood approach. It is tailored for the fast estimation of binary choice models with potentially many individual fixed effects. The routine is based on a special pseudo demeaning algorithm derived by Stammann, Heiss, and McFadden (2016). The estimates obtained are identical to the ones of glm(), but the computation time of bife() is much lower.

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Support

bife has a low active ecosystem.

It has 5 star(s) with 1 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 4 have been closed. On average issues are closed in 66 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of bife is current.

Quality

bife has 0 bugs and 0 code smells.

Security

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

bife code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

bife 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

bife releases are not available. You will need to build from source code and install.

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bife Key Features

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Community Discussions

Trending Discussions on bife

Remove linear dependent variables while using the bife package

Fixed effect logit:adjusted r square-bife package in R

How do I remove an object nested in two arrays?

Calculating AIC for Fixed Effect logit from bife package

QUESTION

Remove linear dependent variables while using the bife package

Asked 2020-Nov-16 at 12:45

Some pre-programmed models automatically remove linear dependent variables in their regression output (e.g. lm()) in R. With the bife package, this does not seem to be possible. As stated in the package description in CRAN on page 5:

If bife does not converge this is usually a sign of linear dependence between one or more regressors and the fixed effects. In this case, you should carefully inspect your model specification.

Now, suppose the problem at hand involves doing many regressions and one cannot inspect adequately each regression output -- one has to suppose some sort of rule-of-thumb regarding the regressors. What could be some of the alternatives to remove linear dependent regressors more or less automatically and achieve an adequate model specification?

I set a code as an example below:

...

ANSWER

Answered 2020-Nov-16 at 12:45

Since you're only looking at linear dependencies, you could simply leverage methods that detect them, like for instance lm.

Here's an example of solution with the package fixest:

Source https://stackoverflow.com/questions/64792747

QUESTION

Fixed effect logit:adjusted r square-bife package in R

Asked 2020-Jun-07 at 09:54

I”m working on my fixed effect logit model using bife package in R.

But the problem is I need the adjusted r square, currently I only can calculate normal r square using the package result.

Does this package have any function to do that?

...

ANSWER

Answered 2020-Jun-07 at 09:54

Logistic regression models do not have the concept of R-squared in the same sense as OLS. Nonetheless, there are a host of pseudo-R-squared metrics, that approximate R2 in the context of nonlinear models. Some of the enclosed metrics also adjust for model complexity in the same spirit as adjusted R2.

You can access the log-likelihood of the full model by using the logLik(.) function. You will also need to calculate the model with intercept only to get the value for L(M_intercept).

Source https://stackoverflow.com/questions/62213598

QUESTION

How do I remove an object nested in two arrays?

Asked 2020-May-05 at 10:20

I want to have a function that receives the name of the category and the name of one of the items of that category and returns the entire object without that item, how do I do that?

This is what I have so far, I managed to find the item I want to delete with two loops but I don't know how to delete it.

...

ANSWER

Answered 2020-May-05 at 10:20

One way to do this is to just filter the items, e.g.

Source https://stackoverflow.com/questions/61610450

QUESTION

Calculating AIC for Fixed Effect logit from bife package

Asked 2020-Jan-09 at 06:47

I would like to ask how to calculace inf. criteria such as AIC, etc... for Fixed effect logit model from bife package.

Basic summmary output does NOT include AIC, how ever when looking at: Goodness-of-fit for fixed effect logit model using 'bife' package

The AIC criterium was computed. how ever I do no have it in my summary output nor log-likelihood.

...

ANSWER

Answered 2020-Jan-09 at 06:47

If you check bife code, AIC was computed in earlier versions at least in version 0.5. You might be using the current version 0.6 in which AIC is no longer included.

If you do not mind using the older version, try the following:

remove the current version from your library.
download version 0.5 from CRAN website: https://cran.r-project.org/src/contrib/Archive/bife/
install to your computer: install.packages("D:\\bife_0.5.tar.gz", repos = NULL, type="source"). Assuming it is stored on D: drive.

Or:

Source https://stackoverflow.com/questions/59654270

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Install bife

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