bgoodri
commented
7 years ago Yeah, there was a postdoc at Columbia who was doing these things for the mice and mi packages. One difficulty is asking the users for priors when each variable with missingness has a turn at being the "outcome". Another is the MCMC is a bit slow for this, so they were doing optimization, which is not quite as reliable.
On Fri, Feb 10, 2017 at 6:07 AM, Aki Vehtari notifications@github.com wrote:
Summary:
This is an idea for a "new developer friendly" project. mice (Multivariate Imputation by Chained Equations) package https://cran.r-https://cran.r-project.org/package=mice allows user defined elementary imputation methods. It would be quite easy to make functions which would allow use of rstanarm, too. Description:
Implement Stan versions of elementary imputation methods
- mice.impute.stan.norm
- mice.impute.stan.logreg
- mice.impute.stan.polr
- mice.impute.stan.2l.norm
- mice.impute.stan.2lonly.norm based on the corresponding existing functions (without .stan in the name). The existing norm function uses a fixed conjugate prior and analytic posterior, logreg uses glm.fit, polr uses a function from MASS package, and 2l functions use Gibbs sampling. rstanarm would provide more flexibility on priors and better inference.
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avehtari
Summary:
This is an idea for a "new developer friendly" project. mice (Multivariate Imputation by Chained Equations) package https://cran.r-https://cran.r-project.org/package=mice allows user defined elementary imputation methods. It would be quite easy to make functions which would allow use of rstanarm, too.
Description:
Implement Stan versions of elementary imputation methods