Comments (4)
Complete for sLDA-X with binary outcome. Need to add for LDA and sLDA/sLDA-X with continuous outcome and sLDA with binary outcome.
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Complete for sLDA with binary and continuous outcomes. Complete for regression with no text for binary and continuous outcomes. Still need to support for LDA.
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Following Merkle et al. (2019) in Psychometrika, the marginal WAIC should probably be the default used for model comparison while the conditional WAIC could be another alternative. Currently, the conditional WAIC is computed and the marginal WAIC is not yet implemented. Merkle et al. (2019) detail an importance sampling adaptive quadrature approach to do this with continuous latent variables. Would this be appropriate in the discrete latent variable (i.e., topic) case?
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Merkle et al. (2019) detail an importance sampling adaptive quadrature approach to do this with continuous latent variables. Would this be appropriate in the discrete latent variable (i.e., topic) case?
We should be able to directly sum over the discrete latent variables in the joint posterior samples (no quadrature/sampling methods needed).
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Related Issues (20)
- Error in `gibbs_logistic()`
- Fix calculation of log-likelihood and log-posterior HOT 1
- Write validator functions for S4 classes
- Implement label switching correction
- Add argument checks to avoid crashing R session if C++ code encounters mismatched parameter dimensions in args
- Make missing data check specific to variables used in SLDA or SLDAX models
- Release psychtm 2020.1 HOT 1
- Write vignettes
- Add Piepel (1986) contrasts to `post_regression()`
- Implement Bayesian $R^2$ computation
- Expand unit tests
- Break up Gibbs sampling functions into subfunctions
- `packagedown` site
- Consider reducing `Rcpp` load/compile overhead by switching to lighter Rcpp headers
- Automatically remove rows containing too few words
- Update the prep_docs function to remove stop words and do stemming HOT 1
- AIC in summary output HOT 2
- waic_all apparently unnecessary parameter
- is.na check is too broad
- Comparing models with/without topics
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