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I walked through the new vignette at https://github.com/RConsortium/brms.mmrm/blob/main/vignettes/usage.Rmd which walks through an MMRM analysis from the data through visualization. Now is a great time to try the prototype on a variety of datasets. The feedback from today was great. Specific suggestions included:
Ask to customize the delimiter in column names for as.mcmc() on emmeans() objects.
Consider a tidy long format for draws. Possible downside: efficiency of long pivots. Could be managed with a tidy = TRUE/FALSE argument.
Consider an opinionated display-ready table for output to include in study reports. Might use gt or xtable or Tplyr. Could use output from PROC MIXED as precedent.
Now is a good time to think about example datasets to include in the package.
Great time to try the package on a large data problem and a small data problem and compare the results to e.g. mrmm and SAS.
Consider HPD intervals as an alternative to equal-tailed quantile-based intervals.
How would be visualize the prior? Maybe using prior predictive marginal samples?
In the prior argument, restrict input to objects created by brms.mmrm. This structure will make it easier to safeguard users and implement MAP priors.
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crew
, its launcher plugin interface, and integration with SLURM and other clusters.as.mcmc()
onemmeans()
objects.tidy = TRUE/FALSE
argument.gt
orxtable
orTplyr
. Could use output from PROC MIXED as precedent.mrmm
and SAS.prior
argument, restrict input to objects created bybrms.mmrm
. This structure will make it easier to safeguard users and implement MAP priors.Beta Was this translation helpful? Give feedback.
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