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Feature expectation reporting #154
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Added a new example for this functionality and returned the simple example to being simple (now with a fixed growth rate). Also made some basic plots and started thinking about if/what data modules need to carry for postprocessing Including example plots below. Interesting here that the expected reported cases look more convincing than the actual nowcast... |
I think it might be a good idea to merge this into I think the minimum that needs to be done is:
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Going to merge into #152 to make a single large PR for merging into |
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LGTM
* add control variables for flexible reporting * add reporting function * extended model compiles * add partial changes for R side observation model * add expectation model input code * add skipping for latent model * add new default enw_expectation args and standardise usage * add support for priors and correctly allow modifiers with no convolution * debug implementation * new example for Rt and prototype plots * update priors specification
* add required delay_group_lmpf changes for missing ref model * debug to compilation * use segment where possible * expectation model compiles * dont store local lobs * remove blocker for expectation model as now MVP * test simple example * add enw_incidence_to_cumulativ and update enw_new_reports to match * add enw_incidence_to_cumulative * add internal helper functions for missing reference lookup * fix indexing bug with enw_reference_by_report * Feature expectation reporting (#154) * add control variables for flexible reporting * add reporting function * extended model compiles * add partial changes for R side observation model * add expectation model input code * add skipping for latent model * add new default enw_expectation args and standardise usage * add support for priors and correctly allow modifiers with no convolution * debug implementation * new example for Rt and prototype plots * update priors specification * Streamline time wording in in-model doc * add usage warning for the missing data MVP * update news * add handling of group-wise missing reference observations and look-ups * use filtered missing reference obs in likelihood * fix merge issues * update test snapshot * update example data * Change formulation readme * Format stan code, improve inline doc * Change expo to expl * Further formatting and refactoring * Rename stan function files * Add log_dot_product * Further refactor latent vs obs in model block / functions * Fix remaining expo to expl * deal with duplicated news item * Add vector to reporting delay and generation time definitions. * refresh docs * fix priors as data list test * updat expectation test snaps * simulated example * Revert "simulated example" This reverts commit ad40667. * Feature optional intercepts (#170) * add tools for making the intercept optional both in model and in R * fix existing tests * add new test * add news item * update doc * update documentation with new defaults * update tests to new default expectation model * update README * spell check * more spell checks * update credit in news Co-authored-by: Adrian Lison <adrian.lison@bsse.ethz.ch> Co-authored-by: Sebastian Funk <sebastian.funk@lshtm.ac.uk>
Rounding this week off with another Draft PR showcasing a prototype feature. Here we add latent reporting (i.e observations are based on some discrete scaled convolution of an underlying latent parameter) and a new formula interface for specifying observation level effects (i.e the growth rate is constant but reported observations have a day-of-the-week adjustment.
This in effect integrates
EpiNow2
(in its most basic form with fixed delays) into theepinowcast
framework with the addition of formula interface flexibility. No changes have been made to make nowcasting optional but this should be implemented in the future as it increases overlap withEpiNow2
making it easier to depreciate.Note: As for other draft PRs this week no testing or bug checking has been done here and this is strictly a draft. I will add a list of to-dos next week/later.
The current example has some slight fitting complaints similar to those seen in #152 and is likely driven by bugs/non-optimal implementation choices.