Caitlin Cherryh has joined the development team and has been working on improving readibility of outputs, documentation, and testing.
This update includes an option for PoolPrev to skip the calculation of Bayesian estimates. When using bayesian = FALSE, only MLE and likelihood ratio confidence intervals will be calculated, substantially speeding up this function (perhaps x100).
This updates also removes one source of bias from prevalence estimates returned for any hierarchical models. This effects the results of HierPoolPrev and getPrevalence applied to models with random effects. Under the update, prevalence estimates will typically slightly increase, though the difference will not be notable if the sample size is large and there is little clustering.
Previous estimates of prevalence did not marginalise out the random effects when calculating population-level prevalence, but as of this version, random effects are marginalised out. Due to the complexity introduced by this bias-correction we now longer-support specifying nested surveys using ~(1|Layer1/Layer2) and recommend using the format ~(1|Layer1) + (1|Layer2) which should be equivalent as long as each level in Layer2 is unique --- i.e. the format already required for HierPoolPrev.
Due to the complexity introduced by this bias-correction, the way of specifying priors for HierPoolPrev has been updated. Priors for HierPoolPrev are now directly on the real-scale (logit-transformed) parameters, rather than prevalence directly. We have also updated the default priors for PoolRegBayes for regression parameters, as we believe the previous priors were too diffuse (normal(0,100)). The defaults for the centered predictors are now student(6,0,1.5).
HierPoolPrev now has functionality to return estimate of intracluster correlation coefficients (ICC) at one or more levels of clustering
HierPoolPrev and PoolPrev now have custom output classes (inheriting from tibble, the previous class for these outputs). This has allowed us (Caitlin) to add pretty-print functions these outputs which are much more human readable. Saving the output with write.csv or similar will still return a detailed, machine-readible output.
This is patch to fix a bug affecting PoolPrev. The bug affected the maximum likelihood estimates (MLE) and likelihood ratio confidence intervals (LR-CIs) of prevalence when the default Jeffrey's prior was being used. The bug would usually make the MLE and LR-CIs much closer to the Bayesian estimates than they should have been. As both sets of estimates are valid, the results will still have been approximately correct.
This patch also includes an option, replicate.poolscreen (default to FALSE), for PoolPrev. This options changes the way the likelihood ratio confidence intervals are calculated. With replicate.poolscreen = TRUE PoolPrev will more closely reproduce the results produced by Poolscreen. We believe that our implementation of these intervals is more correct so would recommend that users continue to use the default (replicate.poolscreen = FALSE), but this option may be helpful for those who are trying to compare results across the two programs.
We have published a paper about PoolTestR in Environmental Modelling and Software now available at https://doi.org/10.1016/j.envsoft.2021.105158. If you find this package useful, please let us know and/or cite our paper!
A couple bug fixes:
A few improvements:
Minor patch so that the package works across more platforms (namely solaris)
This is our first official release! Please see the github site (https://github.com/AngusMcLure/PoolTestR#pooltestr) for a basic crash course on using the package. An upcoming (open access) journal article will go into further detail. A preprint can be accessed at https://arxiv.org/abs/2012.05405. I'll post a link to the article when published.