Similarly to issue #77, get_study_est_chords is having an error due to the sparsity of the data. It will run, but it returns very small estimates (ranging from 0-2) with very large standard errors. You can work on debugging it after defining multi_study in the declaring-multiple-studies.qmd vignette.
I will also note that this function had some parallelization built into it from doParallel, so that is something to keep our eyes out for as we transition to the parallelization within DeclareDesign.
#Debug
dat = draw_data(multi_study, end = 2)
study_designs = list(study_1 = study_1, study_2 = study_2)
study_1_data = dat$population[[1]]
debugonce(get_study_est_chords)
get_study_est_chords(data = study_1_data, type = c("mle", "integrated", "jeffreys"),
seed_condition = "rds_from == -999",
prefix = "rds",
n_boot = 100,
parallel_boot = FALSE,
label = "chords")
Similarly to issue #77, get_study_est_chords is having an error due to the sparsity of the data. It will run, but it returns very small estimates (ranging from 0-2) with very large standard errors. You can work on debugging it after defining multi_study in the declaring-multiple-studies.qmd vignette.
I will also note that this function had some parallelization built into it from doParallel, so that is something to keep our eyes out for as we transition to the parallelization within DeclareDesign.