Postprocess JAGS output
Arguments
- jags_output
output from
runjags::run.jags()- ids
IDs of individuals being sampled (JAGS discards this information, so it has to be re-attached)
- antigen_isos
names of biomarkers being modeled (JAGS discards this information, so it has to be re-attached)
Examples
set.seed(1)
raw_data <-
serocalculator::typhoid_curves_nostrat_100 |>
dplyr::filter(
antigen_iso |> stringr::str_starts(pattern = "HlyE")
) |>
sim_case_data(
n = 5,
antigen_isos = c("HlyE_IgA", "HlyE_IgG")
)
prepped_data <- prep_data(raw_data)
priors <- prep_priors(max_antigens = prepped_data$n_antigen_isos)
nchains <- 2
# nr of MC chains to run simultaneously
nadapt <- 1000
# nr of iterations for adaptation
nburnin <- 100
# nr of iterations to use for burn-in
nmc <- 100
# nr of samples in posterior chains
niter <- 200
# nr of iterations for posterior sample
nthin <- round(niter / nmc)
# thinning needed to produce nmc from niter
tomonitor <- c("y0", "y1", "t1", "alpha", "shape")
file_mod <- serodynamics_example("model.jags")
set.seed(11325)
jags_post <- runjags::run.jags(
model = file_mod,
data = c(prepped_data, priors),
inits = initsfunction,
method = "parallel",
adapt = nadapt,
burnin = nburnin,
thin = nthin,
sample = nmc,
n.chains = nchains,
monitor = tomonitor,
summarise = FALSE
)
#> Calling 2 simulations using the parallel method...
#> Following the progress of chain 1 (the program will wait for all chains
#> to finish before continuing):
#> Welcome to JAGS 4.3.2 on Tue Feb 25 01:11:13 2025
#> JAGS is free software and comes with ABSOLUTELY NO WARRANTY
#> Loading module: basemod: ok
#> Loading module: bugs: ok
#> . . Reading data file data.txt
#> . Compiling model graph
#> Resolving undeclared variables
#> Allocating nodes
#> Graph information:
#> Observed stochastic nodes: 42
#> Unobserved stochastic nodes: 24
#> Total graph size: 930
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 1000
#> -------------------------------------------------| 1000
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation successful
#> . Updating 100
#> -------------------------------------------------| 100
#> ************************************************** 100%
#> . . . . . . Updating 200
#> -------------------------------------------------| 200
#> ************************************************** 100%
#> . . . . Updating 0
#> . Deleting model
#> .
#> All chains have finished
#> Simulation complete. Reading coda files...
#> Coda files loaded successfully
#> Finished running the simulation
curve_params <- jags_post |> postprocess_jags_output(
ids = attr(prepped_data, "ids"),
antigen_isos = attr(prepped_data, "antigens")
)
print(curve_params)
#> # A tibble: 400 × 8
#> antigen_iso iter chain y0 y1 t1 alpha r
#> <fct> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 HlyE_IgA 1 1 1.39 77.1 10.7 0.00202 1.50
#> 2 HlyE_IgA 2 1 0.772 23.3 10.4 0.000669 1.38
#> 3 HlyE_IgA 3 1 1.01 19.2 3.23 0.000132 1.52
#> 4 HlyE_IgA 4 1 1.13 1.39 7.88 0.000579 1.49
#> 5 HlyE_IgA 5 1 1.54 83.6 6.11 0.000722 1.51
#> 6 HlyE_IgA 6 1 1.19 420. 8.36 0.00330 1.32
#> 7 HlyE_IgA 7 1 1.03 5.44 7.98 0.000382 1.41
#> 8 HlyE_IgA 8 1 1.40 3.24 11.0 0.00104 1.69
#> 9 HlyE_IgA 9 1 1.01 12.4 7.19 0.000357 1.54
#> 10 HlyE_IgA 10 1 1.12 2.18 6.46 0.000973 1.51
#> # ℹ 390 more rows