Run Jags Model — run_mod • serodynamics

run_mod() takes a data frame and adjustable mcmc inputs to runjags::run.jags() as an mcmc bayesian model to estimate antibody dynamic curve parameters. The rjags::jags.model() models seroresponse dynamics to an infection. The antibody dynamic curve includes the following parameters:

y0 = baseline antibody concentration
y1 = peak antibody concentration
t1 = time to peak
r = shape parameter
alpha = decay rate

Usage

run_mod(
  data,
  file_mod,
  nchain = 4,
  nadapt = 0,
  nburn = 0,
  nmc = 100,
  niter = 100,
  strat = NA
)

Arguments

data: A base::data.frame() with the following columns.
file_mod: The name of the file that contains model structure.
nchain: An integer between 1 and 4 that specifies the number of mcmc chains to be run per jags model.
nadapt: An integer specifying the number of adaptations per chain.
nburn: An integer specifying the number of burn ins before sampling.
nmc: An integer specifying number of samples in posterior chains
niter: An integer specifying number of iterations.
strat: A character string specifying the stratification variable, entered in quotes.

Value

A jags.post list() object or multiple jags.post list() if stratified. Returned as a list() of class runjags::runjags
A base::data.frame() titled curve_params that contains the posterior distribution will be exported with the following attributes:
iteration = number of sampling iterations.
chain = number of mcmc chains run; between 1 and 4.
indexid = "newperson", indicating posterior distribution.
antigen_iso = antibody/antigen type combination being evaluated
alpha = posterior estimate of decay rate
r = posterior estimate of shape parameter
t1 = posterior estimate of time to peak
y0 = posterior estimate of baseline antibody concentration
y1 = posterior estimate of peak antibody concentration
stratified variable = the variable that jags was stratified by
A list of attributes that summarize the jags inputs, including:
class: Class of the output object.
nChain: Number of chains run.
nParameters: The amount of parameters estimated in the model.
nIterations: Number of iteration specified.
nBurnin: Number of burn ins.
nThin: Thinning number (niter/nmc)

Author

Sam Schildhauer

Examples

if (!is.element(runjags::findjags(), c("", NULL))) {
  library(runjags)
  set.seed(1)
  library(dplyr)
  strat1 <- serocalculator::typhoid_curves_nostrat_100 |>
    sim_case_data(n = 100) |>
    mutate(strat = "stratum 2")
  strat2 <- serocalculator::typhoid_curves_nostrat_100 |>
    sim_case_data(n = 100) |>
    mutate(strat = "stratum 1")

  dataset <- bind_rows(strat1, strat2)

  fitted_model <- run_mod(
    data = dataset, # The data set input
    file_mod = serodynamics_example("model.jags"),
    nchain = 4, # Number of mcmc chains to run
    nadapt = 100, # Number of adaptations to run
    nburn = 100, # Number of unrecorded samples before sampling begins
    nmc = 1000,
    niter = 2000, # Number of iterations
    strat = "strat"
  ) # Variable to be stratified
}
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
#> Calling 4 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 01:11:20 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: 3020
#>    Unobserved stochastic nodes: 535
#>    Total graph size: 44517
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation incomplete.
#> . Updating 100
#> -------------------------------------------------| 100
#> ************************************************** 100%
#> . . . . . . Updating 2000
#> -------------------------------------------------| 2000
#> ************************************************** 100%
#> . . . . Updating 0
#> . Deleting model
#> . 
#> All chains have finished
#> Warning: The adaptation phase of one or more models was not completed in 100 iterations, so the current samples may not be optimal - try increasing the number of iterations to the "adapt" argument
#> Simulation complete.  Reading coda files...
#> Coda files loaded successfully
#> Finished running the simulation
#> Calling 4 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 01:12:07 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: 2555
#>    Unobserved stochastic nodes: 535
#>    Total graph size: 39309
#> . Reading parameter file inits1.txt
#> . Initializing model
#> . Adapting 100
#> -------------------------------------------------| 100
#> ++++++++++++++++++++++++++++++++++++++++++++++++++ 100%
#> Adaptation incomplete.
#> . Updating 100
#> -------------------------------------------------| 100
#> ************************************************** 100%
#> . . . . . . Updating 2000
#> -------------------------------------------------| 2000
#> ************************************************** 100%
#> . . . . Updating 0
#> . Deleting model
#> All chains have finished
#> Warning: The adaptation phase of one or more models was not completed in 100 iterations, so the current samples may not be optimal - try increasing the number of iterations to the "adapt" argument
#> Simulation complete.  Reading coda files...
#> Coda files loaded successfully
#> Finished running the simulation