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Run Household Transmission Model

Source: R/hestia_functions.R
run_model.Rd

Run Household Transmission Model

Usage

run_model(
  inf_model,
  obs_model,
  data,
  init_probs,
  t_start = 1,
  epsilon = 1e-10,
  ih_cov = NULL,
  eh_cov = NULL,
  init = NULL,
  save_llik = FALSE,
  save_states = FALSE,
  stan_opts = stan_options()
)

Arguments

inf_model

Infection process model object generated by make_infection_model

obs_model

Observation process model object generated by make_observation_model

data

data frame with columns the following columns:

  • "t", time of observation

  • "hh_id", numeric household ID which must be 1, 2, ..., N where N is the total number of household

  • "part_id", participant ID within the household which must be 1, 2, ... n where n is the number of observed household members

  • "hh_size", number of household members (whether observed or not)

  • At least one column with binary outcome information where the column name matches the outcome name provided to make_observation_model

init_probs

vector of initial probabilities for each infection process model state

t_start

integer value corresponding to the start of model time

epsilon

very small number to use for zero probability transitions

ih_cov

NULL for run without covariates, otherwise data frame with intra-household covariates for each participant

eh_cov

NULL for run without covariates, otherwise data frame with extra-household covariates for each participant

init

initial conditions for MCMC chains

save_llik

whether to write the per-household log-likelihood (llik_final) in the model's generated quantities, for use with loo/waic (default FALSE). When FALSE, llik_final is length 0 and the extra per-household forward pass is skipped.

save_states

whether to write the per-participant log forward probabilities (logalpha) in the model's generated quantities, for reconstructing latent state probabilities (default FALSE). When FALSE, logalpha is a 0x0 matrix.

stan_opts

a named list of Stan sampler options, as produced by stan_options() (for example stan_options(iter = 1000)).

Value

draws_array object with chains for each model parameter

Examples

if (FALSE) { # interactive()
# Subset sir package data to 10 households (for speedy example)
sir_sub <- sir[sir$hh_id <= 10, ]

# Make infetion process model for SIR model
inf_mod <- make_infection_model(
  transmit(from = "S", to = "I"),
  progress(from = "I", to = "R", gamma = NA))

# Make observation process model
obs_mod <- make_observation_model(
  pcr = c("S" = 0.05, "I" = 0.95, "R" = 0.05),
  igg = c("S" = 0.01, "I" = 0.01, "R" = 0.8))

# Fitting the Stan model is slow, so it is not run on CRAN.
# \donttest{
run_model(inf_model = inf_mod,
          obs_model = obs_mod,
          data = sir_sub,
          init_probs = c(1 - 2 * 1e-10, 1e-10, 1e-10))
# }
}