Simulate single population with given network structure using data.table

get_study_population(
  network_handler = sim_block_network,
  network_handler_args,
  group_names,
  p_visible,
  add_groups
)

Arguments

network_handler: function that takes several arguments and returns igraph network object with vertex attribute type in the binary coded format (consists of 0's and 1's only)
network_handler_args: list of arguments passed to network_handler
group_names: character vector of groups within population with last group being hidden
p_visible: named list of visibility tendencies by group. This is used as mean of Beta distribution (with SD = 0.09) to generate probability of being recognized as member of group, being sampled as seed, etc. The order of objects in list have to follow the order of group_names
add_groups: named list of probabilities of additional group memberships. Examples include probability of service utilization (for service multiplier), being present at particular time-location (for TLS), etc.

Value

Population data frame for single study

Examples

if (FALSE) {
  get_study_population_dt(
    network_handler = sim_block_network,
    network_handler_args =
      list(N = 1000, K = 2, prev_K = c(known = .1, hidden = .2), rho_K = 0,
           p_edge_within = list(known = c(0.1, 0.1), hidden = c(0.1, 0.3)),
           p_edge_between = list(known = 0.02, hidden = 0.02),
           directed = FALSE),

    # groups
    group_names = c("known", "hidden"),

    # probability of visibility (show-up) for each group
    p_visible = list(known = 1, hidden = .5),

    # probability of service utilization in hidden population
    # for service multiplier
    add_groups =
      list(
        service_use = "rbinom(.N, 1, 0.25)",
        "paste0('loc_', 1:10) := lapply(rep(.2, times = 10), function(add) rbinom(.N, 1, 0.05 + hidden * add))",
        known_2 = 0.3,
        "paste0('known_', 3:10) := lapply(3:10, function(x) rbinom(.N, 1, 0.3))")
  )
}