Simulate a cross-sectional serosurvey with noise

Description

Makes a cross-sectional data set (age, y(t) set) and adds noise, if desired.

Usage

sim_pop_data(
  lambda = 0.1,
  n_samples = 100,
  age_range = c(0, 20),
  age_fixed = NA,
  antigen_isos = intersect(get_biomarker_levels(curve_params), rownames(noise_limits)),
  n_mcmc_samples = 0,
  renew_params = FALSE,
  add_noise = FALSE,
  curve_params,
  noise_limits,
  format = "wide",
  verbose = FALSE,
  ...
)

Arguments

lambda	a numeric() scalar indicating the incidence rate (in events per person-years)
n_samples	number of samples to simulate
age_range	age range of sampled individuals, in years
age_fixed	specify the curve parameters to use by age (does nothing at present?)
antigen_isos	Character vector with one or more antibody names. Values must match curve_params.
n_mcmc_samples	how many MCMC samples to use: when n_mcmc_samples is in 1:4000 a fixed posterior sample is used when n_mcmc_samples = 0, a random sample is chosen
renew_params	whether to generate a new parameter set for each infection renew_params = TRUE generates a new parameter set for each infection renew_params = FALSE keeps the one selected at birth, but updates baseline y0
add_noise	a logical() indicating whether to add biological and measurement noise
curve_params	a data.frame() of MCMC samples of antibody decay curve parameters, with one row per MCMC sample. Required columns: antigen_iso, y0, y1, t1, alpha, r. See load_sr_params() for details.
noise_limits	biologic noise distribution parameters
format	a character() variable, containing either: “long” (one measurement per row) or “wide” (one serum sample per row)
verbose	verbosity level for console logging. Accepts a logical scalar or a non-negative whole number: FALSE/0: no log messages TRUE/1: basic progress messages >= 2: basic progress messages plus detailed input logging
…	Arguments passed on to simcs.tinf, ldpar, ab, mk_baseline age age at infection nmc mcmc sample to use npar number of parameters t numeric vector of elapsed times since start of infection par numeric matrix of model parameters: rows are parameters columns are biomarkers kab integer indicating which row to read from blims n number of observations blims range of possible baseline antibody levels

Value

a tibble::tbl_df containing simulated cross-sectional serosurvey data, with columns:

• age: age (in years)

• one column for each element in the antigen_isos input argument

Examples

library("serocalculator")

# Load curve parameters
dmcmc <- typhoid_curves_nostrat_100

# Specify the antibody-isotype responses to include in analyses
antibodies <- c("HlyE_IgA", "HlyE_IgG")

# Set seed to reproduce results
set.seed(54321)

# Simulated incidence rate per person-year
lambda <- 0.2
# Range covered in simulations
lifespan <- c(0, 10)
# Cross-sectional sample size
nrep <- 100

# Biologic noise distribution
dlims <- rbind(
  "HlyE_IgA" = c(min = 0, max = 0.5),
  "HlyE_IgG" = c(min = 0, max = 0.5)
)

# Generate cross-sectional data
csdata <- sim_pop_data(
  curve_params = dmcmc,
  lambda = lambda,
  n_samples = nrep,
  age_range = lifespan,
  antigen_isos = antibodies,
  n_mcmc_samples = 0,
  renew_params = TRUE,
  add_noise = TRUE,
  noise_limits = dlims,
  format = "long"
)