serocalculator (version 1.4.0)

sim_pop_data: 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,
  ...
)

Value

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

• age: age (in days)

• one column for each element in the antigen_iso input argument

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() containing MCMC samples of parameters from the Bayesian posterior distribution of a longitudinal decay curve model. The parameter columns must be named:

• antigen_iso: a character() vector indicating antigen-isotype combinations

• iter: an integer() vector indicating MCMC sampling iterations

• y0: baseline antibody level at $t=0$ ($y(t=0)$)

• y1: antibody peak level (ELISA units)

• t1: duration of infection

• alpha: antibody decay rate (1/days for the current longitudinal parameter sets)

• r: shape factor of antibody decay

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

logical: if TRUE, print verbose log information to console

...

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

Examples

# 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"
)