serocalculator (version 1.4.0)

sim_pop_data_multi: Simulate multiple data sets

Description

Simulate multiple data sets

Usage

sim_pop_data_multi(
  nclus = 10,
  sample_sizes = 100,
  lambdas = c(0.05, 0.1, 0.15, 0.2, 0.3),
  num_cores = max(1, parallel::detectCores() - 1),
  rng_seed = 1234,
  verbose = FALSE,
  ...
)

Value

a tibble::tibble()

Arguments

nclus

number of clusters

sample_sizes

sample sizes to simulate

lambdas

incidence rate, in events/person*year

num_cores

number of cores to use for parallel computations

rng_seed

starting seed for random number generator, passed to rngtools::RNGseq()

verbose

whether to report verbose information

...

Arguments passed on to sim_pop_data

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

noise_limits

biologic noise distribution parameters

format

a character() variable, containing either:

• "long" (one measurement per row) or

• "wide" (one serum sample per row)

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

Examples

# \donttest{
# 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
lambdas = c(.05, .1, .15, .2, .3)

# 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)
)

sim_data <- sim_pop_data_multi(
  curve_params = dmcmc,
  lambdas = lambdas,
  sample_sizes = nrep,
  age_range = lifespan,
  antigen_isos = antibodies,
  n_mcmc_samples = 0,
  renew_params = TRUE,
  add_noise = TRUE,
  noise_limits = dlims,
  format = "long",
  nclus = 10)

sim_data

# }