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,
  ...
)

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 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)

Value

a tibble::tibble()

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
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

# A tibble: 10,000 × 7
     age id    antigen_iso value lambda.sim sample_size cluster
   <dbl> <chr> <chr>       <dbl>      <dbl>       <dbl>   <int>
 1  3.53 1     HlyE_IgA    0.757       0.05         100       1
 2  3.53 1     HlyE_IgG    0.520       0.05         100       1
 3  2.27 2     HlyE_IgA    0.819       0.05         100       1
 4  2.27 2     HlyE_IgG    0.707       0.05         100       1
 5  9.05 3     HlyE_IgA    0.150       0.05         100       1
 6  9.05 3     HlyE_IgG    0.506       0.05         100       1
 7  5.94 4     HlyE_IgA    0.837       0.05         100       1
 8  5.94 4     HlyE_IgG    0.870       0.05         100       1
 9  9.88 5     HlyE_IgA    0.297       0.05         100       1
10  9.88 5     HlyE_IgG    0.272       0.05         100       1
# ℹ 9,990 more rows