Simulate multiple data sets
sim.cs.multi(
nclus = 10,
lambdas = c(0.05, 0.1, 0.15, 0.2, 0.3),
num_cores = max(1, parallel::detectCores() - 1),
rng_seed = 1234,
renew.params = TRUE,
add.noise = TRUE,
verbose = FALSE,
...
)number of clusters
#incidence rate, in events/person*year
number of cores to use for parallel computations
starting seed for random number generator, passed to rngtools::RNGseq()
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
a logical() indicating whether to add biological and measurement noise
whether to report verbose information
Arguments passed on to sim.cs
lambdaa numeric() scalar indicating the incidence rate (in events per person-years)
n.smplnumber of samples to simulate
age.rngage range of sampled individuals, in years
age.fxspecify the curve parameters to use by age (does nothing at present?)
antigen_isosCharacter vector with one or more antibody names. Values must match curve_params.
n.mchow many MCMC samples to use:
when n.mc is in 1:4000 a fixed posterior sample is used
when n.mc = 0, a random sample is chosen
noise_limitsbiologic noise distribution parameters
formata character() variable, containing either:
"long" (one measurement per row) or
"wide" (one serum sample per row)
curve_paramsa 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