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
lambda
a numeric()
scalar indicating the incidence rate (in events per person-years)
n.smpl
number of samples to simulate
age.rng
age range of sampled individuals, in years
age.fx
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.mc
how 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_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