Posterior function pointer

Takes all of the input data/parameters and returns a function pointer. This function finds the posterior for a given set of input parameters (theta) and infection histories without needing to pass the data set back and forth. No example is provided for function_type=2, as this should only be called within serosolver

create_posterior_func(
  par_tab,
  titre_dat,
  antigenic_map = NULL,
  strain_isolation_times = NULL,
  version = 1,
  solve_likelihood = TRUE,
  age_mask = NULL,
  measurement_indices_by_time = NULL,
  mu_indices = NULL,
  n_alive = NULL,
  function_type = 1,
  titre_before_infection = FALSE,
  ...
)

Arguments

par_tab	the parameter table controlling information such as bounds, initial values etc. See example_par_tab
titre_dat	the data frame of data to be fitted. Must have columns: group (index of group); individual (integer ID of individual); samples (numeric time of sample taken); virus (numeric time of when the virus was circulating); titre (integer of titre value against the given virus at that sampling time). See example_titre_dat
antigenic_map	(optional) a data frame of antigenic x and y coordinates. Must have column names: x_coord; y_coord; inf_times. See example_antigenic_map
strain_isolation_times	(optional) if no antigenic map is specified, this argument gives the vector of times at which individuals can be infected
version	which version of the posterior function to solve (corresponds mainly to the infection history prior). Mostly just left to 1, but there is one special case where this should be set to 4 for the gibbs sampler. This is only really used by serosolver to place the infection history prior on the total number of infections across all years and individuals when version = 4
solve_likelihood	usually set to TRUE. If FALSE, does not solve the likelihood and instead just samples/solves based on the model prior
age_mask	see create_age_mask - a vector with one entry for each individual specifying the first epoch of circulation in which an individual could have been exposed
measurement_indices_by_time	if not NULL, then use these indices to specify which measurement bias parameter index corresponds to which time
mu_indices	if not NULL, then use these indices to specify which boosting parameter index corresponds to which time
n_alive	if not NULL, uses this as the number alive in a given year rather than calculating from the ages. This is needed if the number of alive individuals is known, but individual birth dates are not
function_type	integer specifying which version of this function to use. Specify 1 to give a posterior solving function; 2 to give the gibbs sampler for infection history proposals; otherwise just solves the titre model and returns predicted titres. NOTE that this is not the same as the attack rate prior argument, version!
titre_before_infection	TRUE/FALSE value. If TRUE, solves titre predictions, but gives the predicted titre at a given time point BEFORE any infection during that time occurs.
...	other arguments to pass to the posterior solving function

Value

a single function pointer that takes only pars and infection_histories as unnamed arguments. This function goes on to return a vector of posterior values for each individual

Examples

if (FALSE) {
data(example_par_tab)
data(example_titre_dat)
data(example_antigenic_map)
data(example_inf_hist)

## Simple model solving code. Output matches entries of example_titre_dat
model_func <- create_posterior_func(example_par_tab, example_titre_dat, example_antigenic_map, function_type = 3)
y <- model_func(example_par_tab$values, example_inf_hist)

## Solve likelihood
par_tab <- example_par_tab[example_par_tab$names != "phi",]
likelihood_func <- create_posterior_func(par_tab, example_titre_dat, example_antigenic_map, function_type = 1, version = 2)
liks <- likelihood_func(par_tab$values, example_inf_hist)
}