mcmc_prelim_fit
mcmc_prelim_fit.RdTest multiple sets of parameters randomly drawn from range between maximum and minimum values in order to find approximate values giving maximum posterior likelihood
Usage
mcmc_prelim_fit(
n_iterations = 1,
n_param_sets = 1,
n_bounds = 1,
log_params_min = NULL,
log_params_max = NULL,
input_data = list(),
obs_sero_data = list(),
obs_case_data = list(),
mode_start = 0,
prior_settings = list(type = "zero"),
dt = 1,
n_reps = 1,
enviro_data = list(),
p_severe_inf = 0.12,
p_death_severe_inf = 0.39,
add_values = list(vaccine_efficacy = 1, p_rep_severe = 1, p_rep_death = 1, m_FOI_Brazil
= 1),
deterministic = TRUE,
mode_parallel = FALSE,
cluster = NULL,
plot_graphs = FALSE
)Arguments
- n_iterations
= Number of times to run and adjust maximum/minimum
- n_param_sets
= Number of parameter sets to run in each iteration
- n_bounds
= Number of parameter sets (with highest likelihood values) to take at each iteration to create new maximum/minimum values
- log_params_min
Initial lower limits of estimated parameter values (natural logarithm of actual limits)
- log_params_max
Initial upper limits of estimated parameter values (natural logarithm of actual limits)
- input_data
List of population and vaccination data for multiple regions (created using data input creation code and usually loaded from RDS file)
- obs_sero_data
Seroprevalence data for comparison, by region, year & age group, in format no. samples/no. positives
- obs_case_data
Annual reported case/death data for comparison, by region and year, in format no. cases/no. deaths
- mode_start
Flag indicating how to set initial population immunity level in addition to vaccination
If mode_start = 0, only vaccinated individuals
If mode_start = 1, shift some non-vaccinated individuals into recovered to give herd immunity (uniform by age, R0 based only)
If mode_start = 3, shift some non-vaccinated individuals into recovered to give herd immunity (stratified by age)- prior_settings
List containing settings for priors: must contain text named "type": If type = "zero", prior probability is always zero
If type = "norm", prior probability is given by dnorm calculation on parameter values with settings based on vectors of values in prior_settings:
norm_params_mean and norm_params_sd (vectors of mean and standard deviation values applied to log FOI/R0 parameters and to actual values of additional parameters)
+ FOI_mean + FOI_sd (mean + standard deviation of computed FOI, single values)
+ R0_mean + R0_sd (mean + standard deviation of computed R0, single values)- dt
time increment in days (must be 1 or 5)
- n_reps
Number of repetitions
- enviro_data
Data frame of values of environmental covariates (columns) by region (rows)
- p_severe_inf
Probability of an infection being severe
- p_death_severe_inf
Probability of a severe infection resulting in death
- add_values
List of parameters in addition to those governing FOI/R0, either giving a fixed value or giving NA to indicate that they are part of the fitted parameter set
vaccine_efficacy Vaccine efficacy (proportion of reported vaccinations causing immunity) (must be present)
p_rep_severe Probability of observation of severe infection
p_rep_death Probability of observation of death
m_FOI_Brazil Multiplier of spillover FOI for Brazil regions (only relevant if regions in Brazil to be considered)- deterministic
TRUE/FALSE - set model to run in deterministic mode if TRUE
- mode_parallel
TRUE/FALSE - indicate whether to use parallel processing on supplied cluster for speed
- cluster
Cluster of threads to use if mode_parallel = TRUE
- plot_graphs
TRUE/FALSE - plot graphs of evolving parameter space '