drjacoby_mcmc: Run an MCMC Sampler using drjacoby
In mrc-ide/squire: SEIR transmission model of COVID-19

drjacoby_mcmc

R Documentation

Run an MCMC Sampler using drjacoby

Description

The drjacoby mcmc sampler is very similar to [[pmcmc]] but there are a few subtle differences that meant it was easier to have a separate function for using drjacoby for the mcmc process

Usage

drjacoby_mcmc(
  data,
  n_mcmc,
  log_likelihood = NULL,
  log_prior = NULL,
  n_particles = 100,
  steps_per_day = 4,
  output_proposals = FALSE,
  n_chains = 1,
  squire_model = explicit_model(),
  pars_obs = list(phi_cases = 1, k_cases = 2, phi_death = 1, k_death = 2, exp_noise =
    1e+06),
  pars_init = list(start_date = as.Date("2020-02-07"), R0 = 2.5, Meff = 2, Meff_pl = 3,
    R0_pl_shift = 0),
  pars_min = list(start_date = as.Date("2020-02-01"), R0 = 0, Meff = 1, Meff_pl = 2,
    R0_pl_shift = -2),
  pars_max = list(start_date = as.Date("2020-02-20"), R0 = 5, Meff = 3, Meff_pl = 4,
    R0_pl_shift = 5),
  pars_discrete = list(start_date = TRUE, R0 = FALSE, Meff = FALSE, Meff_pl = FALSE,
    R0_pl_shift = FALSE),
  reporting_fraction = 1,
  treated_deaths_only = FALSE,
  country = NULL,
  population = NULL,
  contact_matrix_set = NULL,
  baseline_contact_matrix = NULL,
  date_contact_matrix_set_change = NULL,
  R0_change = NULL,
  date_R0_change = NULL,
  hosp_bed_capacity = NULL,
  baseline_hosp_bed_capacity = NULL,
  date_hosp_bed_capacity_change = NULL,
  ICU_bed_capacity = NULL,
  baseline_ICU_bed_capacity = NULL,
  date_ICU_bed_capacity_change = NULL,
  date_vaccine_change = NULL,
  baseline_max_vaccine = NULL,
  max_vaccine = NULL,
  date_vaccine_efficacy_infection_change = NULL,
  baseline_vaccine_efficacy_infection = NULL,
  vaccine_efficacy_infection = NULL,
  date_vaccine_efficacy_disease_change = NULL,
  baseline_vaccine_efficacy_disease = NULL,
  vaccine_efficacy_disease = NULL,
  Rt_args = NULL,
  burnin = 0,
  replicates = 100,
  forecast = 0,
  drjacoby_list = list(),
  ...
)

Arguments

`data`	Data to fit to. This must be constructed with `particle_filter_data`
`n_mcmc`	number of mcmc mcmc iterations to perform
`log_likelihood`	function to calculate log likelihood, must take named parameter vector as input, allow passing of implicit arguments corresponding to the main function arguments. Returns a named list, with entries: - $log_likelihood, a single numeric - $sample_state, a numeric vector corresponding to the state of a single particle, chosen at random, at the final time point for which we have data. If NULL, calculated using the function calc_loglikelihood.
`log_prior`	function to calculate log prior, must take named parameter vector as input, returns a single numeric. If NULL, uses uninformative priors which do not affect the posterior
`n_particles`	Number of particles (considered for both the PMCMC fit and sampling from posterior)
`steps_per_day`	Number of steps per day
`output_proposals`	Logical indicating whether proposed parameter jumps should be output along with results
`n_chains`	number of MCMC chains to run
`squire_model`	A squire model to use
`pars_obs`	list of parameters to use in comparison with `compare`. Must be a list containing, e.g. list(phi_cases = 0.1, k_cases = 2, phi_death = 1, k_death = 2, exp_noise = 1e6)
`pars_init`	named list of initial inputs for parameters being sampled
`pars_min`	named list of lower reflecting boundaries for parameter proposals
`pars_max`	named list of upper reflecting boundaries for parameter proposals
`pars_discrete`	named list of logicals, indicating if proposed jump should be discrete
`reporting_fraction`	Reporting fraction. Numeric for what proportion of the total deaths the reported deaths represent. E.g. 0.5 results in the model calibrating to twice the deaths provided by `data$deaths`
`treated_deaths_only`	Boolean for whether likelihood is based only on deaths that occur from healthcare systems, i.e. are treated. Default = FALSE, which uses all deaths.
`country`	Character for country beign simulated. WIll be used to generate `population` and `contact_matrix_set` if unprovided. Either `country` or `population` and `contact_matrix_set` must be provided.
`population`	Population vector (for each age group). Default = NULL, which will cause population to be sourced from `country`
`contact_matrix_set`	List of contact matrices to be used from the dates provided in `date_contact_matrix_set_change`.Must be same length as `date_contact_matrix_set_change`
`baseline_contact_matrix`	The starting contact matrix prior to any changes due to interventions or otherwise. Default = NULL, which will use the contact matrix associated with the coutnry provided.
`date_contact_matrix_set_change`	Calendar dates at which the contact matrices set in `model_params` change. Defaut = NULL, i.e. no change
`R0_change`	Numeric vector for relative changes in R0. Default = NULL, i.e. no change in R0
`date_R0_change`	Calendar dates at which R0_change occurs. Defaut = NULL, i.e. no change in R0
`hosp_bed_capacity`	Number of hospital beds at each date specified in `date_hosp_bed_capacity_change`. Must be same length as `date_hosp_bed_capacity_change`.
`baseline_hosp_bed_capacity`	The starting number of hospital beds before the epidemic started. Default = NULL, which will use the hospital beds data for the country provided. If no country is provided then this is 5/1000 of the population
`date_hosp_bed_capacity_change`	Calendar dates at which hospital bed capacity changes set in `model_params` change. Defaut = NULL, i.e. no change
`ICU_bed_capacity`	Number of ICU beds at each date specified in `date_ICU_bed_capacity_change`. Must be same length as `date_ICU_bed_capacity_change`.
`baseline_ICU_bed_capacity`	The starting number of ICU beds before the epidemic started. Default = NULL, which will use the hospital beds data for the country provided. If no country is provided then this is 3/100 of hospital beds
`date_ICU_bed_capacity_change`	Calendar dates at which ICU bed capacity changes set in `model_params` change. Defaut = NULL, i.e. no change
`date_vaccine_change`	Date that vaccine doses per day change. Default = NULL.
`baseline_max_vaccine`	Baseline vaccine doses per day. Default = NULL
`max_vaccine`	Time varying maximum vaccine doeses per day. Default = NULL.
`date_vaccine_efficacy_infection_change`	Date that vaccine efficacy against infection changes. Default = NULL.
`baseline_vaccine_efficacy_infection`	Baseline vaccine effacy against infection. Default = NULL
`vaccine_efficacy_infection`	Time varying vaccine efficacy against infection. Default = NULL.
`date_vaccine_efficacy_disease_change`	Date that vaccine efficacy against disease changes. Default = NULL.
`baseline_vaccine_efficacy_disease`	Baseline vaccine efficacy against disease Default = NULL
`vaccine_efficacy_disease`	Time varying vaccine efficacy against infection. Default = NULL.
`Rt_args`	List of arguments to be passed to `evaluate_Rt_pmcmc` for calculating Rt. Current arguments are available in `Rt_args_list`
`burnin`	number of iterations to discard from the start of MCMC run when sampling from the posterior for trajectories
`replicates`	number of trajectories (replicates) to be returned that are being sampled from the posterior probability results produced by `run_mcmc_chain` to select parameter set. For each parmater set sampled, run particle filter with `n_particles` and sample 1 trajectory
`forecast`	Number of days to forecast forward. Default = 0
`drjacoby_list`	List of arguments to pass to [[drjacoby::run_mcmc]] that are not data, df_params, misc, loglike, logprior, burnin or samples
`...`	Further aguments for the model parameter function. If using the `explicit_model` (default) this will be `parameters_explicit_SEEIR`.