Estimate model parameters from data

Given time series data and a set of starting parameters/structure/etc., calibrate the model via negative binomial maximum likelihood estimation (MLE), simultaneously fitting initial conditions, initial growth rate, time-changes in growth rate, and dispersion parameters.

calibrate(
  start_date = min(data$date) - start_date_offset,
  start_date_offset = 15,
  end_date = max(data$date),
  time_args = list(params_timevar = data.frame(Date = c("2020-03-23", "2020-03-30",
    "2020-04-01"), Symbol = rep("beta0", 3), Relative_value = c(-1, NA, NA))),
  base_params,
  data,
  opt_pars = list(params = c(log_E0 = 4, log_beta0 = -1), logit_time_params = c(-1,
    -1), log_nb_disp = NULL),
  fixed_pars = NULL,
  sim_fun = run_sim_break,
  sim_args = NULL,
  aggregate_args = NULL,
  condense_args = NULL,
  priors = NULL,
  debug = FALSE,
  debug_plot = FALSE,
  debug_hist = FALSE,
  last_debug_plot = FALSE,
  use_DEoptim = FALSE,
  mle2_method = "Nelder-Mead",
  mle2_control = list(maxit = 10000),
  mle2_args = list(),
  seed = NULL,
  DE_args = list(),
  DE_lwr = NULL,
  DE_upr = NULL,
  DE_cores = getOption("mc.cores", 2)
)

Arguments

start_date: starting date for sims (far enough back to allow states to sort themselves out)
start_date_offset: days to go back before first data value
end_date: ending date
time_args: arguments passed to sim_fun
base_params: baseline parameters (an object (vector?) of type params_pansim containing all of the parameters needed for a simulation; some may be overwritten during the calibration process)
data: a data set to compare to, containing date/var/value (current version assumes that only a single state var is included)
opt_pars: starting parameters (and structure). Parameters that are part of the params_pansim parameter vector can be specified within the params element (with prefixes if they are transformed); other parameters can include distributional parameters or time-varying parameters
fixed_pars: parameters to fix
sim_fun: function for simulating a single run (e.g. run_sim_break, run_sim_mobility)
sim_args: additional arguments to pass to run_sim
aggregate_args: arguments passed to aggregate.pansim
condense_args: arguments to pass to condense (via run_sim) [not implemented yet?]
priors: a list of tilde-delimited expressions giving prior distributions expressed in terms of the elements of opt_pars, e.g. list(~dlnorm(rel_beta0[1],meanlog=-1,sd=0.5))
debug: print debugging messages?
debug_plot: plot debugging curves? (doesn't work with parallel DEoptim)
debug_hist: keep information on parameter history?
last_debug_plot: plot debugging curve for only last parameter set (stored in .debug_plot.pdf in current directory)
use_DEoptim: use differential evolution as first stage?
mle2_method: method arg for mle2
mle2_control: control args for mle2
mle2_args: additional arguments for mle2
seed: random-number seed (for DE)
DE_args: arguments for DEoptim
DE_lwr: lower bounds for DE optimization
DE_upr: upper bounds, ditto
DE_cores: number of parallel workers for DE

Details

mle2 is used to estimate parameters by trajectory matching. Differential evolution optimization is conducted via DEoptim.

Other classic_macpan: add_d_log(), add_updated_vaxrate(), aggregate_agecats(), calibrate_comb(), check_age_cat_compatibility(), check_contact_rate_setting(), col_multiply(), condense_age(), condense_params_vax(), condense_state(), condense_vax(), dev_is_tikz(), do_step(), expand_params_age(), expand_params_desc_age(), expand_params_desc_variant(), expand_params_desc_vax(), expand_params_mistry(), expand_params_variant(), expand_params_vax(), expand_state_age(), expand_state_vax(), expand_stateval_testing(), fix_pars(), fix_stored(), forecast_ensemble(), forecast_sim(), getData(), get_GI_moments(), get_Gbar(), get_R0(), get_doses_per_day(), get_evec(), get_kernel_moments(), get_opt_pars(), get_r(), invlink_trans(), make_betavec(), make_beta(), make_jac(), make_ratemat(), make_state(), make_test_wtsvec(), make_vaxrate(), mk_Nvec(), mk_agecats(), mk_contact_rate_setting(), mk_mistry_Nvec(), mk_pmat(), mk_vaxcats(), mle_fun(), non_expanded_states, rExp(), read_params(), repair_names_age(), restore(), run_sim_ageify(), run_sim_break(), run_sim_loglin(), run_sim_mobility(), run_sim_range(), run_sim(), show_ratemat(), testify(), texify(), trans_state_vars(), update_contact_rate_setting(), update_foi(), update_params_mistry(), vis_model(), write_params()

Examples

library(dplyr)
params <- fix_pars(read_params("ICU1.csv"))
 opt_pars <- list(params=c(log_E0=4, log_beta0=-1,
        log_mu=log(params[["mu"]]), logit_phi1=qlogis(params[["phi1"]])),
                                  logit_rel_beta0=c(-1,-1),
                                   log_nb_disp=NULL)
dd <- (ont_all %>% trans_state_vars() %>% filter(var %in% c("report", "death", "H")))
if (FALSE) {
  cal1 <- calibrate(data=dd, base_params=params, opt_pars=opt_pars, debug_plot=TRUE)
  cal1_DE <- calibrate(data=dd, base_params=params, opt_pars=opt_pars, use_DEoptim=TRUE, DE_cores=1)
  cal2 <- calibrate(data=dd, base_params=params, opt_pars=opt_pars, use_DEoptim=TRUE)
  if (require(bbmle)) {
     -logLik(cal2$mle2)
   }
   attr(cal2,"de")$optim$bestval
}

Arguments

Details

See also

Examples