Combined calibration of model to multiple types of data

Top-level calibration based on mobility, splines, and phenomenological heterogeneity. This function is a wrapper for calibrate, which first sets up the model matrix for time-dependent transmission rates \(\beta(t)\). For example, for splines, it sets up the spline basis and the coefficients of each basis function. For mobility, it sets up the slope and intercept parameters (since mobility is treated as a log-linear function between break points).

calibrate_comb(
  data,
  params,
  opt_pars = NULL,
  mob_data = NULL,
  mob_breaks = NULL,
  mob_breaks_int = FALSE,
  mob_logist_scale = NA,
  spline_days = 14,
  spline_setback = 0,
  spline_extrap = c("linear", "constant"),
  spline_df = NA,
  knot_quantile_var = NA,
  spline_pen = 0,
  spline_type = "bs",
  spline_int = FALSE,
  testing_data = NULL,
  maxit = 10000,
  skip.hessian = FALSE,
  use_DEoptim = TRUE,
  DE_cores = 1,
  beta_break_dates = NULL,
  use_mobility = FALSE,
  use_phenomhet = FALSE,
  use_spline = FALSE,
  use_testing = FALSE,
  vars = NULL,
  debug_plot = interactive(),
  debug = FALSE,
  debug_hist = FALSE,
  return_val = c("fit", "X", "formula", "args", "time_args"),
  start_date = NULL,
  priors = list(),
  ...
)

Arguments

data: a data set to compare to, containing date/var/value (current version assumes that only a single state var is included)
params: parameters
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
mob_data: mobility data
mob_breaks: vector of breakpoints for piecewise mobility model
mob_breaks_int: (logical) specifies whether the intercept of the mobility/transmission relationship changes at each mobility breakpoint. The default (FALSE) specifies that mobility in 'mobility period' i is beta0*(rel_mobility)^p_i; TRUE would specify beta0*(a_i)*(rel_mobility)^p_i, with a_1==1.
mob_logist_scale: specifies the scale of the smooth (logistic) transition between mobility periods, in days: if it is NA (default), the model uses sharp breaks for a piecewise-constant model. Otherwise, the parameter used makes a logistic transition between the two periods with the specified scale.
spline_days: days between spline knots
spline_setback: days before end of time series to set boundary knots for spline (this implies linear extrapolation after knots if spline_type="ns" is specified, which is probably wise)
spline_extrap: spline extrapolation model ("linear" or "constant")
spline_df: overall spline degrees of freedom
knot_quantile_var: variable to use cum dist for knotspacing
spline_pen: penalization for spline
spline_type: spline type ("ns" for natural spline or "bs" for b-spline)
spline_int: spline intercept (??)
testing_data: data frame with columns containing dates (Date) and testing intensity (intensity) (= tests per capita per day)
maxit: maximum iterations for Nelder-Mead/optimization step
skip.hessian: skip Hessian calculation?
use_DEoptim: use differential evolution as first stage?
DE_cores: number of parallel workers for DE
beta_break_dates: discrete break dates for transmission changes
use_mobility: include mobility as a covariate in the model?
use_phenomhet: include phenomenological heterogeneity?
use_spline: include spline?
use_testing: include variation in testing intensity?
vars: which vars to use? (default is all in data)
debug_plot: plot debugging curves? (doesn't work with parallel DEoptim)
debug: print debugging messages?
debug_hist: keep information on parameter history?
return_val: "fit" (return calibrated value); "X" (short-circuit/return model matrix?); "formula" (return log-linear formula for time-varying beta)
start_date: start date
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))
...: extra args

Other classic_macpan: add_d_log(), add_updated_vaxrate(), aggregate_agecats(), calibrate(), 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

if (require(dplyr)) {
  dd <- ont_all %>% trans_state_vars() %>%
       filter(var %in% c("H","report"))
 params <- read_params("ICU1.csv")
 ## quick and dirty example (maximize speed)
if (FALSE) {
 calibrate_comb(data=dd, params=params,
               use_spline=TRUE,
              maxit=10, skip.hessian=TRUE, use_DEoptim =FALSE)
X <-  calibrate_comb(data=dd, params=params,
              use_spline=TRUE,
              spline_type="ns",
              spline_setback=1,
              spline_extrap="constant",
              return_val="X")
matplot(X, ylab="")
form <-  calibrate_comb(data=dd, params=params,
              use_spline=TRUE,
              spline_type="ns",
              spline_setback=1,
              spline_extrap="constant",
              return_val="formula")
print(form)
}
}

Arguments

See also

Examples