est_naloxone_vec {bennu}

Run Bayesian estimation of naloxone number under-reporting

Description

Samples from Bayesian model

Usage

est_naloxone_vec(
  N_region,
  N_t,
  N_distributed,
  regions,
  times,
  Orders2D,
  Reported_Distributed,
  Reported_Used,
  region_name,
  psi_vec = c(0.7, 0.2, 0.1),
  max_delays = 3,
  delay_alpha = 2,
  delay_beta = 1,
  run_estimation = TRUE,
  rw_type = 1,
  chains = 4,
  iter = 2000,
  seed = 42,
  adapt_delta = 0.85,
  ...
)

Arguments

N_region

Number of regions

N_t

number of time steps

N_distributed

Number of samples of reporting for distribution of kits

regions

vector (time, region) of regions (coded 1 to N_region)

times

vector (time, region) of regions (coded 1 to N_t)

Orders2D

vector (time, region) of orders

Reported_Distributed

vector (time, region) reported as distributed

Reported_Used

vector (time, region) reported as used

region_name

bring in region names

psi_vec

reporting delay distribution

max_delays

maximum delay from kit ordered to kit distributed

delay_alpha

shape parameter for order to distributed delay distribution

delay_beta

shape parameter for order to distributed delay distribution

run_estimation

if TRUE will sample from posterior otherwise will sample from prior only

rw_type

1 - random walk of order one. 2 - random walk of order 2.

chains

A positive integer specifying the number of Markov chains. The default is 4.

iter

A positive integer specifying the number of iterations for each chain (including warmup). The default is 2000.

seed

Seed for random number generation

adapt_delta

(double, between 0 and 1, defaults to 0.8)

...

other parameters to pass to rstan::sampling

Value

An S4 rstan::stanfit class object containing the fitted model

See Also

Other inference: est_naloxone()