Runs specified stan model for the force-of-infection

Usage

fit_seromodel(
  serosurvey,
  model_type = "constant",
  is_log_foi = FALSE,
  foi_prior = sf_normal(),
  foi_sigma_rw = sf_none(),
  foi_index = NULL,
  foi_init = NULL,
  is_seroreversion = FALSE,
  seroreversion_prior = sf_normal(),
  ...
)

Arguments

serosurvey

survey_year: Year in which the survey took place (only needed to plot time models)
age_min: Floor value of the average between age_min and age_max
age_max: The size of the sample
n_sample: Number of samples for each age group
n_seropositive: Number of positive samples for each age group

model_type

Type of the model. Either "constant", "age" or "time"

is_log_foi

Boolean to set logarithmic scale in the FOI

foi_prior

Force-of-infection distribution specified by means of the helper functions. Currently available options are:

sf_normal: Function to set normal distribution priors
sf_uniform: Function to set uniform distribution priors

foi_sigma_rw

Prior distribution for the standard deviation of the force-of-infection. Currently available options are:

sf_normal: Function to set normal distribution prior. Available for time models in the log-scale
sf_cauchy: Function to set Cauchy distribution prior. Available for time models in regular scale.

foi_index

Integer vector specifying the age-groups for which force-of-infection values will be estimated. It can be specified by means of get_foi_index

is_seroreversion

Boolean specifying whether to include seroreversion rate estimation in the model

seroreversion_prior

seroreversion distribution specified by means of the helper functions. Currently available options are:

sf_normal: Function to set normal distribution priors
sf_uniform: Function to set uniform distribution priors
sf_none: Function to set no prior distribution

...

Additional parameters for rstan

Value

stan_fit object with force-of-infection and seroreversion (when applicable) samples

Examples

data(veev2012)
seromodel <- fit_seromodel(
serosurvey = veev2012,
  model_type = "time",
  foi_index = get_foi_index(veev2012, group_size = 30, model_type = "time")
)
#> 
#> SAMPLING FOR MODEL 'time_no_seroreversion' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 7.9e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.79 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
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#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.372 seconds (Warm-up)
#> Chain 1:                0.291 seconds (Sampling)
#> Chain 1:                0.663 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'time_no_seroreversion' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 4.5e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.365 seconds (Warm-up)
#> Chain 2:                0.286 seconds (Sampling)
#> Chain 2:                0.651 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'time_no_seroreversion' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 4.4e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.44 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.355 seconds (Warm-up)
#> Chain 3:                0.297 seconds (Sampling)
#> Chain 3:                0.652 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'time_no_seroreversion' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 4.5e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4: 
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#> Chain 4: 
#> Chain 4:  Elapsed Time: 0.374 seconds (Warm-up)
#> Chain 4:                0.278 seconds (Sampling)
#> Chain 4:                0.652 seconds (Total)
#> Chain 4: 
#> Warning: There were 28 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: Examine the pairs() plot to diagnose sampling problems