Refers to section 10.3
Arguments
- data
the input data frame, must either have `age`, `pos`, `tot` columns (for aggregated data) OR `age`, `status` for (linelisting data)
- type
type of model ("far2", "far3" or "log_logistic")
- chains
number of Markov chains
- warmup
number of warmup runs
- iter
number of iterations
Value
a list of class hierarchical_bayesian_model with 6 items
- datatype
type of datatype used for model fitting (aggregated or linelisting)
- df
the dataframe used for fitting the model
- type
type of bayesian model far2, far3 or log_logistic
- info
parameters for the fitted model
- sp
seroprevalence
- foi
force of infection
Examples
# \donttest{
df <- mumps_uk_1986_1987
model <- hierarchical_bayesian_model(df, type="far3")
#>
#> SAMPLING FOR MODEL 'fra_3' NOW (CHAIN 1).
#> Chain 1: Rejecting initial value:
#> Chain 1: Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 1: Stan can't start sampling from this initial value.
#> Chain 1:
#> Chain 1: Gradient evaluation took 0.000168 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 1.68 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 5000 [ 0%] (Warmup)
#> Chain 1: Iteration: 500 / 5000 [ 10%] (Warmup)
#> Chain 1: Iteration: 1000 / 5000 [ 20%] (Warmup)
#> Chain 1: Iteration: 1500 / 5000 [ 30%] (Warmup)
#> Chain 1: Iteration: 1501 / 5000 [ 30%] (Sampling)
#> Chain 1: Iteration: 2000 / 5000 [ 40%] (Sampling)
#> Chain 1: Iteration: 2500 / 5000 [ 50%] (Sampling)
#> Chain 1: Iteration: 3000 / 5000 [ 60%] (Sampling)
#> Chain 1: Iteration: 3500 / 5000 [ 70%] (Sampling)
#> Chain 1: Iteration: 4000 / 5000 [ 80%] (Sampling)
#> Chain 1: Iteration: 4500 / 5000 [ 90%] (Sampling)
#> Chain 1: Iteration: 5000 / 5000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 16.86 seconds (Warm-up)
#> Chain 1: 98.178 seconds (Sampling)
#> Chain 1: 115.038 seconds (Total)
#> Chain 1:
#> Warning: There were 288 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: There were 11 transitions after warmup that exceeded the maximum treedepth. Increase max_treedepth above 10. See
#> https://mc-stan.org/misc/warnings.html#maximum-treedepth-exceeded
#> Warning: Examine the pairs() plot to diagnose sampling problems
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
model$info
#> mean se_mean sd 2.5%
#> alpha1 1.396732e-01 2.328172e-04 5.926643e-03 1.290067e-01
#> alpha2 1.989632e-01 3.342249e-04 8.454176e-03 1.847781e-01
#> alpha3 9.017286e-03 2.957517e-04 7.503320e-03 2.519932e-04
#> tau_alpha1 2.051039e+00 2.925237e-01 5.992958e+00 1.814318e-06
#> tau_alpha2 4.280878e+00 1.545375e+00 1.261621e+01 5.514011e-06
#> tau_alpha3 1.594944e+00 2.713658e-01 4.513571e+00 1.717967e-06
#> mu_alpha1 -2.434489e+00 4.143249e+00 4.467754e+01 -1.656101e+02
#> mu_alpha2 -9.043922e-01 1.406475e+00 3.373188e+01 -9.031034e+01
#> mu_alpha3 2.147579e+00 1.666808e+00 4.272829e+01 -9.818932e+01
#> sigma_alpha1 9.673678e+03 9.523462e+03 2.012224e+05 2.027127e-01
#> sigma_alpha2 8.037575e+01 1.664371e+01 5.816331e+02 1.342710e-01
#> sigma_alpha3 1.665419e+02 4.235920e+01 1.470059e+03 2.354869e-01
#> lp__ -2.534311e+03 2.684310e-01 4.176499e+00 -2.542650e+03
#> 25% 50% 75% 97.5% n_eff
#> alpha1 1.353546e-01 1.393536e-01 1.435425e-01 1.520502e-01 648.01829
#> alpha2 1.931939e-01 1.981250e-01 2.037919e-01 2.181404e-01 639.83066
#> alpha3 3.301399e-03 6.948630e-03 1.310897e-02 2.798314e-02 643.65402
#> tau_alpha1 4.699710e-04 1.339575e-02 4.377299e-01 2.433537e+01 419.72073
#> tau_alpha2 1.099782e-03 3.547442e-02 9.730938e-01 5.546799e+01 66.64842
#> tau_alpha3 3.847899e-04 1.424805e-02 4.511886e-01 1.803311e+01 276.64990
#> mu_alpha1 -4.777740e+00 1.761566e-01 4.990839e+00 8.616947e+01 116.27771
#> mu_alpha2 -3.043304e+00 1.912986e-01 2.809284e+00 6.930927e+01 575.19790
#> mu_alpha3 -4.356601e+00 8.834931e-02 7.109717e+00 1.097394e+02 657.14323
#> sigma_alpha1 1.511461e+00 8.640059e+00 4.612799e+01 7.424105e+02 446.43988
#> sigma_alpha2 1.013731e+00 5.309408e+00 3.015428e+01 4.258861e+02 1221.23065
#> sigma_alpha3 1.488748e+00 8.377652e+00 5.097869e+01 7.629637e+02 1204.40906
#> lp__ -2.537035e+03 -2.534291e+03 -2.531361e+03 -2.526569e+03 242.08025
#> Rhat
#> alpha1 1.0006390
#> alpha2 0.9997486
#> alpha3 0.9999655
#> tau_alpha1 1.0023789
#> tau_alpha2 1.0060590
#> tau_alpha3 1.0005442
#> mu_alpha1 1.0039620
#> mu_alpha2 0.9997144
#> mu_alpha3 1.0015442
#> sigma_alpha1 1.0019918
#> sigma_alpha2 1.0000931
#> sigma_alpha3 0.9997146
#> lp__ 1.0215691
plot(model)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
# }