Summarise specified model

Usage

summarise_seromodel(
  seromodel,
  serosurvey,
  alpha = 0.05,
  par_loo_estimate = "elpd_loo",
  loo_estimate_digits = 1,
  central_estimate_digits = 2,
  rhat_digits = 2
)

Arguments

seromodel

stan_fit object obtained from sampling a model with fit_seromode

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

alpha

1 - alpha indicates the credibility level to be used

par_loo_estimate

Name of the loo estimate to be extracted. Available options are:

"elpd_loo": Expected log pointwise predictive density
"p_loo": Effective number of parameters
"looic": Leave-one-out cross-validation information criteria

For additional information refer to loo.

loo_estimate_digits

Number of loo estimate digits

central_estimate_digits

Number of central estimate digits

rhat_digits

Number of rhat estimate digits

Value

A list summarising the specified model

model_name: Name of the model
elpd: elpd and its standard deviation
foi: Estimated foi with credible interval (for 'constant' model)
foi_rhat: foi rhat value (for 'constant' model)
seroreversion_rate: Estimated seroreversion rate
seroreversion_rate_rhat: Seroreversion rate rhat value

Examples

data(veev2012)
seromodel <- fit_seromodel(veev2012, iter = 100)
#> 
#> SAMPLING FOR MODEL 'constant_no_seroreversion' 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 2.4e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.24 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: WARNING: There aren't enough warmup iterations to fit the
#> Chain 1:          three stages of adaptation as currently configured.
#> Chain 1:          Reducing each adaptation stage to 15%/75%/10% of
#> Chain 1:          the given number of warmup iterations:
#> Chain 1:            init_buffer = 7
#> Chain 1:            adapt_window = 38
#> Chain 1:            term_buffer = 5
#> Chain 1: 
#> Chain 1: Iteration:  1 / 100 [  1%]  (Warmup)
#> Chain 1: Iteration: 10 / 100 [ 10%]  (Warmup)
#> Chain 1: Iteration: 20 / 100 [ 20%]  (Warmup)
#> Chain 1: Iteration: 30 / 100 [ 30%]  (Warmup)
#> Chain 1: Iteration: 40 / 100 [ 40%]  (Warmup)
#> Chain 1: Iteration: 50 / 100 [ 50%]  (Warmup)
#> Chain 1: Iteration: 51 / 100 [ 51%]  (Sampling)
#> Chain 1: Iteration: 60 / 100 [ 60%]  (Sampling)
#> Chain 1: Iteration: 70 / 100 [ 70%]  (Sampling)
#> Chain 1: Iteration: 80 / 100 [ 80%]  (Sampling)
#> Chain 1: Iteration: 90 / 100 [ 90%]  (Sampling)
#> Chain 1: Iteration: 100 / 100 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.005 seconds (Warm-up)
#> Chain 1:                0.003 seconds (Sampling)
#> Chain 1:                0.008 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'constant_no_seroreversion' NOW (CHAIN 2).
#> Chain 2: Rejecting initial value:
#> Chain 2:   Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 2:   Stan can't start sampling from this initial value.
#> Chain 2: 
#> Chain 2: Gradient evaluation took 2e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
#> Chain 2: WARNING: There aren't enough warmup iterations to fit the
#> Chain 2:          three stages of adaptation as currently configured.
#> Chain 2:          Reducing each adaptation stage to 15%/75%/10% of
#> Chain 2:          the given number of warmup iterations:
#> Chain 2:            init_buffer = 7
#> Chain 2:            adapt_window = 38
#> Chain 2:            term_buffer = 5
#> Chain 2: 
#> Chain 2: Iteration:  1 / 100 [  1%]  (Warmup)
#> Chain 2: Iteration: 10 / 100 [ 10%]  (Warmup)
#> Chain 2: Iteration: 20 / 100 [ 20%]  (Warmup)
#> Chain 2: Iteration: 30 / 100 [ 30%]  (Warmup)
#> Chain 2: Iteration: 40 / 100 [ 40%]  (Warmup)
#> Chain 2: Iteration: 50 / 100 [ 50%]  (Warmup)
#> Chain 2: Iteration: 51 / 100 [ 51%]  (Sampling)
#> Chain 2: Iteration: 60 / 100 [ 60%]  (Sampling)
#> Chain 2: Iteration: 70 / 100 [ 70%]  (Sampling)
#> Chain 2: Iteration: 80 / 100 [ 80%]  (Sampling)
#> Chain 2: Iteration: 90 / 100 [ 90%]  (Sampling)
#> Chain 2: Iteration: 100 / 100 [100%]  (Sampling)
#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.005 seconds (Warm-up)
#> Chain 2:                0.003 seconds (Sampling)
#> Chain 2:                0.008 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'constant_no_seroreversion' NOW (CHAIN 3).
#> Chain 3: Rejecting initial value:
#> Chain 3:   Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3:   Stan can't start sampling from this initial value.
#> Chain 3: Rejecting initial value:
#> Chain 3:   Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 3:   Stan can't start sampling from this initial value.
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.9e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
#> Chain 3: 
#> Chain 3: WARNING: There aren't enough warmup iterations to fit the
#> Chain 3:          three stages of adaptation as currently configured.
#> Chain 3:          Reducing each adaptation stage to 15%/75%/10% of
#> Chain 3:          the given number of warmup iterations:
#> Chain 3:            init_buffer = 7
#> Chain 3:            adapt_window = 38
#> Chain 3:            term_buffer = 5
#> Chain 3: 
#> Chain 3: Iteration:  1 / 100 [  1%]  (Warmup)
#> Chain 3: Iteration: 10 / 100 [ 10%]  (Warmup)
#> Chain 3: Iteration: 20 / 100 [ 20%]  (Warmup)
#> Chain 3: Iteration: 30 / 100 [ 30%]  (Warmup)
#> Chain 3: Iteration: 40 / 100 [ 40%]  (Warmup)
#> Chain 3: Iteration: 50 / 100 [ 50%]  (Warmup)
#> Chain 3: Iteration: 51 / 100 [ 51%]  (Sampling)
#> Chain 3: Iteration: 60 / 100 [ 60%]  (Sampling)
#> Chain 3: Iteration: 70 / 100 [ 70%]  (Sampling)
#> Chain 3: Iteration: 80 / 100 [ 80%]  (Sampling)
#> Chain 3: Iteration: 90 / 100 [ 90%]  (Sampling)
#> Chain 3: Iteration: 100 / 100 [100%]  (Sampling)
#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.005 seconds (Warm-up)
#> Chain 3:                0.003 seconds (Sampling)
#> Chain 3:                0.008 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'constant_no_seroreversion' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.9e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4: 
#> Chain 4: 
#> Chain 4: WARNING: There aren't enough warmup iterations to fit the
#> Chain 4:          three stages of adaptation as currently configured.
#> Chain 4:          Reducing each adaptation stage to 15%/75%/10% of
#> Chain 4:          the given number of warmup iterations:
#> Chain 4:            init_buffer = 7
#> Chain 4:            adapt_window = 38
#> Chain 4:            term_buffer = 5
#> Chain 4: 
#> Chain 4: Iteration:  1 / 100 [  1%]  (Warmup)
#> Chain 4: Iteration: 10 / 100 [ 10%]  (Warmup)
#> Chain 4: Iteration: 20 / 100 [ 20%]  (Warmup)
#> Chain 4: Iteration: 30 / 100 [ 30%]  (Warmup)
#> Chain 4: Iteration: 40 / 100 [ 40%]  (Warmup)
#> Chain 4: Iteration: 50 / 100 [ 50%]  (Warmup)
#> Chain 4: Iteration: 51 / 100 [ 51%]  (Sampling)
#> Chain 4: Iteration: 60 / 100 [ 60%]  (Sampling)
#> Chain 4: Iteration: 70 / 100 [ 70%]  (Sampling)
#> Chain 4: Iteration: 80 / 100 [ 80%]  (Sampling)
#> Chain 4: Iteration: 90 / 100 [ 90%]  (Sampling)
#> Chain 4: Iteration: 100 / 100 [100%]  (Sampling)
#> Chain 4: 
#> Chain 4:  Elapsed Time: 0.005 seconds (Warm-up)
#> Chain 4:                0.003 seconds (Sampling)
#> Chain 4:                0.008 seconds (Total)
#> Chain 4: 
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> 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
summarise_seromodel(seromodel, veev2012)
#> Warning: Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details.
#> $model_name
#> [1] "constant_no_seroreversion"
#> 
#> $elpd_loo
#> [1] "-23.3(se=11.9)"
#> 
#> $foi
#> [1] "0.083(95% CI, 0.065-0.11)"
#> 
#> $foi_rhat
#> [1] 1
#> 
#> $converged
#> [1] "yes"
#>