Visualise results of the provided model

Usage

plot_seromodel(
  seromodel,
  serosurvey,
  alpha = 0.05,
  bin_serosurvey = FALSE,
  bin_step = 5,
  foi_df = NULL,
  foi_max = NULL,
  loo_estimate_digits = 1,
  central_estimate_digits = 2,
  seroreversion_digits = 2,
  rhat_digits = 2,
  size_text = 11
)

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

bin_serosurvey

If TRUE, serodata is binned by means of prepare_bin_serosurvey. Otherwise, age groups are kept as originally input.

bin_step

Integer specifying the age groups bin size to be used when bin_serosurvey is set to TRUE.

foi_df

Dataframe with columns

year/age: Year/Age (depending on the model)
foi: Force-of-infection values by year/age

foi_max

Max force-of-infection value for plotting

loo_estimate_digits

Number of loo estimate digits

central_estimate_digits

Number of central estimate digits

seroreversion_digits

Number of seroreversion rate digits

rhat_digits

Number of rhat estimate digits

size_text

Size of text for plotting (base_size in ggplot2)

Value

seromodel summary plot

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 2.3e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.23 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: 0.006 seconds (Warm-up)
#> Chain 1:                0.004 seconds (Sampling)
#> Chain 1:                0.01 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'constant_no_seroreversion' NOW (CHAIN 2).
#> 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: 0.004 seconds (Warm-up)
#> Chain 2:                0.003 seconds (Sampling)
#> Chain 2:                0.007 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'constant_no_seroreversion' NOW (CHAIN 3).
#> 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: 0.006 seconds (Warm-up)
#> Chain 3:                0.003 seconds (Sampling)
#> Chain 3:                0.009 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'constant_no_seroreversion' NOW (CHAIN 4).
#> Chain 4: Rejecting initial value:
#> Chain 4:   Log probability evaluates to log(0), i.e. negative infinity.
#> Chain 4:   Stan can't start sampling from this initial value.
#> 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
plot_seromodel(seromodel, veev2012)
#> Warning: Some Pareto k diagnostic values are too high. See help('pareto-k-diagnostic') for details.