Plots force-of-infection central estimates

Usage

plot_foi_estimates(
  seromodel,
  serosurvey,
  alpha = 0.05,
  foi_df = NULL,
  foi_max = NULL,
  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

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

size_text

Size of text for plotting (base_size in ggplot2)

Value

ggplot object with estimated force-of-infection

Examples

data(chagas2012)
seromodel <- fit_seromodel(
  serosurvey = chagas2012,
  model_type = "time",
  foi_index = data.frame(
    year = 1935:2011,
    foi_index = c(rep(1, 46), rep(2, 31))
  ),
  iter = 100,
  chains = 2
)
#> 
#> SAMPLING FOR MODEL 'time_no_seroreversion' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 0.000591 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 5.91 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: 0.435 seconds (Warm-up)
#> Chain 1:                0.443 seconds (Sampling)
#> Chain 1:                0.878 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'time_no_seroreversion' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 0.000589 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 5.89 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: 0.378 seconds (Warm-up)
#> Chain 2:                0.455 seconds (Sampling)
#> Chain 2:                0.833 seconds (Total)
#> Chain 2: 
#> Warning: The largest R-hat is 1.13, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> 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_foi_estimates(seromodel, chagas2012)