serofoi (version 1.0.3)

plot_seromodel: Visualise results of the provided model

Description

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,
  plot_constant = FALSE,
  x_axis = NA
)

Value

seromodel summary plot

Arguments

seromodel

stan_fit object obtained from sampling a model with fit_seromodel

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 FoI 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)

plot_constant

boolean specifying whether to plot single Force-of-Infection estimate and its corresponding rhat value instead of showing this information in the summary. Only relevant when seromodel@model_name == "constant")

x_axis

either "time" or "age". Specifies time axis values label for constant model additional plots. Only relevant when and seromodel@model_name == "constant"

Examples

# \donttest{
data(veev2012)
seromodel <- fit_seromodel(veev2012, iter = 100)
plot_seromodel(seromodel, veev2012)
# }