Plots estimated antibody kinetics model

Source: R/plot_antibody_model.R

Plots the posterior antibody kinetics implied by the fitted model, optionally including prediction intervals for observed measurements.

plot_estimated_antibody_model(
  chain,
  antibody_data = NULL,
  demographics = NULL,
  antigenic_map = NULL,
  possible_exposure_times = NULL,
  par_tab = NULL,
  nsamp = 1000,
  measurement_bias = NULL,
  solve_times = seq(1, 30, by = 1),
  data_type = 1,
  settings = NULL,
  by_group = TRUE,
  add_prediction_intervals = FALSE,
  exponential_waning = NULL,
  set_infections = NULL,
  verbose = FALSE
)

Arguments

chain

the full MCMC chain to generate antibody level trajectories from, usually `chains$theta_chain`

antibody_data

the antibody data frame, with one row per measurement

demographics

optional data frame identifying the demographic group for each individual. This is used when model parameters are stratified by demographics. See the [demographic stratification and covariate vignette](https://seroanalytics.github.io/serosolver/articles/demographics_covariates.html).

antigenic_map

(optional) a data frame of antigenic x and y coordinates. Must have column names: x_coord; y_coord; inf_times. The `inf_times` column identifies the circulation or exposure time represented by each map entry. See example_antigenic_map

possible_exposure_times

(optional) if no antigenic map is specified, this argument gives the vector of times at which individuals can be infected

par_tab

the model control table specifying the parameters in the MCMC chain

nsamp

number of draws to take from the posterior

measurement_bias

default NULL, optional data frame mapping each `biomarker_id` and `biomarker_group` combination to the `rho_index` of the measurement-shift parameter that it uses. See the [advanced features vignette](https://seroanalytics.github.io/serosolver/articles/advanced_features.html).

solve_times

vector of times to solve model over

data_type

numeric or text value: `1` or `"discrete"` for discrete, bounded data; `2` or `"continuous"` for continuous, bounded data; or `3` or `"false_positive"` for continuous data with the false-positive observation model. Supply one value per biomarker group, or one value to use for all groups. For bounded data, the limits are given by `min_measurement` and `max_measurement` in par_tab.

settings

if not NULL, list of serosolver settings as returned from the main serosolver function, such as `res$settings`

by_group

if TRUE, plots separate trajectories for each biomarker ID; otherwise combines the trajectories

add_prediction_intervals

if TRUE, adds intervals for predicted observations as well as the underlying antibody model

exponential_waning

Deprecated compatibility argument. Prefer a fixed `exponential_waning` row in `par_tab`, with `values = 1` and `par_type = 0`.

set_infections

numeric vector giving the corresponding times in `possible_exposure_times` to simulate infections

verbose

if TRUE, prints messages when settings are used or predictions are prepared

Value

a ggplot2 object giving model-predicted antibody level and predicted observations over time since infection