plot_antibody_predictions: Plots model predicted titers against observations

In seroanalytics/serosolver: Inference Framework for Serological Data

Plots model predicted titers against observations

Description

Plots model predicted titers against observations

Usage

plot_antibody_predictions(
  chain,
  infection_histories,
  antibody_data = NULL,
  demographics = NULL,
  par_tab = NULL,
  antigenic_map = NULL,
  possible_exposure_times = NULL,
  nsamp = 1000,
  measurement_bias = NULL,
  data_type = 1,
  start_level = "none",
  settings = NULL
)

Arguments

chain	the full MCMC chain to generate antibody level trajectories from
infection_histories	the MCMC chain for infection histories
antibody_data	the data frame of antibody level data
par_tab	the table controlling the parameters in the MCMC chain
antigenic_map	(optional) a data frame of antigenic x and y coordinates. Must have column names: x_coord; y_coord; inf_times. 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
nsamp	number of samples to take from posterior
measurement_bias	default NULL, optional data frame giving the index of ‘rho' that each biomarker_id and biomarker_group which uses the measurement shift from from. eg. if there’s 6 circulation years and 3 strain clusters
data_type	integer, currently accepting 1 or 2. Set to 1 for discretized, bounded data, or 2 for continuous, bounded data.
settings	if not NULL, list of serosolver settings as returned from the main serosolver function

Value

a list with:

• a data frame with all posterior estimates for each observation;

• the proportion of observations captured by the 95

• a histogram comparing posterior median estimates to the observed data (note, this can be misleading for continuous data due to the zero-inflated observation model);

• a histogram comparing random posterior draws to the observed data (can be more reliable than posterior medians);

• comparison of observations and all posterior medians and 95

See Also

Other infection_history_plots: calculate_infection_history_statistics(), plot_antibody_data(), plot_cumulative_infection_histories(), plot_estimated_antibody_model(), plot_individual_number_infections(), plot_infection_history_chains_indiv(), plot_infection_history_chains_time(), plot_model_fits(), plot_posteriors_infhist(), plot_total_number_infections()

seroanalytics/serosolver documentation built on Dec. 14, 2024, 5:33 a.m.