plot_model_fits: Plots infection histories and antibody model

In seroanalytics/serosolver: Inference Framework for Serological Data

Plots infection histories and antibody model

Description

Given outputs from an MCMC run and the data used for fitting, generates an NxM matrix of plots where N is the number of individuals to be plotted and M is the range of sampling times. Where data are available, plots the observed antibody measurements and model predicted trajectories. Unlike plot_infection_histories_cross_sectional, places biomarker\_id on the x-axis and facets by sample time and individual.

Usage

plot_model_fits(
  chain,
  infection_histories,
  antibody_data = NULL,
  demographics = NULL,
  individuals,
  par_tab = NULL,
  antigenic_map = NULL,
  possible_exposure_times = NULL,
  nsamp = 1000,
  known_infection_history = NULL,
  measurement_bias = NULL,
  p_ncol = max(1, floor(length(individuals)/2)),
  data_type = 1,
  expand_to_all_times = FALSE,
  orientation = "cross-sectional",
  subset_biomarker_ids = NULL,
  subset_biomarker_groups = NULL,
  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
individuals	the subset of individuals to generate credible intervals for
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
known_infection_history	nxm matrix of known infection histories
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
p_ncol	integer giving the number of columns of subplots to create if using orientation = "longitudinal"
data_type	integer, currently accepting 1 or 2. Set to 1 for discretized, bounded data, or 2 for continuous, bounded data.
expand_to_all_times	TRUE/FALSE value. If TRUE, solves antibody level predictions for all possible infection times (i.e., for the range in possible_exposure_times). If left FALSE, then only solves for the infections times at which a antibody level against the circulating biomarker_id was measured in antibody_data.
orientation	either "cross-sectional" or "longitudinal"
subset_biomarker_ids	if not NULL, then a vector giving the entries of biomarker_id to include in the longitudinal plot
settings	if not NULL, list of serosolver settings as returned from the main serosolver function

Value

a ggplot2 object

See Also

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

Examples

## Not run: 
data(example_theta_chain)
data(example_inf_chain)
data(example_antibody_data)
data(example_antigenic_map)
data(example_par_tab)

model_fit_plot <- plot_model_fits(example_theta_chain, example_inf_chain, example_antibody_data, 
                                           1:10, example_antigenic_map, example_par_tab,orientation="longitudinal")

## End(Not run)

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