get_antibody_level_predictions: Generate antibody level credible intervals
In seroanalytics/serosolver: Inference Framework for Serological Data

get_antibody_level_predictions

R Documentation

Generate antibody level credible intervals

Description

Generates credible intervals on antibody levels and infection histories from an MCMC chain output.

Usage

get_antibody_level_predictions(
  chain,
  infection_histories,
  antibody_data,
  individuals,
  antigenic_map = NULL,
  possible_exposure_times = NULL,
  par_tab,
  nsamp = 1000,
  add_residuals = FALSE,
  measurement_indices_by_time = NULL,
  for_res_plot = FALSE,
  expand_antibody_data = FALSE,
  expand_to_all_times = FALSE,
  antibody_level_before_infection = FALSE,
  for_regression = FALSE,
  data_type = 1,
  start_level = "none"
)

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
`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
`par_tab`	the table controlling the parameters in the MCMC chain
`nsamp`	number of samples to take from posterior
`add_residuals`	if true, returns an extra output summarising residuals between the model prediction and data
`measurement_indices_by_time`	default NULL, optional vector giving the index of ‘measurement_bias' that each strain uses the measurement shift from from. eg. if there’s 6 circulation years and 3 strain clusters
`for_res_plot`	TRUE/FALSE value. If using the output of this for plotting of residuals, returns the actual data points rather than summary statistics
`expand_antibody_data`	TRUE/FALSE value. If TRUE, solves antibody level predictions for the entire study period (i.e., between the range of antibody_data$sample_time). 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.
`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.
`antibody_level_before_infection`	TRUE/FALSE value. If TRUE, solves antibody level predictions, but gives the predicted antibody level at a given time point BEFORE any infection during that time occurs.
`for_regression`	if TRUE, returns posterior draws rather than posterior summaries
`data_type`	integer, currently accepting 1 or 2. Set to 1 for discretized, bounded data, or 2 for continuous, bounded data.

Value

a list with the antibody level predictions (95

Examples

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

y <- get_antibody_level_predictions(example_theta_chain, example_inf_chain, example_antibody_data,
                          unique(example_antibody_data$individual), example_antigenic_map,
                          example_par_tab,expand_antibody_data = FALSE)

## End(Not run)

seroanalytics/serosolver documentation built on April 26, 2024, 1:19 a.m.