simulate_data: Simulate full data set

In seroanalytics/serosolver: Inference Framework for Serological Data

Simulate full data set

Description

Simulates a full data set for a given set of parameters etc.

Usage

simulate_data(
  par_tab,
  group = 1,
  n_indiv = 100,
  buckets = 1,
  antigenic_map = NULL,
  strain_isolation_times = NULL,
  measured_strains = NULL,
  sampling_times,
  nsamps = 2,
  titre_sensoring = 0,
  age_min = 5,
  age_max = 80,
  attack_rates,
  repeats = 1,
  mu_indices = NULL,
  measurement_indices = NULL,
  add_noise = TRUE
)

Arguments

par_tab	the full parameter table controlling parameter ranges and values
group	which group index to give this simulated data
n_indiv	number of individuals to simulate
buckets	time resolution of the simulated data. buckets=1 indicates annual time resolution; buckets=4 indicates quarterly; buckets=12 monthly
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
strain_isolation_times	(optional) If no antigenic map is specified, this argument gives the vector of times at which individuals can be infected
measured_strains	vector of strains that have titres measured matching entries in strain_isolation_times
sampling_times	possible sampling times for the individuals, matching entries in strain_isolation_times
nsamps	the number of samples each individual has (eg. nsamps=2 gives each individual 2 random sampling times from sampling_times)
titre_sensoring	numeric between 0 and 1, used to censor a proportion of titre observations at random (MAR)
age_min	simulated age minimum
age_max	simulated age maximum
attack_rates	a vector of attack_rates for each entry in strain_isolation_times to be used in the simulation (between 0 and 1)
repeats	number of repeat observations for each year
mu_indices	default NULL, optional vector giving the index of 'mus' that each strain uses the boosting parameter from. eg. if there are 6 circulation years in strain_isolation_times and 3 strain clusters, then this might be c(1,1,2,2,3,3)
measurement_indices	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, then this might be c(1,1,2,2,3,3)
add_noise	if TRUE, adds observation noise to the simulated titres

Value

a list with: 1) the data frame of titre data as returned by simulate_group; 2) a matrix of infection histories as returned by simulate_infection_histories; 3) a vector of ages

See Also

Other simulation_functions: simulate_attack_rates(), simulate_group(), simulate_individual_faster(), simulate_individual(), simulate_infection_histories()

Examples

data(example_par_tab)
data(example_antigenic_map)

## Times at which individuals can be infected
strain_isolation_times <- example_antigenic_map$inf_times
## Simulate some random attack rates between 0 and 0.2
attack_rates <- runif(length(strain_isolation_times), 0, 0.2)
## Vector giving the circulation times of measured strains
sampled_viruses <- seq(min(strain_isolation_times), max(strain_isolation_times), by=2)
all_simulated_data <- simulate_data(par_tab=example_par_tab, group=1, n_indiv=50,    
                                   strain_isolation_times=strain_isolation_times,
                                   measured_strains=sampled_viruses,
                                   sampling_times=2010:2015, nsamps=2, antigenic_map=example_antigenic_map, 
                                   age_min=10,age_max=75,
                                   attack_rates=attack_rates, repeats=2)
titre_dat <- all_simulated_data$data
titre_dat <- merge(titre_dat, all_simulated_data$ages)

seroanalytics/serosolver documentation built on April 24, 2023, 9:52 a.m.