This package provides an implementation of the Adaptive Multiple Importance Sampling algorithm, as described in
Integrating geostatistical maps and infectious disease transmission models using adaptive multiple importance sampling. Renata Retkute, Panayiota Touloupou, María-Gloria Basáñez, T. Deirdre Hollingsworth and Simon E.F. Spencer (2021). Annals of Applied Statistics, 15 (4), 1980-1998. DOI https://doi.org/10.1214/21-AOAS1486
Make sure you have the package devtools installed. Then
devtools::install_github("OxfordRSE/trachomAMIS")The package exports a single function amis that takes a
geostatistical map, a model and returns sampled parameters and their
associated weights.
param_and_weights <- AMISforInfectiousDiseases::amis(geo_map, model, prior, amis_params)geo_map: A matrix representing the geostatistical map, with one row per pixel.model: A function implementing the model. It can be anything, as long as it conforms to a specific interface. See defining a model function.amis_params: A list containing the parameters for the AMIS algorithm.nsamples: The number of sample parameters to draw at each iteration.mixture_samples: The number of samples drawn from the weighted distribution to fit a new mixture to.target_ess: The target effective sample size.max_iters: The maximum number of iterations.delta: The Randon-Nikodym derivative smoothing parameter.log: logical indicating whether to work with log weights.
The amis function expects its argument model_func to be a function with the
following interface
observables <- model_func(seeds, parameters)
parameters: A matrix of parameter values (double)seeds: A vector of seeds (integer)
Function model_func is expected to run the model for each pair
(seed, parameter) and return the corresponding values for the
observable (e.g. infection prevalence). parameter must be a matrix
with ncols equal to the dimension of the parameter space and the output
must be a matrix with ncols equal to the number of observed timepoints.
If your model does not comply with the above interface, you can provide a wrapper function. The following example illustrates how it can be done in the case where the function implementing the model reads and writes values in files and expects several additional parameters to be specified:
wrapped_model <- function(seeds, parameters) {
# write input on disk
input_file <- "beta_values.csv"
write_model_input(seeds, parameters, input_file)
# run model
run_model(input_file, mda_file, output_file, infect_output,
SaveOutput = F, OutSimFilePath = NULL,
InSimFilePath = NULL)
# read results and return obsvervable
res <- read.csv(output_file)
return(100 * res[, dim(res)[2]])
}