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library(DataFusionGDM)
# Generate a full matrix in memory (no package file I/O)
full <- simulate_genetic_distances(n_pops = 40, verbose = FALSE, seed = 233)$distance_matrix
summary_table <- data.frame()
for (k in 1:3) {
for (bs_i in 1:3) {
res <- besmi_create_masked_matrices(full, k, seed = 100 + 10 * k + bs_i)
base_dir <- tempdir()
dir.create(file.path(base_dir, "training_set"), recursive = TRUE, showWarnings = FALSE)
training_path <- sprintf("%s", file.path(base_dir, sprintf("training_set/masked_k%d_bs%d.rds", k, bs_i)))
saveRDS(res$masked_matrix, training_path)
summary_table <- rbind(summary_table, data.frame(k = k, bs = bs_i, training_path = training_path))
}
}
paths <- summary_table$training_path
output_dir <- file.path(tempdir(), "imputation_set")
metrics <- besmi_batch_impute(paths, the_method = "midastouch", max_iter = 3,
imputation_convergence_threshold = 1e-3, propagation_convergence_threshold = 1e-3,
distance_metric = "mae", output_dir = output_dir)
head(metrics)These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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