Optimize sequencing allocation across strata

Given fixed total sequencing capacity, finds the optimal allocation across strata that minimizes a specified objective function.

Usage

# S3 method for class 'surv_allocation'
print(x, ...)

# S3 method for class 'surv_allocation'
as.data.frame(x, ...)

surv_optimize_allocation(
  design,
  objective = c("min_mse", "max_detection", "min_imbalance"),
  total_capacity,
  budget = NULL,
  min_per_stratum = 2L,
  target_lineage = NULL,
  target_prevalence = 0.01,
  cost_col = NULL
)

Arguments

x

Object to print.

...

Additional arguments (unused).

design

A surv_design object.

objective

Character. One of "min_mse", "max_detection", or "min_imbalance".

total_capacity

Integer. Total sequences available.

budget

Numeric or NULL. Optional budget constraint.

min_per_stratum

Integer. Minimum per stratum. Default 2.

target_lineage

Character. Required for "max_detection".

target_prevalence

Numeric. Assumed prevalence for detection. Default 0.01.

cost_col

Character or NULL. Column name for per-sequence cost.

Value

Invisibly returns the input object.

A surv_allocation object.

Examples

sim <- surv_simulate(n_regions = 4, n_weeks = 10, seed = 1)
d <- surv_design(sim$sequences, ~ region,
                 sim$population[c("region", "seq_rate")], sim$population)
a <- surv_optimize_allocation(d, "min_mse", total_capacity = 500)
print(a)
#> ── Optimal Sequencing Allocation ───────────────────────────────────────────────
#> Objective: min_mse
#> Total capacity: 500 sequences
#> Strata: 4
#> 
#> # A tibble: 4 × 3
#>   region   n_allocated proportion
#>   <chr>          <int>      <dbl>
#> 1 Region_A          44      0.088
#> 2 Region_B         178      0.356
#> 3 Region_C         176      0.352
#> 4 Region_D         102      0.204