Particle analysis validation metrics — crowd

Helpers for assessing particle crowding, spike recovery, minimum detectable amount (MDA), and batch detection limit (BDL) from particle-count tables.

Usage

crowd_lookup(
  x,
  sample_col = "sample_id",
  area_col = "area_um2",
  size_col = "min_length_um",
  material_col = NULL,
  group_cols = sample_col,
  size_threshold = 500,
  surface_area = NULL,
  simulations = 10000,
  seed = NULL,
  na.rm = TRUE
)

recovery_rate(
  x,
  observed_col = "count",
  expected_col = "total_spiked",
  group_cols = NULL,
  pre_recovered_col = NULL,
  na.rm = TRUE
)

minimum_detectable_amount(
  x,
  count_col = "count",
  group_cols = NULL,
  offset = 3,
  md_multiplier = 3.29,
  bdl_multiplier = 4.65,
  spike_replicates = 4,
  round = c("integer", "ceiling", "none"),
  na.rm = TRUE
)

batch_detection_limit(
  x,
  count_col = NULL,
  offset = 3,
  multiplier = 4.65,
  round = c("integer", "ceiling", "none"),
  ...
)

Arguments

x: a data frame or data table.
sample_col: column identifying samples.
area_col: column containing particle area.
size_col: optional column containing particle size. If NULL, size is inferred as sqrt(area_col).
material_col: optional material column to include in crowding groups.
group_cols: columns used for grouped summaries.
size_threshold: particles larger than this size are assessed for possible crowding.
surface_area: optional analyzed surface area used to calculate percent area covered.
simulations: number of simulated small-particle cumulative-area draws.
seed: optional random seed for reproducible crowding simulations.
na.rm: logical; remove missing values from summaries?
observed_col, expected_col: columns with observed and expected spike counts.
pre_recovered_col: optional column with particles recovered before the automated analysis.
count_col: column with blank counts.
offset, md_multiplier, bdl_multiplier: numeric constants used in MDA and BDL formulas.
spike_replicates: number of spike replicates in the MDA formula.
round: one of "integer", "ceiling", or "none" for detection-limit rounding. "integer" matches the historic workflow's as.integer().
multiplier: numeric multiplier used by batch_detection_limit().
...: reserved for future extensions.

Value

A data.table containing the requested summary.

Examples

blanks <- data.frame(sample_id = c("b1", "b2", "b3", "b4"),
                     area_bins = "(0,212]",
                     count = c(0, 0, 0, 11))
minimum_detectable_amount(blanks, group_cols = "area_bins")
#>    area_bins n_blanks method   MDA
#>       <char>    <int> <char> <int>
#> 1:   (0,212]        4    MDA    25
batch_detection_limit(11)
#> [1] 29