Fit a standard curve to known mfi and dilution values.

We wish to convert the standard curve samples to a five parameter logistic curve. This function takes those values and calls optim to determine the fit.

Usage

.fit_standard_curve(mfi, dilution, control = NULL)

Arguments

mfi

Known mfi of samples

dilution

Known dilution of samples

control

Optional list of control parameters for the underlying call to optim.

Value

standard curve log logistic

Author

Eamon Conway

Examples

# This function is typically called within data-processing workflows.
# Workflow-style example (not run on CRAN)

# \donttest{

# This block demonstrates how .fit_standard_curve() is typically used
# inside the MFItoRAU_Adj-conversion pipeline.

# Step 1 — Prepare master file (normally from readSeroData)
master_file <- data.frame(
  Location = c("A1","A2","A3"),
  Sample   = c("S1","S2","S3"),
  Plate    = c("Plate1","Plate1","Plate1"),
  Ag1 = c(12000, 8000, 4000),
  Ag2 = c(9000,  5000, 2500)
)

# Convert antigen columns to numeric
L <- master_file |>
  dplyr::mutate(dplyr::across(-c(Location, Sample, Plate), as.numeric))

# Fake plate layout (normally from readPlateLayout)
layout <- list(Plate1 = data.frame(Location = c("A1","A2","A3"), WellType = "STD"))


# Step 2 — Load reference standard curve MFI values (dummy data)
refs <- data.frame(
  std_plate = rep("StdPlate1", 5),
  antigen   = rep("Ag1", 5),
  dilution  = c(1, 1/2, 1/4, 1/8, 1/16),
  eth_mfi   = c(14000, 7000, 3500, 1800, 900),
  png_mfi   = c(15000, 7600, 3800, 1900, 950)
)


# Step 3 — Define optimisation settings
control <- list(
  maxit  = 10000,
  abstol = 1e-8,
  reltol = 1e-6
)


# Step 4 — Fit ETH and PNG curves per standard-plate × antigen
ref_fit <- refs |>
  dplyr::group_by(.data$std_plate, .data$antigen) |>
  tidyr::nest() |>
  dplyr::mutate(
    eth_fit = purrr::map(data, ~ .fit_standard_curve(.x$eth_mfi, .x$dilution, control)),
    png_fit = purrr::map(data, ~ .fit_standard_curve(.x$png_mfi, .x$dilution, control))
  )

ref_fit
#> # A tibble: 1 × 5
#> # Groups:   std_plate, antigen [1]
#>   std_plate antigen data             eth_fit   png_fit  
#>   <chr>     <chr>   <list>           <list>    <list>   
#> 1 StdPlate1 Ag1     <tibble [5 × 3]> <dbl [7]> <dbl [7]>
# }