All Classification Data

This function runs the classification algorithm for all possible sensitivity and specificity options.

Usage

renderClassificationTable(mfi_to_rau_output, algorithm_type, qc_results)

Arguments

mfi_to_rau_output

Output from `MFItoRAU()` or `MFItoRAU_Adj()`.

algorithm_type

User-selected algorithm choice: - "antibody_model" (PvSeroTaT model; default), or - "antibody_model_excLF016" (PvSeroTat excluding LF016).

qc_results

Output from `runQC()`.

Value

A table of all classification outputs.

Author

Dionne Argyropoulos

Examples

# \donttest{

# Step 0: Load in Raw Data
your_raw_data <- c(
  system.file("extdata", "example_MAGPIX_plate1.csv", package = "SeroTrackR"),
  system.file("extdata", "example_MAGPIX_plate2.csv", package = "SeroTrackR")
)
your_plate_layout <- system.file("extdata", "example_platelayout_1.xlsx", package = "SeroTrackR")

# Step 1: Reading in Raw Data
sero_data     <- readSeroData(raw_data = your_raw_data, "magpix")
#> PASS: File example_magpix_plate1.csv successfully validated.
#> PASS: File example_magpix_plate2.csv successfully validated.
plate_list    <- readPlateLayout(
  plate_layout = your_plate_layout,
  sero_data = sero_data
)
#> Plate layouts correctly identified!

# Step 2: Quality Control and MFI to RAU
qc_results <- runQC(sero_data, plate_list)

# Step 4: Run MFI to RAU (e.g., using ETH beads)
mfi_to_rau_output  <- MFItoRAU_Adj(sero_data, plate_list, qc_results)
#> Joining with `by = join_by(antigen)`
#> Joining with `by = join_by(antigen)`
#> Joining with `by = join_by(antigen)`
#> Joining with `by = join_by(antigen)`
#> Joining with `by = join_by(antigen)`
#> Joining with `by = join_by(antigen)`

# Step 5: Render classification table
renderClassificationTable(
  mfi_to_rau_output = mfi_to_rau_output,
  algorithm_type = "antibody_model",
  qc_results = qc_results
)
#> # A tibble: 7 × 3
#> # Groups:   Sensitivity/Specificity [7]
#>   `Sensitivity/Specificity` Seropositive Seronegative
#>   <chr>                            <int>        <int>
#> 1 85% sensitivity                    158           10
#> 2 85% specificity                    125           43
#> 3 90% sensitivity                    160            8
#> 4 90% specificity                     92           76
#> 5 95% sensitivity                    160            8
#> 6 95% specificity                     65          103
#> 7 balanced                           150           18

# }