Skip to contents

Run Rao-Scott Adjusted Cochran-Armitage Trend Test for a Specific Injury Threshold

Source: R/RSCABS_AO.R
run_threshold_RSCA.Rd

This function performs the Rao-Scott adjusted Cochran-Armitage trend test for a specific injury threshold, counting fish with injuries at or above that threshold as "affected".

Usage

run_threshold_RSCA(
  data,
  threshold,
  score_cols = NULL,
  treatment_col = "tmt",
  replicate_col = "tank",
  total_col = "total",
  direction = "greater",
  alternative = "two.sided"
)

Arguments

data

A data frame containing fish injury data

threshold

Numeric value indicating the injury threshold (e.g., 1 for S1+)

score_cols

Character vector of column names containing injury scores (default: NULL, auto-detected)

treatment_col

Name of the column containing treatment groups (default: "tmt")

replicate_col

Name of the column containing tank/replicate IDs (default: "tank")

total_col

Name of the column containing total counts (default: "total")

direction

Character string indicating threshold direction: "greater" for \ge threshold, "less" for \le threshold (default: "greater")

alternative

Character string specifying the alternative hypothesis: "two.sided" (default), "greater" (proportion increases with treatment level), or "less" (proportion decreases with treatment level)

Value

A list containing:

threshold

The numeric threshold value

threshold_label

Character label for the threshold (e.g., "S1+")

Z

Z-statistic from the RSCA test

p_value

P-value based on the specified alternative hypothesis

alternative

The alternative hypothesis used

interm_values

Intermediate values from the Rao-Scott adjustment

has_zero_counts

Logical indicating if any treatment group had zero affected individuals

affected_counts

Data frame showing affected counts by treatment group

Examples

# Example data
fish_data <- data.frame(
  tmt = rep(c("Control", "Low", "Medium", "High"), each = 3),
  tank = paste0(rep(c("Control", "Low", "Medium", "High"), each = 3),
                rep(1:3, times = 4)),
  S0 = c(8,7,9, 6,5,7, 4,5,3, 2,3,1),
  S1 = c(2,2,1, 3,4,2, 4,3,5, 4,3,5),
  S2 = c(0,1,0, 1,1,1, 2,2,1, 3,3,3),
  S3 = c(0,0,0, 0,0,0, 0,0,1, 1,1,1),
  S4 = c(0,0,0, 0,0,0, 0,0,0, 0,0,0),
  total = rep(10, 12)
)

# Two-sided test for trend in S2+ injuries
result_two_sided <- run_threshold_RSCA(fish_data, threshold = 2)

# One-sided test (proportion increases with treatment level)
result_greater <- run_threshold_RSCA(fish_data, threshold = 2,
                                    alternative = "greater")

# One-sided test (proportion decreases with treatment level)
result_less <- run_threshold_RSCA(fish_data, threshold = 2,
                                 alternative = "less")