myTAI (version 2.3.4)

diagnose_test_robustness: Diagnose Test Robustness

Description

Evaluate the robustness of conservation tests across different sample sizes for null distribution generation.

Usage

diagnose_test_robustness(
  test,
  phyex_set,
  sample_sizes = c(500, 1000, 5000, 10000),
  plot_result = TRUE,
  num_reps = 5,
  ...
)

Value

A data frame with test results across different sample sizes

Arguments

test

Function representing the conservation test to evaluate

phyex_set

A PhyloExpressionSet object

sample_sizes

Numeric vector of sample sizes to test (default: c(500, 1000, 5000, 10000))

plot_result

Logical indicating whether to plot results (default: TRUE)

num_reps

Number of replicates for each sample size (default: 5)

...

Additional arguments passed to the test function

Details

This function assesses how consistent test results are across different sample sizes for null distribution generation, helping to determine appropriate sample sizes for reliable testing.

Examples

# Diagnose flatline test robustness
robustness <- diagnose_test_robustness(stat_flatline_test, 
                                       example_phyex_set,
                                       sample_sizes=c(10,20),
                                       plot_result=FALSE,
                                       num_reps=3)