Diagnose Test Robustness — diagnose_test

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,
  ...
)

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

Value

A data frame with test results across different sample sizes

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)
#> 
Computing: [========================================] 100% (done)                         
#> 
Computing: [========================================] 100% (done)                         
#> 
Computing: [========================================] 100% (done)                         
#> 
Computing: [========================================] 100% (done)                         
#> 
Computing: [========================================] 100% (done)                         
#> 
Computing: [========================================] 100% (done)