adjust_pvalues: Compute Fisher-Adjusted P-Values for Multiple Testing

In ymabene/Counternull: Randomization-Based Inference

Compute Fisher-Adjusted P-Values for Multiple Testing

Description

Adjusts p-values obtained from multiple comparisons. Computes Fisher-Adjusted P-Values utilizing randomization-based method (Lee et al., 2017).

Usage

adjust_pvalues(ls, bw = NULL)

Arguments

ls	List of "null_rand" objects
bw	Histogram bin width (optional)

Details

Argument "ls" must have a "null_rand" object for each p-value that needs to be adjusted.

Function plots joint p-value distribution.

Value

Vector with adjusted p-values

References

\Sexpr[results=rd]{tools:::Rd_expr_doi("10.5705/ss.202016.0116")}

Examples

y = sample_data$turn_angle
w = sample_data$w
n_one = create_null_rand(y, w, sample_matrix, test_stat = c("t"))
y = sample_data$turn_angle
w = sample_data$w
fun = function(x,y){
  return(invisible(ks.test(x,y)$statistic))
}
n_two = create_null_rand(y, w, sample_matrix, fun = fun,
alternative = c("greater"))
adjust_pvalues(list(n_one,n_two))

ymabene/Counternull documentation built on Feb. 25, 2024, 10:18 a.m.