Apply two-sample KS test to all pairs of datasets contained within a dataframe

Usage

ks_compare(df, correct_multiple_comparisons = TRUE)

Arguments

df: A dataframe of datasets with columns: name and time, one unique name per dataset
correct_multiple_comparisons: If TRUE, an adjustment will be made to the p-values based on Holm, 1979, A simple sequentially rejective multiple test procedure

Value

A dataframe with columns name1, name2, D, and p_value

Examples

data <- prepare_data(dplyr::filter(
  carpenter_williams_1995,
  participant == "b"
))
ks_compare(data)
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#> Warning: p-value will be approximate in the presence of ties
#>        name1 name2          D       p_value
#> D...1  b_p05 b_p10 0.24236774  8.242450e-16
#> D...2  b_p05 b_p25 0.53065300  8.104245e-80
#> D...3  b_p05 b_p50 0.66043494 1.079212e-148
#> D...4  b_p05 b_p75 0.70992975 2.392157e-192
#> D...5  b_p05 b_p90 0.77235259 8.563762e-253
#> D...6  b_p05 b_p95 0.80149617 7.009049e-279
#> D...7  b_p10 b_p25 0.31689876  2.448127e-34
#> D...8  b_p10 b_p50 0.48014114  4.476983e-99
#> D...9  b_p10 b_p75 0.55927635 1.687271e-155
#> D...10 b_p10 b_p90 0.62695131 1.475795e-224
#> D...11 b_p10 b_p95 0.67768643 3.722007e-271
#> D...12 b_p25 b_p50 0.19463212  2.924255e-18
#> D...13 b_p25 b_p75 0.27841580  1.222154e-43
#> D...14 b_p25 b_p90 0.36795184  1.700954e-89
#> D...15 b_p25 b_p95 0.44470214 4.947388e-136
#> D...16 b_p50 b_p75 0.09136495  1.578924e-07
#> D...17 b_p50 b_p90 0.20267955  2.773457e-44
#> D...18 b_p50 b_p95 0.27838393  2.761189e-89
#> D...19 b_p75 b_p90 0.12102841  8.414813e-24
#> D...20 b_p75 b_p95 0.19784556  2.732080e-70
#> D...21 b_p90 b_p95 0.08168901  1.980667e-22