Calculates ici-kendall-tau

Usage

ici_kt(
  x,
  y,
  perspective = "local",
  alternative = "two.sided",
  continuity = FALSE,
  output = "simple"
)

Arguments

x: numeric vector
y: numeric vector
perspective: should we consider the "local" or "global" perspective?
alternative: what is the alternative for the p-value test?
continuity: logical: if true, a continuity correction is used
output: used to control reporting of values for debugging

Value

kendall tau correlation, p-value, max-correlation

Details

Calculates the information-content-informed Kendall-tau correlation measure. This correlation is based on concordant and discordant ranked pairs, like Kendall-tau, but also includes missing values (as NA). Missing values are assumed to be primarily due to lack of detection due to instrumental sensitivity, and therefore encode some information.

For more details see the ICI-Kendall-tau vignette:

browseVignettes("ICIKendallTau")

Examples

x = sort(rnorm(100))
y = x + 1
y2 = y
y2[1:10] = NA
ici_kt(x, y)
#>          tau       pvalue      tau_max 
#> 1.000000e+00 3.480281e-49 1.000000e+00 
ici_kt(x, y2, "global")
#>          tau       pvalue      tau_max 
#> 9.954442e-01 2.228308e-48 9.954442e-01 
ici_kt(x, y2)
#>          tau       pvalue      tau_max 
#> 9.954442e-01 2.228308e-48 9.954442e-01