Data Driven Smooth Test for Two-Sample Problem

Performs data driven smooth test for the classical two-sample problem. It is a special case of data driven test for k-samples. Detailed description of the test statistic is provided in Wylupek (2010).

Usage

ddst.twosample.test(
  x,
  y,
  d.N = 12,
  c = 2,
  B = 1e+05,
  compute.p = TRUE,
  alpha = 0.05,
  compute.cv = TRUE
)

Arguments

x

a (non-empty) numeric vector of data

y

a (non-empty) numeric vector of data

d.N

an integer specifying the maximum dimension considered, only for advanced users

c

a calibrating parameter in the penalty in the model selection rule

B

an integer specifying the number of runs for a p-value and a critical value computation if any

compute.p

a logical value indicating whether to compute a p-value or not

alpha

a significance level

compute.cv

a logical value indicating whether to compute a critical value corresponding to the significance level alpha or not

References

Data-driven k-sample tests. Wylupek (2010) https://www.jstor.org/stable/40586684?seq=1

Examples

set.seed(7)
# H0 is true
x <- runif(80)
y <- runif(80)
t <- ddst.twosample.test(x, y)
t
#> 
#> 	Data Driven Smooth Test for Two-Sample Problem
#> 
#> data:  listxy, c: 2, d.N: 12
#> WT = 0.108, T = (1, 1)
#> 
plot(t)


# H0 is false
x <- runif(80)
y <- rexp(80, 1)
t <- ddst.twosample.test(x, y)
t
#> 
#> 	Data Driven Smooth Test for Two-Sample Problem
#> 
#> data:  listxy, c: 2, d.N: 12
#> WT = 22.785, T = (3, 3)
#> 
plot(t)