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).

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

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)
#> Error in ddst.ksample.Nk(x.vector, n, d_N = d.n, c = c): object 'd.n' not found
t
#> function (x) 
#> UseMethod("t")
#> <bytecode: 0x7fa25a157eb0>
#> <environment: namespace:base>
plot(t)

# H0 is false
x <- runif(80)
y <- rexp(80, 1)
t <- ddst.twosample.test(x, y)
#> Error in ddst.ksample.Nk(x.vector, n, d_N = d.n, c = c): object 'd.n' not found
t
#> function (x) 
#> UseMethod("t")
#> <bytecode: 0x7fa25a157eb0>
#> <environment: namespace:base>
plot(t)