ddst.twosample.test | R Documentation |
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
)
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 |
Data-driven k-sample tests. Wylupek (2010) https://www.jstor.org/stable/40586684?seq=1
set.seed(7)
# H0 is true
x <- runif(80)
y <- runif(80)
t <- ddst.twosample.test(x, y)
t
plot(t)
# H0 is false
x <- runif(80)
y <- rexp(80, 1)
t <- ddst.twosample.test(x, y)
t
plot(t)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.