View source: R/PermutationTest.R
CriticalValue | R Documentation |
Find a critical value by permutation test using variance of kernel (Gini) distance covariance or correlation statistics, in which Xs are quantitative, Y are categorical, sigma is kernel standard deviation, alpha is an exponent on Euclidean distance and returns the critical value of the measures of dependence.
CriticalValue(x, y, sigma, alpha, level, M = 1000, method)
x |
data |
y |
label of data or univariate response variable |
sigma |
kernel standard deviation |
alpha |
exponent on Euclidean distance, in (0,2] |
level |
significance level of the test, the default value = 0.05 |
M |
number of permutations |
method |
string name of the method for permutation test, e.g. gCov |
CriticalValue
compute the critical value of a dependence test of a kernel (Gini) distance covariance or correlation statistics.
It is a self-contained R function returning the critical value of the measure of dependence statistics.
The critical value of the test of significance level γ, however, is obtained by a permutation procedure. Let ν = 1: n be the vector of original sample indices of the sample for Y labels and \hat{ρ}_g(α) = \hat{ρ}(ν;α). Let π(ν) denote a permutation of the elements of ν and the corresponding \hat{ρ}_g(π;α) is computed. Under the {\cal H}_0, \hat{ρ}_g(ν) and \hat{ρ}_g(π;α) are identically distributed for every permutation π of ν. Hence, based on M permutations, the critical value q_{γ} is estimated by the (1-γ)100\% sample quantile of \hat{ρ}_g(π_m;α), m=1,...,M. Usually 100≤q M≤q 1000 is sufficient for a good estimation on the critical value.
See PermutationTest
for a test of multivariate independence
based on the (Gini) distance statistic.
CriticalValue
returns return the critical value of the measures of the dependence of the permutation test of a specified function
PermutationTest
n = 50 x <- runif(n) y <- c(rep(1,n/2),rep(2,n/2)) CriticalValue(x, y, sigma=1, alpha=2, level=0.04, M = 1000, method='KgCov')
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