semidist (version 0.1.0)

mv_test: MV independence test

Description

Implement the MV independence test via permutation test, or via the asymptotic approximation

Usage

mv_test(x, y, test_type = "perm", num_perm = 10000)

Value

A list with class "indtest" containing the following components

• method: name of the test;

• name_data: names of the x and y;

• n: sample size of the data;

• num_perm: number of replications in permutation test;

• stat: test statistic;

• pvalue: computed p-value. (Notice: asymptotic test cannot return a p-value, but only the critical values crit_vals for 90%, 95% and 99% confidence levels.)

Arguments

x

Data of univariate continuous variables, which should be a vector of length \(n\).

y

Data of categorical variables, which should be a factor of length \(n\).

test_type

Type of the test:

• "perm" (the default): Implement the test via permutation test;

• "asym": Implement the test via the asymptotic approximation.

See the Reference for details.

num_perm

The number of replications in permutation test.

Examples

x <- mtcars[, "mpg"]
y <- factor(mtcars[, "am"])
test <- mv_test(x, y)
print(test)
test_asym <- mv_test(x, y, test_type = "asym")
print(test_asym)

# Man-made independent data -------------------------------------------------
n <- 30; R <- 5; prob <- rep(1/R, R)
x <- rnorm(n)
y <- factor(sample(1:R, size = n, replace = TRUE, prob = prob), levels = 1:R)
test <- mv_test(x, y)
print(test)
test_asym <- mv_test(x, y, test_type = "asym")
print(test_asym)

# Man-made functionally dependent data --------------------------------------
n <- 30; R <- 3
x <- rep(0, n)
x[1:10] <- 0.3; x[11:20] <- 0.2; x[21:30] <- -0.1
y <- factor(rep(1:3, each = 10))
test <- mv_test(x, y)
print(test)
test_asym <- mv_test(x, y, test_type = "asym")
print(test_asym)