ci.test: Independence and conditional independence tests

In bnlearn: Bayesian Network Structure Learning, Parameter Learning and Inference

Independence and conditional independence tests

Description

Perform an independence or a conditional independence test.

Usage

ci.test(x, y, z, data, test, ..., debug = FALSE)

Arguments

x	a character string (the name of a variable), a data frame, a numeric vector or a factor object.
y	a character string (the name of another variable), a numeric vector or a factor object.
z	a vector of character strings (the names of the conditioning variables), a numeric vector, a factor object or a data frame. If NULL an independence test will be executed.
data	a data frame containing the variables to be tested.
test	a character string, the label of the conditional independence test to be used in the algorithm. If none is specified, the default test statistic is the mutual information for categorical variables, the Jonckheere-Terpstra test for ordered factors and the linear correlation for continuous variables. See independence tests for details.
...	optional arguments to be passed to the test specified by test. See below for details.
debug	a boolean value. If TRUE a lot of debugging output is printed; otherwise the function is completely silent.

Details

Additional arguments of the ci.test() function:

• B: a positive integer, the number of permutations used to compute the p-value of permutation tests. The default value is 5000 for nonparametric permutation tests and 100 for semiparametric permutation tests.

• fun: the function that computes the conditional independence test in the custom-test test. fun must have arguments x, y, z, data and args, in this order; in other words, it must have signature function(x, y, z, data, args). x and y will be the labels of the nodes to test for independence (one character string each); z will be the labels of the nodes in the conditioning set (a vector of character strings, possibily NULL for empty sets); data will contain the complete data set, with all the variables (a data frame); and args will be a list containing any additional arguments to the test.

• args: a list containing the optional arguments to fun, for tuning custom-test test functions.

Value

An object of class htest containing the following components:

statistic	the value the test statistic.
parameter	the degrees of freedom of the approximate chi-squared or t distribution of the test statistic; the number of permutations computed by Monte Carlo tests. Semiparametric tests have both.
p.value	the p-value for the test.
method	a character string indicating the type of test performed, and whether Monte Carlo simulation or continuity correction was used.
data.name	a character string giving the name(s) of the data.
null.value	the value of the test statistic under the null hypothesis, always 0.
alternative	a character string describing the alternative hypothesis.

Author(s)

Marco Scutari

See Also

independence tests, arc.strength.

Examples

data(gaussian.test)
data(learning.test)

# using a data frame and column labels.
ci.test(x = "F" , y = "B", z = c("C", "D"), data = gaussian.test)
# using a data frame.
ci.test(gaussian.test)
# using factor objects.
attach(learning.test)
ci.test(x = F , y = B, z = data.frame(C, D))

bnlearn documentation built on Sept. 11, 2024, 8:27 p.m.