bnlearn (version 1.6)

ci.test: Independence and Conditional Independence Tests

Description

Perform either an independence test or a conditional independence test.

Usage

## S3 method for class 'character':
ci.test(x, y = NULL, z = NULL, data, test = NULL,
    B = NULL, debug = FALSE, ...)
  ## S3 method for class 'data.frame':
ci.test(x, test = NULL, B = NULL, debug = FALSE, ...)
  ## S3 method for class 'numeric':
ci.test(x, y = NULL, z = NULL, test = NULL,
    B = NULL, debug = FALSE, ...)
  ## S3 method for class 'factor':
ci.test(x, y = NULL, z = NULL, test = NULL,
    B = NULL, debug = FALSE, ...)
  ## S3 method for class 'default':
ci.test(x, ...)

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 discrete data sets and the linear correlation

B

a positive integer, the number of permutations considered for each permutation test. It will be ignored with a warning if the conditional independence test specified by the test argument is not a permutation test.

debug

a boolean value. If TRUE a lot of debugging output is printed; otherwise the function is completely silent.

...

extra arguments from the generic method (currently ignored).

Value

• An object of class htest containing the following components:
• statistic
  the value the conditional independence test statistic.
• parameter
  the degrees of freedom of the approximate chi-squared or t distribution of the test statistic, NA if the p-value is computed by Monte Carlo simulation.
• 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

References

Edwards DI (2000). Introduction to Graphical Modelling. Springer, 2nd edition.

Legendre P (2000). "Comparison of Permutation Methods for the Partial Correlation and Partial Mantel Tests". Journal of Statistical Computation and Simulation, 67, 37-73.

Pesarin F (2001). Multivariate Permutation Tests With Applications in Biostatistics. Wiley.

See Also

choose.direction, 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)
#
#	 linear correlation
#
# data:  F ~ B | C + D
# cor = -0.1275, df = 4996, p-value < 2.2e-16
# alternative hypothesis: true value is not equal to 0

# using a data frame.
ci.test(gaussian.test)
#
#	 linear correlation
#
# data:  A ~ B | C + D + E + F + G
# cor = -0.5654, df = 4993, p-value < 2.2e-16
# alternative hypothesis: true value is not equal to 0

# using factor objects.
ci.test(x = learning.test[, "F"] , y = learning.test[, "B"],
  z = learning.test[, c("C", "D")] )
#
#	 mutual information
#
# data:  learning.test[, "F"] ~ learning.test[, "B"] | learning.test[, c("C", "D")]
# mi = 25.2664, df = 18, p-value = 0.1178
# alternative hypothesis: true value is greater than 0