ci.test {bnlearn} | R Documentation |
Perform either an independence test or a conditional independence test.
## 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, ...) ## Default S3 method: ci.test(x, ...)
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 for
continuous ones. See bnlearn-package for details. |
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). |
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 |
Marco Scutari
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.
choose.direction
, arc.strength
.
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. attach(learning.test) ci.test(x = F , y = B, z = data.frame(C, D)) # # mutual information (discrete) # # data: F ~ B | data.frame(C, D) # mi = 25.2664, df = 18, p-value = 0.1178 # alternative hypothesis: true value is greater than 0