shipley.test {ggm} | R Documentation |
Computes a simultaneous test of all independence relationships implied by a given Gaussian model defined according to a directed acyclic graph, based on the sample covariance matrix.
shipley.test(S, n, A)
S |
a symmetric positive definite matrix, the sample covariance matrix. |
n |
a positive integer, the sample size. |
A |
a square Boolean matrix, of the same dimension as S ,
representing the edge matrix of a DAG. |
The test statistic is C = -2 sum ln p_j where p_j are the
p-values of tests of conditional independence in the basis set
computed by basiSet(A)
. The p-values are independent
uniform variables on (0,1) and the statistic has exactly a
chi square distribution on 2k degrees of freedom where
k is the number of elements of the basis set.
Shipley (2002) calls this test Fisher's C test.
ctest |
Test statistic C. |
df |
Degrees of freedom. |
pvalue |
The P-value of the test, assuming a two-sided alternative. |
Giovanni M. Marchetti
Shipley, B. (2000). A new inferential test for path models based on directed acyclic graphs. Structural Equation Modeling, 7(2), 206218.
## A decomposable model for the mathematics marks data data(marks) dag <- DAG(mec ~ vec+alg, vec ~ alg, sta ~ alg+ana, ana ~ alg) shipley.test(cov(marks), n=88, dag)