R: Test of all independencies implied by a given DAG

shipley.test {ggm} | R Documentation

## Test of all independencies implied by a given DAG

### Description

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.

### Usage

    shipley.test(amat, S, n)

### Arguments

`amat` | a square Boolean matrix, of the same dimension as `S`, representing the adjacency matrix of a DAG.
`S` | a symmetric positive definite matrix, the sample covariance matrix.
`n` | a positive integer, the sample size.

### Details

The test statistic is C = -2 ∑ \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.

### Value

`ctest` | Test statistic C.
`df` | Degrees of freedom.
`pvalue` | The P-value of the test, assuming a two-sided alternative.

### Author(s)

Giovanni M. Marchetti

### References

Shipley, B. (2000). A new inferential test for path models based on directed acyclic graphs. Structural Equation Modeling, 7(2), 206–218.

### See Also

`basiSet`, `pcor.test`

### Examples

    ## A decomposable model for the mathematics marks data
    data(marks)
    dag <- DAG(mechanics ~ vectors+algebra, vectors ~ algebra,
    statistics ~ algebra+analysis, analysis ~ algebra)
    shipley.test(dag, cov(marks), n=88)

[Package ggm version 2.3 Index]