P-values of one minimal testable implication (with the
smallest possible conditioning set) is returned per missing edge
given an acyclic graph (DAG or BAP) using the function
impliedConditionalIndependencies plus the
function localTests from package dagitty.
Without assuming any particular dependence structure, the p-values of
every CI test, in a DAG (BAP), is then combined using the Bonferroni’s
statistic in an overall test of the fitted model, B = K*min(p1,...,pK),
as reviewed in Vovk & Wang (2020).
localCI.test(graph, data, bap = FALSE, limit = 100, verbose = TRUE, ...)A directed graph as an igraph object.
A data matrix with subjects as rows and variables as columns.
If TRUE, the input graph is trasformend in a BAP, if FALSE (defult) the input graph is reduced in a DAG.
An integer value corresponding to the size of the
extracted acyclic graph. Beyond this limit, switch to Shipley's
C-test (Shipley 2000) is enabled to reduce the computational burden.
By default, limit = 100.
If TRUE, LocalCI results will be showed to screen (default = TRUE).
Currently ignored.
A list of three objects: (i) "dag": the DAG used to perform the localCI test (ii) "msep": the list of all m-separation tests over missing edges in the input graph and (iii) "mtest":the overall Bonferroni's P-value.
Vovk V, Wang R (2020). Combining p-values via averaging. Biometrika 107(4): 791-808. <https://doi.org/10.1093/biomet/asaa027>
Shipley B (2000). A new inferential test for path models based on DAGs. Structural Equation Modeling, 7(2): 206-218. <https://doi.org/10.1207/S15328007SEM0702_4>
# Nonparanormal(npn) transformation
als.npn <- transformData(alsData$exprs)$data
#> Conducting the nonparanormal transformation via shrunkun ECDF...done.
sem <- SEMrun(alsData$graph, als.npn)
#> NLMINB solver ended normally after 1 iterations
#>
#> deviance/df: 10.92504 srmr: 0.2858859
#>
B_test <- localCI.test(sem$graph, als.npn, verbose = TRUE)
#> d-separation test (minimal set) of 420 edges...
#> B_test df B_pvalue
#> 1 376.6275 1 0