IAMB-FDR (bnlearn)

Module name	bnlearn_iambfdr
Package	bnlearn
Version	4.8.3
Language	R
Docs	here
Paper	Peña[1]
Graph type	DAG
MCMC	No
Edge constraints	Yes
Data type	C, D, M
Data missingness
Intervention type
Docker	bpimages/bnlearn:4.8.3

IAMB-FDR

Abstract: In many cases what matters is not whether a false discovery is made or not but the expected proportion of false discoveries among all the discoveries made, i.e. the so-called false discovery rate (FDR). We present an algorithm aiming at controlling the FDR of edges when learning Gaussian graphical models (GGMs). The algorithm is particularly suitable when dealing with more nodes than samples, e.g. when learning GGMs of gene networks from gene expression data. We illustrate this on the Rosetta compendium [8].

Some fields described

edgeConstraints Name of the JSON file containing background knowledge

Example JSON

[
  {
    "id": "iambfdr-zf",
    "alpha": [
      0.01,
      0.05
    ],
    "test": "zf",
    "B": null,
    "maxsx": null,
    "debug": false,
    "undirected": false,
    "timeout": null,
    "edgeConstraints": "edgeConstraints.json"
  },
  {
    "id": "iambfdr-mi",
    "alpha": [
      0.01,
      0.05
    ],
    "test": "mi",
    "B": null,
    "maxsx": null,
    "debug": false,
    "undirected": false,
    "timeout": null,
    "edgeConstraints": "edgeConstraints.json"
  }
]