FBF_LS {FBFsearch} R Documentation

## Moment Fractional Bayes Factor Stochastic Search with Local Prior for DAG Models

### Description

Estimate the edge inclusion probabilities for a directed acyclic graph (DAG) from observational data, using the moment fractional Bayes factor approach with local prior.

### Usage

```FBF_LS(Corr, nobs, G_base, h, C, n_tot_mod)

```

### Arguments

 `Corr` qxq correlation matrix. `nobs` Number of observations. `G_base` Base DAG. `h` Parameter prior. `C` Costant who keeps the probability of all local moves bounded away from 0 and 1. `n_tot_mod` Maximum number of different models which will be visited by the algorithm, for each equation.

### Value

An object of `class` `matrix` with the estimated edge inclusion probabilities.

### Author(s)

Davide Altomare (davide.altomare@gmail.com).

### References

D. Altomare, G. Consonni and L. LaRocca (2012).Objective Bayesian search of Gaussian directed acyclic graphical models for ordered variables with non-local priors.Article submitted to Biometric Methodology.

### Examples

```
## Not run:

data(SimDag6)

Corr=dataSim6\$SimCorr[[1]]
nobs=50
q=ncol(Corr)
Gt=dataSim6\$TDag

M_q=FBF_LS(Corr, nobs, matrix(0,q,q), 0, 0.01, 1000)

G_med=M_q
G_med[M_q>=0.5]=1
G_med[M_q<0.5]=0 #median probability DAG

#Structural Hamming Distance between the true DAG and the median probability DAG
sum(sum(abs(G_med-Gt)))

## End(Not run)

```

[Package FBFsearch version 1.1 Index]