FBF_RS {FBFsearch} R Documentation

## Moment Fractional Bayes Factor Stochastic Search for Regression Models

### Description

Estimate the edge inclusion probabilities for a regression model (Y(q) on Y(q-1),...,Y(1)) with q variables from observational data, using the moment fractional Bayes factor approach.

### Usage

```FBF_RS(Corr, nobs, G_base, h, C, n_tot_mod, n_hpp)
```

### Arguments

 `Corr` qxq correlation matrix. `nobs` Number of observations. `G_base` Base model. `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. `n_hpp` Number of the highest posterior probability models which will be returned by the procedure.

### Value

An object of `class` `list` with:

`M_q`

Matrix (qxq) with the estimated edge inclusion probabilities.

`M_G`

Matrix (n*n_hpp)xq with the n_hpp highest posterior probability models returned by the procedure.

`M_P`

Vector (n_hpp) with the n_hpp posterior probabilities of the models in M_G.

### 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

Res_search=FBF_RS(Corr, nobs, matrix(0,1,(q-1)), 1, 0.01, 1000, 10)
M_q=Res_search\$M_q
M_G=Res_search\$M_G
M_P=Res_search\$M_P

Mt=rev(matrix(Gt[1:(q-1),q],1,(q-1))) #True Model

M_med=M_q
M_med[M_q>=0.5]=1
M_med[M_q<0.5]=0 #median probability model

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

## End(Not run)

```

[Package FBFsearch version 1.1 Index]