Description Usage Arguments Value Author(s) Examples

Select the model based on the SBIC criterion and the two-step algorithm

1 |

`data` |
An n by p dataframe representing the observations |

`lambda` |
A vector of tuning parameters used to build the model pool |

`M` |
The prior adjacency matrix |

`prob` |
The mean error rate |

A list of objects containing:

`networkhat` |
The final selected adjacency matrix |

`candidates` |
The model pool |

Jie Zhou

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
set.seed(1)
m1 = 100
m2 = 30
p = 100
n = 100
d=simulate(n=n,p=p, m1 = m1, m2 = m2) # simulate fake data
lambda=exp(seq(-5,5,length=100)) # tuning parameter
data=d$data # data from the simulation
M=d$priornetwork # prior network from simulation
# calculating the error rate
r1=m2/m1
r2=m2/(p*(p-1)/2-m1)
r=(r1+r2)/2
# apply sggm
result=sggm(data=data, lambda=lambda, M=M, prob=r)
# compare the final network and the true network
result$networkhat
d$realnetwork
``` |

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