Description Usage Arguments Value References Examples

Return the best lambda number selected by AIC by different norms, given sample covariance matrices of two sample classes, estimation by different lambdas and the total number of samples.

1 | ```
dpmdtl.ic(S1, S0, ret, n, penalty)
``` |

`S1` |
A pXp matrix. The sample covariance matrix of one sample class. |

`S0` |
A pXp matrix. The sample covariance matrix of one sample class. |

`ret` |
A list consist of pXp matrices. |

`n` |
The total number of samples. |

`penalty` |
The magnitude of penalty. |

A vector of best lambda number chosen by different matrix norms.

Zhao,S., Cai,T.& Li,H.(2014) Direct estimation of differential networks. Biometrika 101, 253-268.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
##generate samples
library(MASS)
set.seed(1);
Sigma1 = genp(50,0.2,0.5)
set.seed(1);
Sigma2 = Sigma1+genp1(50,100,0.5)
tdelta = Sigma2-Sigma1
S1<-solve(Sigma1)
S0<-solve(Sigma2)
n<-200
p<-50
X1<-mvrnorm(n,rep(0,p),S1)
Y1<-mvrnorm(n,rep(0,p),S0)
dpmdtl<- Dpmdtl(X1,Y1,nlambda=10,tuning="none")
ret<-dpmdtl$Dpmdtl
##use of dpmdtl.ic
aic=dpmdtl.ic(S1,S0,ret,2*n,2)
``` |

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