Description Usage Arguments Value See Also Examples

Tomogravity estimation for a single time point using L-BFGS-B

1 2 | ```
tomogravity.fit(yt, A, srcDstInd, lambda, N = 1, normalize = FALSE,
lower = 0, control = list())
``` |

`yt` |
length-m numeric vector of observed aggregate flows at time t |

`A` |
m x k routing matrix |

`srcDstInd` |
list of source and destination flow
indices corresponding to each point-to-point flow, as
produced by |

`lambda` |
regularization parameter for mutual information prior. Note that this is scaled by the squared total traffic in the objective function before scaling the mututal information prior. |

`N` |
total traffic for normalization. Unused if normalized is FALSE. |

`normalize` |
If TRUE, xt and yt are scaled by N. Typically used in conjunction with calcN to normalize traffic to proportions, easing the tuning of lambda. |

`lower` |
Component-wise lower bound for xt in L-BFGS-B optimization. |

`control` |
List of control information for optim. |

A list as returned by optim, with element `par`

containing the estimated point-to-point flows and elementer
`gr`

containing the analytic gradient evaluated at the
estimate.

Other tomogravity: `tomogravity`

1 2 3 4 | ```
data(cmu)
srcDstInd <- getSrcDstIndices(cmu$A.full)
estimate <- tomogravity.fit(yt=cmu$Y.full[1, ], A=cmu$A.full,
srcDstInd=srcDstInd, lambda=0.01)
``` |

networkTomography documentation built on May 29, 2017, 4:56 p.m.

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