tomogravity.fit: Tomogravity estimation for a single time point using L-BFGS-B
In networkTomography: Tools for network tomography

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 `getSrcDstIndices`
`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

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