simulateLSM
(Package: lvm4net) :
Simulate from LSM model
Function to simulate networks from the LSM model
● Data Source:
CranContrib
● Keywords:
● Alias: simulateLSM
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plot.lsm
(Package: lvm4net) :
Two dimensional plot of the Latent Space Model output
Function to plot an object of class 'lsm'
● Data Source:
CranContrib
● Keywords:
● Alias: plot.lsm
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boxroc
(Package: lvm4net) :
Boxplot and ROC Curves
Function to display boxplots and ROC curves to show model fit in terms of in-sample link prediction.
● Data Source:
CranContrib
● Keywords:
● Alias: boxroc
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lvm4net-package
(Package: lvm4net) :
Latent Variable Models for Networks
lvm4net provides a range of tools for latent variable models for network data. Most of the models are implemented using a fast variational inference approach. Latent space models for binary networks: the function lsm implements the latent space model (LSM) introduced by Hoff et al. (2002) using a variational inference and squared Euclidian distance; the function lsjm implements latent space joint model (LSJM) for multiplex networks introduced by Gollini and Murphy (2014). These models assume that each node of a network has a latent position in a latent space: the closer two nodes are in the latent space, the more likely they are connected. Functions for binary bipartite networks will be added soon.
● Data Source:
CranContrib
● Keywords:
● Alias: lvm4net, lvm4net-package
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lsm
(Package: lvm4net) :
Latent Space Model
Latent space models (LSM) are a well known family of latent variable models for network data introduced by Hoff et al. (2002) under the basic assumption that each node has an unknown position in a D-dimensional Euclidean latent space: generally the smaller the distance between two nodes in the latent space, the greater the probability of them being connected. Unfortunately, the posterior distribution of the LSM cannot be computed analytically. For this reason we propose a variational inferential approach which proves to be less computationally intensive than the MCMC procedure proposed in Hoff et al. (2002) (implemented in the latentnet package) and can therefore easily handle large networks. Salter-Townshend and Murphy (2013) applied variational methods to fit the LSM with the Euclidean distance in the VBLPCM package. In this package, a distance model with squared Euclidean distance is used. We follow the notation of Gollini and Murphy (2014).
● Data Source:
CranContrib
● Keywords:
● Alias: lsm
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Function to plot an object of class 'gofobj'
● Data Source:
CranContrib
● Keywords:
● Alias: plot.gofobj
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plotY
(Package: lvm4net) :
Plot the adjacency matrix of the network
Function to plot the adjacency matrix of the network.
● Data Source:
CranContrib
● Keywords:
● Alias: plotY
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goflsm
(Package: lvm4net) :
Goodness-of-Fit diagnostics for LSM model
This function produces goodness-of-fit diagnostics for LSM model.
● Data Source:
CranContrib
● Keywords:
● Alias: goflsm
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rotXtoY
(Package: lvm4net) :
Rotate X to match Y
Function to rotate X to match Y via singular value decomposition
● Data Source:
CranContrib
● Keywords:
● Alias: rotXtoY
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Function to print an object of class 'gofobj'
● Data Source:
CranContrib
● Keywords:
● Alias: print.gofobj
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providing 
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