Package 'latentnet'

Title:	Latent Position and Cluster Models for Statistical Networks
Description:	Fit and simulate latent position and cluster models for statistical networks. See Krivitsky and Handcock (2008) <doi:10.18637/jss.v024.i05> and Krivitsky, Handcock, Raftery, and Hoff (2009) <doi:10.1016/j.socnet.2009.04.001>.
Authors:	Pavel N. Krivitsky [aut, cre] , Mark S. Handcock [aut], Susan M. Shortreed [ctb], Jeremy Tantrum [ctb], Peter D. Hoff [ctb], Li Wang [ctb], Kirk Li [ctb], Jake Fisher [ctb], Jordan T. Bates [ctb]
Maintainer:	Pavel N. Krivitsky <[email protected]>
License:	GPL-3 + file LICENSE
Version:	2.11.0
Built:	2024-10-17 06:49:46 UTC
Source:	CRAN

Help Index

latentnet: Latent Position and Cluster Models for Statistical Networks
Convert an ERGMM Object to an MCMC list object for Diagnostics.
Bilinear (inner-product) latent space, with optional clustering
Auxiliary for Controlling ERGMM Fitting
Southern Women Data Set (Davis) as a bipartite “network” object
Fit a Latent Space Random Graph Model
Class of Fitted Exponential Random Graph Mixed Models
Edge Weight Distribution Families
Draw a pie chart at a specified location.
A List of ERGMM Parameter Configuration
Auxiliary for Setting the ERGMM Prior
Euclidean distance latent space, with optional clustering
Squared euclidean distance latent space, with optional clustering
Conduct Goodness-of-Fit Diagnostics on a Exponential Family Random Graph Mixed Model Fit
Intercept
Edge covariates for the latent model
Covariate effect on self-loops
Factor attribute effect on self-loops
Self-loops
Conduct MCMC diagnostics on an ERGMM fit
Merge two or more replications of ERGMM fits
Plotting Method for class ERGMM
Predicted Dyad Values for an ERGMM.
Receiver covariate effect
Random receiver effect
Random sender effect
Random sociality effect
Sender covariate effect
Draw from the distribution of an Exponential Random Graph Mixed Model
Sociality covariate effect
ERGMM Fit Summaries
Read Highland Tribes

latentnet: Latent Position and Cluster Models for Statistical Networks

Description

Fit and simulate latent position and cluster models for statistical networks. See Krivitsky and Handcock (2008) doi:10.18637/jss.v024.i05 and Krivitsky, Handcock, Raftery, and Hoff (2009) doi:10.1016/j.socnet.2009.04.001.

Details

The package latentnet is used to fit latent cluster random effect models, where the probability of a network $g$ , on a set of nodes is a product of dyad probabilities, each of which is a GLM with linear component $\eta_{i,j}=\sum_{k=1}^p \beta_k X_{i,j,k}+d(Z_i,Z_j)+\delta_i+\gamma_j$ , where $X$ is an array of dyad covariates, $\beta$ is a vector of covariate coefficients, $Z_i$ is the latent space position of node $i$ , $d(\cdot,\cdot)$ is a function of the two positions: either negative Euclidean ( $-||Z_i-Z_j||$ ) or bilinear ( $Z_i\cdot Z_j$ ), and $\delta$ and $\gamma$ are vectors of sender and receiver effects. (Note that these are different from the eigenmodel of Hoff (2007) “Modeling homophily and stochastic equivalence in symmetric relational data”, fit by package eigenmodel.)

The ergmm specifies models via: g ~ <model terms> where g is a network object For the list of possible <model terms>, see ergmTerm. For the list of the possible dyad distribution families, see families.ergmm.

The arguments in the ergmm function specific to latent variable models are ergmm.control. See the help page for ergmm for the details.

The result of a latent variable model fit is an ergmm object. Hence the summary, print, and plot functions apply to the fits. The plot.ergmm function has many options specific to latent variable models.

Author(s)

Maintainer: Pavel N. Krivitsky [email protected] (ORCID)

Authors:

Mark S. Handcock [email protected]

Other contributors:

Susan M. Shortreed [contributor]
Jeremy Tantrum [contributor]
Peter D. Hoff [contributor]
Li Wang [email protected] [contributor]
Kirk Li [email protected] [contributor]
Jake Fisher [email protected] [contributor]
Jordan T. Bates [email protected] [contributor]

References

Mark S. Handcock, Adrian E. Raftery and Jeremy Tantrum (2007). Model-Based Clustering for Social Networks. Journal of the Royal Statistical Society: Series A (Statistics in Society), 170(2), 301-354.

Peter D. Hoff (2005). Bilinear Mixed Effects Models for Dyadic Data. Journal of the American Statistical Association, 100(469), 286-295.

Peter D. Hoff, Adrian E. Raftery and Mark S. Handcock (2002). Latent space approaches to social network analysis. Journal of the American Statistical Association, 97(460), 1090-1098.

Pavel N. Krivitsky, Mark S. Handcock, Adrian E. Raftery, and Peter D. Hoff (2009). Representing degree distributions, clustering, and homophily in social networks with latent cluster random effects models. Social Networks, 31(3), 204-213.

Pavel N. Krivitsky and Mark S. Handcock (2008). Fitting Position Latent Cluster Models for Social Networks with latentnet. Journal of Statistical Software, 24(5). doi:10.18637/jss.v024.i05

Susan M. Shortreed, Mark S. Handcock, and Peter D. Hoff (2006). Positional Estimation within the Latent Space Model for Networks. Methodology, 2(1), 24-33.

Convert an ERGMM Object to an MCMC list object for Diagnostics.

Description

Functions to extract a subset of MCMC-sampled variables from an object of class ergmm and construct an mcmc.list object.

Usage

## S3 method for class 'list.ergmm'
as.mcmc(x, burnin = FALSE, which.vars = NULL, vertex.i = c(1), ...)
## S3 method for class 'list.ergmm'
as.mcmc(x, burnin = FALSE, which.vars = NULL, vertex.i = c(1), ...)

Arguments

`x`	An object of class `ergmm`.
`burnin`	If `TRUE`, generates an `mcmc.list` object for the burnin (if stored) instead of the main sampling run.
`which.vars`	A named list mapping variable names to the indices to include. If given, overrides the defaults and all arguments that follow.
`vertex.i`	A numeric vector of vertices whose latent space coordinates and random effects to include.
`...`	Not used at this time.

Details

Unless which.vars is specified, the mcmc.list returned also includes all of the covariate coefficients.

Regardless of whether the MCMC run was single- or multi-threaded, this function returns an mcmc.list, with a single thread, if necessary.

Value

A mcmc.list object with the sample of the selected subset of the variables.

Examples



library(coda)
data(sampson)
monks.fit<-ergmm(samplike~euclidean(d=2,G=3))
monks.fit.mcmc<-as.mcmc.list(monks.fit)
plot(monks.fit.mcmc)
raftery.diag(monks.fit.mcmc)

library(coda)
data(sampson)
monks.fit<-ergmm(samplike~euclidean(d=2,G=3))
monks.fit.mcmc<-as.mcmc.list(monks.fit)
plot(monks.fit.mcmc)
raftery.diag(monks.fit.mcmc)

Bilinear (inner-product) latent space, with optional clustering

Description

Adds a term to the model equal to the inner product of the latent positions: $Z_i \cdot Z_j$ , where $Z_i$ and $Z_j$ are the positions of their respective actors in an unobserved social space. These positions may optionally have a finite spherical Gaussian mixture clustering structure. Note: For a bilinear latent space effect, two actors being closer in the clustering sense does not necessarily mean that the expected value of a tie between them is higher. Thus, a warning is printed when this model is combined with clustering.

Important: This term works in latentnet's ergmm() only. Using it in ergm() will result in an error.

Usage

# binary: bilinear(d, G=0, var.mul=1/8, var=NULL, var.df.mul=1, var.df=NULL,
#             mean.var.mul=1, mean.var=NULL, pK.mul=1, pK=NULL)

# valued: bilinear(d, G=0, var.mul=1/8, var=NULL, var.df.mul=1, var.df=NULL,
#             mean.var.mul=1, mean.var=NULL, pK.mul=1, pK=NULL)
# binary: bilinear(d, G=0, var.mul=1/8, var=NULL, var.df.mul=1, var.df=NULL,
#             mean.var.mul=1, mean.var=NULL, pK.mul=1, pK=NULL)

# valued: bilinear(d, G=0, var.mul=1/8, var=NULL, var.df.mul=1, var.df=NULL,
#             mean.var.mul=1, mean.var=NULL, pK.mul=1, pK=NULL)

Arguments

`d`	The dimension of the latent space.
`G`	The number of groups (0 for no clustering).
`var.mul`	In the absence of `var`, this argument will be used as a scaling factor for a function of average cluster size and latent space dimension to set `var`. To set it in the `prior` argument to `ergmm`, use `Z.var.mul`.
`var`	If given, the scale parameter for the scale-inverse-chi-squared prior distribution of the within-cluster variance. To set it in the `prior` argument to `ergmm`, use `Z.var`.
`var.df.mul`	In the absence of `var.df`, this argument is the multiplier for the square root of average cluster size, which serves in place of `var.df`. To set it in the `prior` argument to `ergmm`, use `Z.var.df.mul`.
`var.df`	The degrees of freedom parameter for the scale-inverse-chi-squared prior distribution of the within-cluster variance. To set it in the `prior` argument to `ergmm`, use `Z.var.df`.
`mean.var.mul`	In the absence of `mean.var`, the multiplier for a function of number of vertices and latent space dimension to set `mean.var`. To set it in the `prior` argument to `ergmm`, use `Z.mean.var.mul`.
`mean.var`	The variance of the spherical Gaussian prior distribution of the cluster means. To set it in the `prior` argument to `ergmm`, use `Z.mean.var`.
`pK.mul`	In the absence of `pK`, this argument is the multiplier for the square root of the average cluster size, which is used as `pK`. To set it in the `prior` argument to `ergmm`, use `Z.pK`.
`pK`	The parameter of the Dirichilet prior distribution of cluster assignment probabilities. To set it in the `prior` argument to `ergmm`, use `Z.pK`.

Details

The following parameters are associated with this term:

Z: Numeric matrix with rows being latent space positions.
Z.K (when $\code{G}>0$ ): Integer vector of cluster assignments.
Z.mean (when $\code{G}>0$ ): Numeric matrix with rows being cluster means.
Z.var (when $\code{G}>0$ ): Depending on the model, either a numeric vector with within-cluster variances or a numeric scalar with the overal latent space variance.
Z.pK (when $\code{G}>0$ ): Numeric vector of probabilities of a vertex being in a particular cluster.

Auxiliary for Controlling ERGMM Fitting

Description

Auxiliary function as user interface for ergmm fitting. Typically only used when calling ergmm. It is used to set parameters that affect the sampling but do not affect the posterior distribution.

Usage

control.ergmm(
  sample.size = 4000,
  burnin = 10000,
  interval = 10,
  threads = 1,
  kl.threads = 1,
  mle.maxit = 100,
  Z.delta = 0.6,
  RE.delta = 0.6,
  group.deltas = 0.4,
  pilot.runs = 4,
  pilot.factor = 0.8,
  pilot.discard.first = 0.5,
  target.acc.rate = 0.234,
  backoff.threshold = 0.05,
  backoff.factor = 0.2,
  accept.all = FALSE,
  store.burnin = FALSE,
  refine.user.start = TRUE
)

ergmm.control(
  sample.size = 4000,
  burnin = 10000,
  interval = 10,
  threads = 1,
  kl.threads = 1,
  mle.maxit = 100,
  Z.delta = 0.6,
  RE.delta = 0.6,
  group.deltas = 0.4,
  pilot.runs = 4,
  pilot.factor = 0.8,
  pilot.discard.first = 0.5,
  target.acc.rate = 0.234,
  backoff.threshold = 0.05,
  backoff.factor = 0.2,
  accept.all = FALSE,
  store.burnin = FALSE,
  refine.user.start = TRUE
)
control.ergmm(
  sample.size = 4000,
  burnin = 10000,
  interval = 10,
  threads = 1,
  kl.threads = 1,
  mle.maxit = 100,
  Z.delta = 0.6,
  RE.delta = 0.6,
  group.deltas = 0.4,
  pilot.runs = 4,
  pilot.factor = 0.8,
  pilot.discard.first = 0.5,
  target.acc.rate = 0.234,
  backoff.threshold = 0.05,
  backoff.factor = 0.2,
  accept.all = FALSE,
  store.burnin = FALSE,
  refine.user.start = TRUE
)

ergmm.control(
  sample.size = 4000,
  burnin = 10000,
  interval = 10,
  threads = 1,
  kl.threads = 1,
  mle.maxit = 100,
  Z.delta = 0.6,
  RE.delta = 0.6,
  group.deltas = 0.4,
  pilot.runs = 4,
  pilot.factor = 0.8,
  pilot.discard.first = 0.5,
  target.acc.rate = 0.234,
  backoff.threshold = 0.05,
  backoff.factor = 0.2,
  accept.all = FALSE,
  store.burnin = FALSE,
  refine.user.start = TRUE
)

Arguments

`sample.size`	The number of draws to be taken from the posterior distribution.
`burnin`	The number of initial MCMC iterations to be discarded.
`interval`	The number of iterations between consecutive draws.
`threads`	The number of chains to run. If greater than 1, package `snowFT` is used to take advantage of any multiprocessing or distributed computing capabilities that may be available. Currently, only PVM (via `rpvm`) has been tested. Note, also, that PVM daemon needs to be started before the package is loaded.
`kl.threads`	If greather than 1, uses an experimental parallelized label-switching algorithm. This is not guaranteed to work.
`mle.maxit`	Maximum number of iterations for computing the starting values, posterior modes, MLEs, MKL estimates, etc..
`Z.delta`	Standard deviation of the proposal for the jump in the individual latent space position, or its starting value for the tuner.
`RE.delta`	Standard deviation of the proposal for the jump in the individual random effects values, or its starting value for the tuner.
`group.deltas`	A scalar, a vector, or a matrix of an appropriate size, giving the initial proposal structure for the “group proposal” of a jump in covariate coefficients, scaling of latent space positions, and a shift in random ffects. If a matrix of an appropriate size is given, it is used as a matrix of coefficients for a correlated proposal. If a vector is given, an independent proposal is used with the corresponding elements being proposal standard deviations. If a scalar is given, it is used as a multiplier for an initial heuristic for the proposal structure. It is usually best to leave this argument alone and let the adaptive sampling be used.
`pilot.runs`	Number of pilot runs into which to split the burn-in period. After each pilot run, the proposal standard deviations and coefficients `Z.delta`, `RE.delta`, and `group.deltas` are reevaluated. If set to `0`, disables adaptive sampling, and only makes a single burn-in run.
`pilot.factor`	Initial value for the factor by which the coefficients gotten by a Choletsky decomposition of the pilot sample covariance matrix are multiplied.
`pilot.discard.first`	Proportion of draws from the pilot run to discard for estimating acceptance rate and group proposal covariance.
`target.acc.rate`	Taget acceptance rate for the proposals used. After a pilot run, the proposal variances are adjusted upward if the acceptance rate is above this, and downward if below.
`backoff.threshold`	If a pilot run's acceptance rate is below this, redo it with drastically reduced proposal standard deviation. Set to `0` to disable this behavior.
`backoff.factor`	Factor by which to multiply the relevant proposal standard deviation if its acceptance rate fell below the backoff threshold.
`accept.all`	Forces all proposals to be accepted unconditionally. Use only in debugging proposal distributions!
`store.burnin`	If `TRUE`, the samples from the burnin are also stored and returned, to be used in MCMC diagnostics.
`refine.user.start`	If `TRUE`, the values passed to `ergmm` in the `user.start` argument can be updated by the mode-finding algorithm.

Value

A list with the arguments as components.

Examples



data(sampson)
## Shorter run than default.
samp.fit<-ergmm(samplike~euclidean(d=2,G=3)+rreceiver,
control=ergmm.control(burnin=1000,sample.size= 2000,interval=5))


data(sampson)
## Shorter run than default.
samp.fit<-ergmm(samplike~euclidean(d=2,G=3)+rreceiver,
control=ergmm.control(burnin=1000,sample.size= 2000,interval=5))

Southern Women Data Set (Davis) as a bipartite “network” object

Description

This is a data set of 18 women observed over a nine-month period. During that period, various subsets of these women had met in a series of 14 informal social events. The data recored which women met for which events. The data is originally from Davis, Gardner and Gardner (1941) via UCINET and stored as a network object.

Details

This documentation is taken from Freeman (2003) in his usual lucid description. See the reference to the paper below:

In the 1930s, five ethnographers, Allison Davis, Elizabeth Stubbs Davis, Burleigh B. Gardner, Mary R. Gardner and J. G. St. Clair Drake, collected data on stratification in Natchez, Mississippi (Warner, 1988, p. 93). They produced the book cited below (DGG) that reported a comparative study of social class in black and in white society. One element of this work involved examining the correspondence between people's social class levels and their patterns of informal interaction. DGG was concerned with the issue of how much the informal contacts made by individuals were established solely (or primarily) with others at approximately their own class levels. To address this question the authors collected data on social events and examined people's patterns of informal contacts.

In particular, they collected systematic data on the social activities of 18 women whom they observed over a nine-month period. During that period, various subsets of these women had met in a series of 14 informal social events. The participation of women in events was uncovered using ⁠interviews, the records of participant observers, guest lists, and the newspapers'' (DGG, p. 149). Homans (1950, p. 82), who presumably had been in touch with the research team, reported that the data reflect joint activities like, ⁠a day's work behind the counter of a store, a meeting of a women's club, a church supper, a card party, a supper party, a meeting of the Parent-Teacher Association, etc.”

This data set has several interesting properties. It is small and manageable. It embodies a relatively simple structural pattern, one in which, according to DGG, the women seemed to organize themselves into two more or less distinct groups. Moreover, they reported that the positions - core and peripheral - of the members of these groups could also be determined in terms of the ways in which different women had been involved in group activities. At the same time, the DGG data set is complicated enough that some of the details of its patterning are less than obvious. As Homans (1950, p. 84) put it, ⁠The pattern is frayed at the edges.'' And, finally, this data set comes to us in a two-mode ⁠woman by event” form. Thus, it provides an opportunity to explore methods designed for direct application to two-mode data. But at the same time, it can easily be transformed into two one-mode matrices (woman by woman or event by event) that can be examined using tools for one-mode analysis.

Because of these properties, this DGG data set has become something of a touchstone for comparing analytic methods in social network analysis. Davis, Gardner and Gardner presented an intuitive interpretation of the data, based in part on their ethnographic experience in the community. Then the DGG data set was picked up by Homans (1950) who provided an alternative intuitive interpretation. In 1972, Phillips and Conviser used an analytic tool, based on information theory, that provided a systematic way to reexamine the DGG data. Since then, this data set has been analyzed again and again. It reappears whenever any network analyst wants to explore the utility of some new tool for analyzing data.

If the source of the data set does not specified otherwise, this data set is protected by the Creative Commons License https://creativecommons.org/licenses/by-nc-nd/2.5/.

When publishing results obtained using this data set the original authors should be cited. In addition this package should be cited.

Source

Linton C. Freeman (2003). Finding Social Groups: A Meta-Analysis of the Southern Women Data, In Ronald Breiger, Kathleen Carley and Philippa Pattison, eds. Dynamic Social Network Modeling and Analysis. Washington: The National Academies Press.

References

Davis, A., Gardner, B. B. and M. R. Gardner (1941) Deep South, Chicago: The University of Chicago Press.

Examples



data(davis)
# Fit a 2D 2-cluster fit and plot.
davis.fit<-ergmm(davis~euclidean(d=2,G=2)+rsociality)
plot(davis.fit,pie=TRUE,rand.eff="sociality")


data(davis)
# Fit a 2D 2-cluster fit and plot.
davis.fit<-ergmm(davis~euclidean(d=2,G=2)+rsociality)
plot(davis.fit,pie=TRUE,rand.eff="sociality")

Fit a Latent Space Random Graph Model

Description

ergmm() is used to fit latent space and latent space cluster random network models, as described by Hoff, Raftery and Handcock (2002), Handcock, Raftery and Tantrum (2005), and Krivitsky, Handcock, Raftery, and Hoff (2009). ergmm() can return either a Bayesian model fit or the two-stage MLE.

Usage

ergmm(
  formula,
  response = NULL,
  family = "Bernoulli",
  fam.par = NULL,
  control = control.ergmm(),
  user.start = list(),
  prior = ergmm.prior(),
  tofit = c("mcmc", "mkl", "mkl.mbc", "procrustes", "klswitch"),
  Z.ref = NULL,
  Z.K.ref = NULL,
  seed = NULL,
  verbose = FALSE
)
ergmm(
  formula,
  response = NULL,
  family = "Bernoulli",
  fam.par = NULL,
  control = control.ergmm(),
  user.start = list(),
  prior = ergmm.prior(),
  tofit = c("mcmc", "mkl", "mkl.mbc", "procrustes", "klswitch"),
  Z.ref = NULL,
  Z.K.ref = NULL,
  seed = NULL,
  verbose = FALSE
)

Arguments

`formula`	An formula object, of the form `g ~ <term 1> + <term 2> ...`, where `g` is a network object or a matrix that can be coerced to a network object, and `<term 1>`, `<term 2>`, etc., are each terms for the model. See `ergmTerm` for the terms that can be fitted, though note the section on fixed effects below. To create a network object in , use the `network` function, then add nodal attributes to it using `set.vertex.attribute` if necessary. Note that, as in `lm()`, the model will include an `intercept` term. This behavior can be overridden by including a `-1` or `+0` term in the formula, and a `1(mean=...,var=...)` term can be used to set a prior different from default.
`response`	An optional edge attribute that serves as the response variable. By default, presence (1) or absence (0) of an edge in `g` is used.
`family`	A character vector specifying the conditional distribution of each edge value. See families.ergmm for the currently implemented families.
`fam.par`	For those families that require additional parameters, a list.
`control`	The MCMC parameters that do not affect the posterior distribution such as the sample size, the proposal variances, and tuning parameters, in the form of a named list. See `control.ergmm` for more information and defaults.
`user.start`	An optional initial configuration parameters for MCMC in the form of a list. By default, posterior mode conditioned on cluster assignments is used. It is permitted to only supply some of the parameters of a configuration. If this is done, the remaining paramters are fitted conditional on those supplied.
`prior`	The prior parameters for the model being fitted in the form of a named list. See term help for the terms to use. If given, will override those given in the formula terms, making it useful as a convenient way to store and reproduce a prior distribution. The list or prior parameters can also be extracted from an ERGMM fit object. See `ergmm.prior` for more information.
`tofit`	A character vector listing some subset of "pmode", "mcmc", "mkl", "mkl.mbc", "mle","procrustes", and "klswitch", defaulting to all of the above, instructing `ergmm` what should be returned as a part of the ERGMM fit object. Omiting can be used to skip particular steps in the fitting process. If the requested procedure or output depends on some other procedure or output not explictly listed, the dependency will be resolved automatically.
`Z.ref`	If given, used as a reference for Procrustes analysis.
`Z.K.ref`	If given, used as a reference for label-switching.
`seed`	If supplied, random number seed.
`verbose`	If this is `TRUE` (or `1`), causes information to be printed out about the progress of the fitting, particularly initial value generation. Higher values lead to greater verbosity.

Value

ergmm returns an object of class ergmm containing the information about the posterior.

Specifying fixed effects

Each coefficient for a fixed effect covariate has a normal prior whose mean and variance are set by the mean and var parameters of the term. For those formula terms that add more than one covariate, a vector can be given for mean and variance. If not, the vectors given will be repeated until the needed length is reached.

ergmm can use model terms implemented for the ergm package and via the ergm.userterms API (in GitHub repository statnet/ergm.userterms). See ergmTerm for a list of available terms. If you wish to specify the prior mean and variance, you can add them to the call. E.g.,
TERMNAME(..., mean=0, var=9),
where ... are the arguments for the ergm term, will initialize TERMNAME with prior mean of 0 and prior variance of 9.

Some caveats:

ergm has a binary and a valued mode. Regardless of the family used, the binary variant of the ergm term will be used in the linear predictor of the model.
ergm does not support modeling self-loops, so terms imported in this way will always have predictor x[i,i]==0. This should not affect most situations, but if you absolutely must model self-loops and non-self-edges in one term, use the deprecated terms below.
latentnet only fits models with dyadic independence. Terms that induce dyadic dependence (e.g., triangles) can be used, but then the likelihood of the model will, effectively, be replaced with pseudolikelihood. (Note that under dyadic independence, the two are equal.)

References

Mark S. Handcock, Adrian E. Raftery and Jeremy Tantrum (2002). Model-Based Clustering for Social Networks. Journal of the Royal Statistical Society: Series A, 170(2), 301-354.

Peter D. Hoff, Adrian E. Raftery and Mark S. Handcock (2002). Latent space approaches to social network analysis. Journal of the American Statistical Association, 97(460), 1090-1098.

Pavel N. Krivitsky and Mark S. Handcock (2008). Fitting Position Latent Cluster Models for Social Networks with latentnet. Journal of Statistical Software, 24(5). doi:10.18637/jss.v024.i05

Examples



#
# Use 'data(package = "latentnet")' to list the data sets in a
#
data(package="latentnet")
#
# Using Sampson's Monk data, lets fit a 
# simple latent position model
#
data(sampson)
samp.fit <- ergmm(samplike ~ euclidean(d=2))
#
# See if we have convergence in the MCMC
mcmc.diagnostics(samp.fit)
#
# Plot the fit
#
plot(samp.fit)
#
# Using Sampson's Monk data, lets fit a latent clustering random effects model
#
samp.fit2 <- ergmm(samplike ~ euclidean(d=2, G=3)+rreceiver)
#
# See if we have convergence in the MCMC
mcmc.diagnostics(samp.fit2)
#
# Plot the fit.
#
plot(samp.fit2, pie=TRUE)

#
# Use 'data(package = "latentnet")' to list the data sets in a
#
data(package="latentnet")
#
# Using Sampson's Monk data, lets fit a 
# simple latent position model
#
data(sampson)
samp.fit <- ergmm(samplike ~ euclidean(d=2))
#
# See if we have convergence in the MCMC
mcmc.diagnostics(samp.fit)
#
# Plot the fit
#
plot(samp.fit)
#
# Using Sampson's Monk data, lets fit a latent clustering random effects model
#
samp.fit2 <- ergmm(samplike ~ euclidean(d=2, G=3)+rreceiver)
#
# See if we have convergence in the MCMC
mcmc.diagnostics(samp.fit2)
#
# Plot the fit.
#
plot(samp.fit2, pie=TRUE)

Class of Fitted Exponential Random Graph Mixed Models

Description

A class ergmm to represent a fitted exponential random graph mixed model. The output of ergmm.

Details

There are methods summary.ergmm, print.ergmm, plot.ergmm, predict.ergmm, and as.mcmc.list.ergmm.

The structure of ergmm is as follows:

sample: An object of class ergmm.par.list containing the MCMC sample from the posterior. If the run had multiple threads, their output is concatenated.
mcmc.mle: A list containing the parameter configuration of the highest-likelihood MCMC iteration.
mcmc.pmode: A list containing the parameter configuration of the highest-joint-density (conditional on cluster assignments) MCMC iteration.
mkl: A list containing the MKL estimate.
model: A list containing the model that was fitted.
prior: A list containing the information about the prior distribution used. It can be passed as parameter prior to ergmm to reproduce the prior in a new fit.
control: A list containing the information about the model fit settings that do not affect the posterior distribution. It can be passed as parameter control to ergmm to reproduce control parameters in a new fit.
mle: A list containing the MLE, conditioned on cluster assignments.
pmode: A list containing the posterior mode, conditioned on cluster assignments.
burnin.start: A list containing the starting value for the burnin.
main.start: A list (or a list of lists, for a multithreaded run) containing the starting value for the sampling.

Edge Weight Distribution Families

Description

Family-link combinations supported by ergmm.

Details

Each supported family has a family of functions, of the form pY.-, lpY.-, EY.-, dlpY.deta.-, dlpY.ddispersion.-, lpYc.-, rsm.-, followed by the family's name, for the respective family's name, representing the family's likelihood, log-likelihood, expectation, derivative of log-likelihood with repect to the linear predictor, derivative of log-likelihood with respect to the dispersion parameter, log-normalizing-constant, and random sociomatrix generation functions.

On the C side, similar functions exist, but becuase of static typing, are also provided for “continuous” versions of those families. These should not be used on their own, but are used in estimating MKL positions from the posterior distribution.

Family-link combinations

ID	`C` name	R name	Type	Family	Link
1	`Bernoulli_logit`	`Bernoulli.logit`	Discrete	Bernoulli	logit
2	`binomial_logit`	`binomial.logit`	Discrete	binomial	logit
3	`Poisson_log`	`Poisson.log`	Discrete	Possion	log
4	`Bernoulli_cont_logit`	NA	Continuous	Bernoulli	logit
5	`binomial_cont_logit`	NA	Continuous	binomial	logit
6	`Poisson_cont_log`	NA	Continuous	Possion	log
7	`normal_identity`	`normal.identity`	Continuous	normal	identity

.link can be omited when not ambiguous. Some families require an appropriate fam.par argument to be supplied to ergmm:

binomial families: a mandatory trials parameter for the number of trials (same for every dyad) whose success the response counts represent
normal: a mandatory prior.var and prior.var.df parameter for the prior scale and degrees of freedom of the variance of the dyad values

Draw a pie chart at a specified location.

Description

Used by plot.ergmm to draw pie charts to visualize soft clusterings when pie=TRUE. Exported as a courtesy to dependent packages.

Usage

ergmm.drawpie(center, radius, probs, n = 50, cols = seq_along(probs), ...)
ergmm.drawpie(center, radius, probs, n = 50, cols = seq_along(probs), ...)

Arguments

`center`	A numeric vector of length 2, specifying the horizontal and the vertical coordinates of its center.
`radius`	Radius of the pie chart.
`probs`	A vector of probabilities/weights of each sector; they do not have to sum to 1.
`n`	Number of points to use to approximate the "circle".
`cols`	A vector of colors to use for the sectors.
`...`	Additional arguments, currently unused.

Author(s)

See COPYRIGHT.

Examples


plot(c(0,sum(1:11))*2,c(-10,10),type="n",asp=1)
for(i in 1:10) ergmm.drawpie(c(sum(1:i)*2,0), radius=i, probs=1:(i+1))

plot(c(0,sum(1:11))*2,c(-10,10),type="n",asp=1)
for(i in 1:10) ergmm.drawpie(c(sum(1:i)*2,0), radius=i, probs=1:(i+1))

A List of ERGMM Parameter Configuration

Description

A class ergmm.par.list to represent a series of parameter configurations for the same exponential random graph mixed model.

Usage

as.ergmm.par.list(x, ...)

## S3 method for class 'ergmm.par.list'
x[i]

## S3 method for class 'ergmm.par.list'
length(x)

## S3 method for class 'ergmm.par.list'
x[[i]]

## S3 method for class 'ergmm.par.list'
unstack(x, ...)
as.ergmm.par.list(x, ...)

## S3 method for class 'ergmm.par.list'
x[i]

## S3 method for class 'ergmm.par.list'
length(x)

## S3 method for class 'ergmm.par.list'
x[[i]]

## S3 method for class 'ergmm.par.list'
unstack(x, ...)

Arguments

`x`	an `ergm.par.list` object.
`...`	extra arguments, currently unused.
`i`	index of the iteration to extract.

Details

[[ operator with a numeric or integer index returns a list with the the configuration with that index. [ operator given a numeric vector returns a ergmm.par.list object with the subset of configurations with the indices given.

The structure of ergmm.par.list is derived from named lists, with each entry having an additional dimension (always the first one), indexed by configuration. That is, scalars become vectors, vectors become matrixes with the original vectors in rows, and matrices become 3-dimensional arrays, with the original matrices indexed by their first dimension. See term documentation for comon elements of these configurations. In some cases, such as when representing MCMC or optimization output, the object may also have some of the following elements:

mlp: $\log p(Y,Z,\beta,\mu,\sigma,\delta,\gamma,\sigma_\delta,\sigma_\gamma,|K)$ , the joint probability/density of network, the covariate coefficients, the latent space positions and parameters, and the random effects and their variances, conditional on cluster assignments.
lpY: $\log p(Y|\dots)$ , depending on the model, the log-probability or log-density of the network conditional on all the parameters.
lpZ: $\log p(Z|\mu,\sigma,K)$ , the log-density of latent space positions conditional on latent space or cluster parameters and cluster assignments.
lpbeta: $\log p(\beta)$ , the prior log-density of the covariate coefficients.
lpRE: $\log p(\delta,\gamma|\sigma_\delta,\sigma_\gamma)$ , the log-density of all random effects, conditional on their respective variances.
lpLV: $\log p(\mu,\sigma)$ , the prior log-density of latent space or cluster parameters (but not that of the cluster assignments).
lpREV: $\log p(\sigma_\delta,\sigma_\gamma)$ , the prior log-density of all random effect variances.
Z.rate: Proportion of single-vertex proposals accepted over the preceding interval.
beta.rate: Proportion of group proposals accepted over the preceding interval.

Auxiliary for Setting the ERGMM Prior

Description

Auxiliary function as user interface for ergmm prior specification. Typically only used when calling ergmm. It is used to supply the parameters of the prior distribution of the model, to overwrite those specified in the model formula, and to supply miscellaneous prior parameters.

Usage

ergmm.prior(..., adjust.beta.var = TRUE)
ergmm.prior(..., adjust.beta.var = TRUE)

Arguments

`...`	Prior distribution parameters. See term documentation and `ergmm()` for more information.
`adjust.beta.var`	A shortcut: whether the prior variance for each covariate coefficient should be divided by the mean square of that covariate. This adjustment affects those variances specified in the formula or by default, but not those specified through the `prior=` argument.

Value

A list with the arguments as elements.

Euclidean distance latent space, with optional clustering

Description

Adds a term to the model equal to the negative Eucledean distance $-||Z_i-Z_j||$ , where $Z_i$ and $Z_j$ are the positions of their respective actors in an unobserved social space. These positions may optionally have a finite spherical Gaussian mixture clustering structure. This term was previously called latent.

Important: This term works in latentnet's ergmm() only. Using it in ergm() will result in an error.

Usage

# binary: euclidean(d, G=0, var.mul=1/8, var=NULL, var.df.mul=1, var.df=NULL,
#             mean.var.mul=1, mean.var=NULL, pK.mul=1, pK=NULL)

# valued: euclidean(d, G=0, var.mul=1/8, var=NULL, var.df.mul=1, var.df=NULL,
#             mean.var.mul=1, mean.var=NULL, pK.mul=1, pK=NULL)
# binary: euclidean(d, G=0, var.mul=1/8, var=NULL, var.df.mul=1, var.df=NULL,
#             mean.var.mul=1, mean.var=NULL, pK.mul=1, pK=NULL)

# valued: euclidean(d, G=0, var.mul=1/8, var=NULL, var.df.mul=1, var.df=NULL,
#             mean.var.mul=1, mean.var=NULL, pK.mul=1, pK=NULL)

Arguments

`d`	The dimension of the latent space.
`G`	The number of groups (0 for no clustering).
`var.mul`	In the absence of `var`, this argument will be used as a scaling factor for a function of average cluster size and latent space dimension to set `var`. To set it in the `prior` argument to `ergmm`, use `Z.var.mul`.
`var`	If given, the scale parameter for the scale-inverse-chi-squared prior distribution of the within-cluster variance. To set it in the `prior` argument to `ergmm`, use `Z.var`.
`var.df.mul`	In the absence of `var.df`, this argument is the multiplier for the square root of average cluster size, which serves in place of `var.df`. To set it in the `prior` argument to `ergmm`, use `Z.var.df.mul`.
`var.df`	The degrees of freedom parameter for the scale-inverse-chi-squared prior distribution of the within-cluster variance. To set it in the `prior` argument to `ergmm`, use `Z.var.df`.
`mean.var.mul`	In the absence of `mean.var`, the multiplier for a function of number of vertices and latent space dimension to set `mean.var`. To set it in the `prior` argument to `ergmm`, use `Z.mean.var.mul`.
`mean.var`	The variance of the spherical Gaussian prior distribution of the cluster means. To set it in the `prior` argument to `ergmm`, use `Z.mean.var`.
`pK.mul`	In the absence of `pK`, this argument is the multiplier for the square root of the average cluster size, which is used as `pK`. To set it in the `prior` argument to `ergmm`, use `Z.pK`.
`pK`	The parameter of the Dirichilet prior distribution of cluster assignment probabilities. To set it in the `prior` argument to `ergmm`, use `Z.pK`.

Details

The following parameters are associated with this term:

Z: Numeric matrix with rows being latent space positions.
Z.K (when $\code{G}>0$ ): Integer vector of cluster assignments.
Z.mean (when $\code{G}>0$ ): Numeric matrix with rows being cluster means.
Z.var (when $\code{G}>0$ ): Depending on the model, either a numeric vector with within-cluster variances or a numeric scalar with the overal latent space variance.
Z.pK (when $\code{G}>0$ ): Numeric vector of probabilities of a vertex being in a particular cluster.

Squared euclidean distance latent space, with optional clustering

Description

Adds a term to the model equal to the negative Eucledean distance $-||Z_i-Z_j||^2$ , where $Z_i$ and $Z_j$ are the positions of their respective actors in an unobserved social space. These positions may optionally have a finite spherical Gaussian mixture clustering structure. This term was previously called latent.

Important: This term works in latentnet's ergmm() only. Using it in ergm() will result in an error.

Usage

# binary: euclidean(d, G=0, var.mul=1/8, var=NULL, var.df.mul=1, var.df=NULL,
#             mean.var.mul=1, mean.var=NULL, pK.mul=1, pK=NULL)

# valued: euclidean(d, G=0, var.mul=1/8, var=NULL, var.df.mul=1, var.df=NULL,
#             mean.var.mul=1, mean.var=NULL, pK.mul=1, pK=NULL)
# binary: euclidean(d, G=0, var.mul=1/8, var=NULL, var.df.mul=1, var.df=NULL,
#             mean.var.mul=1, mean.var=NULL, pK.mul=1, pK=NULL)

# valued: euclidean(d, G=0, var.mul=1/8, var=NULL, var.df.mul=1, var.df=NULL,
#             mean.var.mul=1, mean.var=NULL, pK.mul=1, pK=NULL)

Arguments

`d`	The dimension of the latent space.
`G`	The number of groups (0 for no clustering).
`var.mul`	In the absence of `var`, this argument will be used as a scaling factor for a function of average cluster size and latent space dimension to set `var`. To set it in the `prior` argument to `ergmm`, use `Z.var.mul`.
`var`	If given, the scale parameter for the scale-inverse-chi-squared prior distribution of the within-cluster variance. To set it in the `prior` argument to `ergmm`, use `Z.var`.
`var.df.mul`	In the absence of `var.df`, this argument is the multiplier for the square root of average cluster size, which serves in place of `var.df`. To set it in the `prior` argument to `ergmm`, use `Z.var.df.mul`.
`var.df`	The degrees of freedom parameter for the scale-inverse-chi-squared prior distribution of the within-cluster variance. To set it in the `prior` argument to `ergmm`, use `Z.var.df`.
`mean.var.mul`	In the absence of `mean.var`, the multiplier for a function of number of vertices and latent space dimension to set `mean.var`. To set it in the `prior` argument to `ergmm`, use `Z.mean.var.mul`.
`mean.var`	The variance of the spherical Gaussian prior distribution of the cluster means. To set it in the `prior` argument to `ergmm`, use `Z.mean.var`.
`pK.mul`	In the absence of `pK`, this argument is the multiplier for the square root of the average cluster size, which is used as `pK`. To set it in the `prior` argument to `ergmm`, use `Z.pK`.
`pK`	The parameter of the Dirichilet prior distribution of cluster assignment probabilities. To set it in the `prior` argument to `ergmm`, use `Z.pK`.

Details

The following parameters are associated with this term:

Z: Numeric matrix with rows being latent space positions.
Z.K (when $\code{G}>0$ ): Integer vector of cluster assignments.
Z.mean (when $\code{G}>0$ ): Numeric matrix with rows being cluster means.
Z.var (when $\code{G}>0$ ): Depending on the model, either a numeric vector with within-cluster variances or a numeric scalar with the overal latent space variance.
Z.pK (when $\code{G}>0$ ): Numeric vector of probabilities of a vertex being in a particular cluster.

Conduct Goodness-of-Fit Diagnostics on a Exponential Family Random Graph Mixed Model Fit

Description

gof calculates $p$ -values for geodesic distance, degree, and reachability summaries to diagnose the goodness-of-fit of exponential family random graph mixed models. See ergmm for more information on these models.

Usage

## S3 method for class 'ergmm'
gof(
  object,
  ...,
  nsim = 100,
  GOF = ~idegree + odegree + distance,
  verbose = FALSE
)
## S3 method for class 'ergmm'
gof(
  object,
  ...,
  nsim = 100,
  GOF = ~idegree + odegree + distance,
  verbose = FALSE
)

Arguments

`object`	an `ergmm` object (returned by `ergmm`).
`...`	Additional arguments, to be passed to lower-level functions in the future.
`nsim`	The number of simulations to use for the MCMC $p$ -values. This is the size of the sample of graphs to be randomly drawn from the distribution specified by the object on the set of all graphs.
`GOF`	formula; an formula object, of the form `~ <model terms>` specifying the statistics to use to diagnosis the goodness-of-fit of the model. They do not need to be in the model formula specified in `formula`, and typically are not. Examples are the degree distribution ("degree"), minimum geodesic distances ("dist"), and shared partner distributions ("espartners" and "dspartners"). For the details on the possible `<model terms>`, see `ergm-terms`.
`verbose`	Provide verbose information on the progress of the simulation.

Details

A sample of graphs is randomly drawn from the posterior of the ergmm.

A plot of the summary measures is plotted. More information can be found by looking at the documentation of ergm.

Value

gof and gof.ergmm return an object of class gof. This is a list of the tables of statistics and $p$ -values. This is typically plotted using plot.gof.

Examples



#
data(sampson)
#
# test the gof.ergm function
#
samplike.fit <- ergmm(samplike ~ euclidean(d=2,G=3),
                      control=ergmm.control(burnin=1000,interval=5))
samplike.fit
summary(samplike.fit)

#
# Plot the probabilities first
#
monks.gof <- gof(samplike.fit)
monks.gof
#
# Place all three on the same page
# with nice margins
#
par(mfrow=c(1,3))
par(oma=c(0.5,2,1,0.5))
#
plot(monks.gof)
#
# And now the odds 
#
plot(monks.gof, plotlogodds=TRUE)

#
data(sampson)
#
# test the gof.ergm function
#
samplike.fit <- ergmm(samplike ~ euclidean(d=2,G=3),
                      control=ergmm.control(burnin=1000,interval=5))
samplike.fit
summary(samplike.fit)

#
# Plot the probabilities first
#
monks.gof <- gof(samplike.fit)
monks.gof
#
# Place all three on the same page
# with nice margins
#
par(mfrow=c(1,3))
par(oma=c(0.5,2,1,0.5))
#
plot(monks.gof)
#
# And now the odds 
#
plot(monks.gof, plotlogodds=TRUE)

Intercept

Description

This term serves as an intercept term, is included by default (though, as in lm, it can be excluded by adding +0 or -1 into the model formula). It adds one covariate to the model, for which x[i,j]=1 for all i and j.

It can be used explicitly to set prior mean and variance for the intercept term.

This term differs from the ergm's edges-ergmTerm term if the network has self-loops.

Important: This term works in latentnet's ergmm() only. Using it in ergm() will result in an error.

Usage

# binary: 1(mean=0, var=9)

# binary: Intercept(mean=0, var=9)

# binary: intercept(mean=0, var=9)

# valued: 1(mean=0, var=9)

# valued: Intercept(mean=0, var=9)

# valued: intercept(mean=0, var=9)
# binary: 1(mean=0, var=9)

# binary: Intercept(mean=0, var=9)

# binary: intercept(mean=0, var=9)

# valued: 1(mean=0, var=9)

# valued: Intercept(mean=0, var=9)

# valued: intercept(mean=0, var=9)

Arguments

mean, var

prior mean and variance.

Edge covariates for the latent model

Description

This term adds multiple covariates to the model, one for each of (a subset of) the unique values of the attrname attribute (or each combination of the attributes given). Each of these covariates has x[i,i]=1 if attrname(i)==l, where l is that covariate's level, and x[i,j]=0 otherwise. To include all attribute values se base=0 – because the sum of all such statistics equals twice the number of self-loops and hence a linear dependency would arise in any model also including loops. Thus, the base argument tells which value(s) (numbered in order according to the sort function) should be omitted. The default value, base=1, means that the smallest (i.e., first in sorted order) attribute value is omitted. For example, if the “fruit” factor has levels “orange”, “apple”, “banana”, and “pear”, then to add just two terms, one for “apple” and one for “pear”, then set “banana” and “orange” to the base (remember to sort the values first) by using nodefactor("fruit", base=2:3). For an analogous term for quantitative vertex attributes, see nodecov.attrname is a character string giving the name of a numeric (not categorical) attribute in the network's vertex attribute list. This term adds one covariate to the model, for which x[i,i]=attrname(i) and x[i,j]=0 for i!=j. This term only makes sense if the network has self-loops.

latentcov can be called more than once, to model the effects of multiple covariates. Note that some covariates can be more conveniently specified using the following terms.

Important: This term works in latentnet's ergmm() only. Using it in ergm() will result in an error.

Usage

# binary: latentcov(x, attrname=NULL, mean=0, var=9)

# valued: latentcov(x, attrname=NULL, mean=0, var=9)
# binary: latentcov(x, attrname=NULL, mean=0, var=9)

# valued: latentcov(x, attrname=NULL, mean=0, var=9)

Arguments

`x`	either a matrix of covariates on each pair of vertices, a network, or an edge attribute.
`attrname`	optional argument to provide the name of the edge attribute.
`mean`, `var`	prior mean and variance.

Covariate effect on self-loops

Description

This term adds one covariate to the model, for which x[i,i]=attrname(i) and x[i,j]=0 for i!=j. This term only makes sense if the network has self-loops.

Important: This term works in latentnet's ergmm() only. Using it in ergm() will result in an error.

Usage

# binary: loopcov(attrname, mean=0, var=9)

# valued: loopcov(attrname, mean=0, var=9)
# binary: loopcov(attrname, mean=0, var=9)

# valued: loopcov(attrname, mean=0, var=9)

Arguments

`attrname`	a character string giving the name of a numeric (not categorical) attribute in the network's vertex attribute list.
`mean`, `var`	prior mean and variance.

Factor attribute effect on self-loops

Description

Important: This term works in latentnet's ergmm() only. Using it in ergm() will result in an error.

Usage

# binary: loopfactor(attrname, mean=0, var=9)

# valued: loopfactor(attrname, mean=0, var=9)
# binary: loopfactor(attrname, mean=0, var=9)

# valued: loopfactor(attrname, mean=0, var=9)

Arguments

`attrname`	a character vector giving one or more names of categorical attributes in the network's vertex attribute list.
`mean`, `var`	prior mean and variance.

Self-loops

Description

Effect of the dyad being a self-loop (i.e., $(i,i)$ ).

Important: This term works in latentnet's ergmm() only. Using it in ergm() will result in an error.

Usage

# binary: loops(mean=0, var=9)

# valued: loops(mean=0, var=9)
# binary: loops(mean=0, var=9)

# valued: loops(mean=0, var=9)

Arguments

mean, var

prior mean and variance.

Conduct MCMC diagnostics on an ERGMM fit

Description

This function creates simple diagnostic plots for the MCMC sampled statistics produced from a fit. It also prints the Raftery-Lewis diagnostics, indicates if they are sufficient, and suggests the run length required.

Usage

## S3 method for class 'ergmm'
mcmc.diagnostics(
  object,
  which.diags = c("cor", "acf", "trace", "raftery"),
  burnin = FALSE,
  which.vars = NULL,
  vertex.i = c(1),
  ...
)
## S3 method for class 'ergmm'
mcmc.diagnostics(
  object,
  which.diags = c("cor", "acf", "trace", "raftery"),
  burnin = FALSE,
  which.vars = NULL,
  vertex.i = c(1),
  ...
)

Arguments

`object`	An object of class `ergmm`.
`which.diags`	A list of diagnostics to produce. "cor" is the correlation matrix of the statistics, "acf" plots the autocorrelation functions, "trace" produces trace plots and density estimates, and "raftery" produces the Raftery-Lewis statistics.
`burnin`	If not `FALSE`, rather than perform diagnostics on the sampling run, performs them on the pilot run whose index is given.
`which.vars`	A named list mapping variable names to the indices to include. If given, overrides the defaults and all arguments that follow.
`vertex.i`	A numeric vector of vertices whose latent space coordinates and random effects to include.
`...`	Additional arguments. None are supported at the moment.

Details

Produces the plots per which.diags. Autocorrelation function that is printed if "acf" is requested is for lags 0 and interval.

Value

mcmc.diagnostics.ergmm returns a table of Raftery-Lewis diagnostics.

Examples



#
data(sampson)
#
# test the mcmc.diagnostics function
#
gest <- ergmm(samplike ~ euclidean(d=2),
              control=ergmm.control(burnin=1000,interval=5))
summary(gest)
#
# Plot the traces and densities
#
mcmc.diagnostics(gest)


#
data(sampson)
#
# test the mcmc.diagnostics function
#
gest <- ergmm(samplike ~ euclidean(d=2),
              control=ergmm.control(burnin=1000,interval=5))
summary(gest)
#
# Plot the traces and densities
#
mcmc.diagnostics(gest)

Merge two or more replications of ERGMM fits

Description

A merge method for ergmm objects, constructing an ergmm object containing the combined MCMC output (and derived estimates) of several ergmm objects produced with the same input parameters but different starting values, random seeds, etc..

Usage

## S3 method for class 'ergmm'
merge(x, y, ..., verbose = FALSE)
## S3 method for class 'ergmm'
merge(x, y, ..., verbose = FALSE)

Arguments

`x`	The first `ergmm` object to be merged.
`y`	The second `ergmm` object to be merged.
`...`	Additional `ergmm` objects to be merged.
`verbose`	If `TRUE`, marks the progress of merging.

Value

An object of class ergmm.

Examples



data(sampson)
# Run two short MCMC-based fits.
samp.fit1 <- ergmm(samplike ~ euclidean(d=2, G=3),
  control=ergmm.control(burnin=1000,interval=10,sample.size=2000))
samp.fit2 <- ergmm(samplike ~ euclidean(d=2, G=3),
  control=ergmm.control(burnin=1000,interval=10,sample.size=2000))

# Combine them, and summarize the result.
samp.fit <-  merge(samp.fit1,samp.fit2)
summary(samp.fit)


data(sampson)
# Run two short MCMC-based fits.
samp.fit1 <- ergmm(samplike ~ euclidean(d=2, G=3),
  control=ergmm.control(burnin=1000,interval=10,sample.size=2000))
samp.fit2 <- ergmm(samplike ~ euclidean(d=2, G=3),
  control=ergmm.control(burnin=1000,interval=10,sample.size=2000))

# Combine them, and summarize the result.
samp.fit <-  merge(samp.fit1,samp.fit2)
summary(samp.fit)

Plotting Method for class ERGMM

Description

plot.ergmm is the plotting method for ergmm objects. For latent models, this plots the minimum Kullback-Leibler positions by default. The maximum likelihood, posterior mean, posterior mode, or a particular iteration's or configuration's positions can be used instead, or pie charts of the posterior probabilities of cluster membership can be shown. See ergmm for more information on how to fit these models.

Usage

## S3 method for class 'ergmm'
plot(
  x,
  ...,
  vertex.cex = 1,
  vertex.sides = 16 * ceiling(sqrt(vertex.cex)),
  what = "mkl",
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  zlab = NULL,
  xlim = NULL,
  ylim = NULL,
  zlim = NULL,
  object.scale = formals(plot.network.default)[["object.scale"]],
  pad = formals(plot.network.default)[["pad"]],
  cluster.col = c("red", "green", "blue", "cyan", "magenta", "orange", "yellow",
    "purple"),
  vertex.col = NULL,
  print.formula = TRUE,
  edge.col = 8,
  Z.ref = NULL,
  Z.K.ref = NULL,
  zoom.on = NULL,
  pie = FALSE,
  labels = FALSE,
  rand.eff = NULL,
  rand.eff.cap = NULL,
  plot.means = TRUE,
  plot.vars = TRUE,
  suppress.axes = FALSE,
  jitter1D = 1,
  curve1D = TRUE,
  use.rgl = FALSE,
  vertex.3d.cex = 1/20,
  edge.plot3d = TRUE,
  suppress.center = FALSE,
  density.par = list()
)
## S3 method for class 'ergmm'
plot(
  x,
  ...,
  vertex.cex = 1,
  vertex.sides = 16 * ceiling(sqrt(vertex.cex)),
  what = "mkl",
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  zlab = NULL,
  xlim = NULL,
  ylim = NULL,
  zlim = NULL,
  object.scale = formals(plot.network.default)[["object.scale"]],
  pad = formals(plot.network.default)[["pad"]],
  cluster.col = c("red", "green", "blue", "cyan", "magenta", "orange", "yellow",
    "purple"),
  vertex.col = NULL,
  print.formula = TRUE,
  edge.col = 8,
  Z.ref = NULL,
  Z.K.ref = NULL,
  zoom.on = NULL,
  pie = FALSE,
  labels = FALSE,
  rand.eff = NULL,
  rand.eff.cap = NULL,
  plot.means = TRUE,
  plot.vars = TRUE,
  suppress.axes = FALSE,
  jitter1D = 1,
  curve1D = TRUE,
  use.rgl = FALSE,
  vertex.3d.cex = 1/20,
  edge.plot3d = TRUE,
  suppress.center = FALSE,
  density.par = list()
)

Arguments

`x`	an R object of class `ergmm`. See documentation for `ergmm`.
`...`	Other optional arguments passed to the `plot.network` function.
`what`	Character vector, integer, or a list that specifies the point estimates to be used. Can be one of the follwoing: `"mkl"` This is the defult. Plots the Minimum Kulblack-Leibler divergence values. `"start"`,`"burnin.start"` Plots the starting configuration. `"sampling.start"` Plots the starting configuration of the sampling phase (the last burnin configuration). `"mle"` Plots the maximum likelihood estimates. Random effects are treated as fixed. `"pmean"` Plots the posterior means. `"pmode"` Plots the conditional posterior mode. `"cloud"` Plots the “cloud” of latent space position draws, with their cluster colors. `"density"` Plots density and contours of the posterior latent positions, and, in cluster models, each cluster. `list` Plots the configuration contained in the list. integer Plots the configuration of `what`th MCMC draw stored in `x`.
`main`, `vertex.cex`, `vertex.col`, `xlim`, `ylim`, `vertex.sides`	Arguments passed to `plot.network`, whose defaults differ from those of `plot.network`.
`zlim`, `zlab`	Limits and labels for the third latent space dimension or principal component, if `use.rgl=TRUE`.
`object.scale`, `pad`, `edge.col`, `xlab`, `ylab`	Arguments passed to `plot.network`, whose defaults differ from those of `plot.network`.
`cluster.col`	A vector of colors used to distinguish clusters in a latent cluster model.
`print.formula`	Whether the formula based on which the `x` was fitted should be printed under the main title. Defaults to `TRUE`.
`Z.ref`	If given, rotates the the latent positions to the nearest configuration to this one before plotting.
`Z.K.ref`	If given, relabels the clusters to the nearest configuration to this one before plotting.
`zoom.on`	If given a list of vertex indices, sets the plotting region to the smallest that can fit those vertices.
`pie`	For latent clustering models, each node is drawn as a pie chart representing the probabilities of cluster membership.
`labels`	Whether vertex labels should be displayed. Defaults to `FALSE`.
`rand.eff`	A character vector selecting "sender", "receiver", "sociality", or "total" random effects. Each vertex is scaled such that its area is proportional to the odds ratio due to its selected random effect.
`rand.eff.cap`	If not `NULL` and `rand.eff` is given, limits the scaling of the plotting symbol due to random effect to the given value.
`plot.means`	Whether cluster means are plotted for latent cluster models. The "+" character is used. Defaults to `TRUE`.
`plot.vars`	Whether circles with radius equal to the square root of posterior latent or intracluster variance estimates are plotted. Defaults to `TRUE`.
`suppress.axes`	Whether axes should not be drawn. Defaults to `FALSE`. (Axes are drawn.)
`jitter1D`	For 1D latent space fits, it often helps to jitter the positions for visualization. This option controls the amount of jitter.
`curve1D`	Controls whether the edges in 1D latent space fits are plotted as curves. Defaults to `TRUE`.
`use.rgl`	Whether the package rgl should be used to plot fits for latent space dimension 3 or higher in 3D. Defaults to `FALSE`. If set to `TRUE`, argument `pie` has no effect.
`vertex.3d.cex`	Controls the size of the plotting symbol when `use.rgl=TRUE`.
`edge.plot3d`	If `TRUE` (the default) edges or arcs in a 3D plot will be drawn. Otherwise, only vertices and clusters.
`suppress.center`	Suppresses the plotting of "+" at the origin. Defaults to `FALSE`.
`density.par`	A list of optional parameters for density plots: `totaldens` Whether the overal density of latent space positions should be plotted. Defaults to `TRUE`. `subdens` Whether the densities of latent space positions broken down by cluster should be plotted. Defaults to `TRUE`. `mfrow` When plotting multiple clusters' densities, passed to `par`

Details

At this time, no plotting non-latent-space model fits is not supported.

Plots the results of an ergmm fit.

More information can be found by looking at the documentation of ergmm.

For bipartite networks, the events are marked with a bullet (small black circle) inside the plotting symbol.

Value

If applicable, invisibly returns the vertex positions plotted.

Examples



#
# Using Sampson's Monk data, let's fit a 
# simple latent position model
#
data(sampson)
#
# Using Sampson's Monk data, let's fit a
# latent clustering random effects model
# Store the burn-in.
samp.fit <- ergmm(samplike ~ euclidean(d=2, G=3)+rreceiver,
                  control=ergmm.control(store.burnin=TRUE))
#
# See if we have convergence in the MCMC
mcmc.diagnostics(samp.fit)
# We can also plot the burn-in:
for(i in samp.fit$control$pilot.runs) mcmc.diagnostics(samp.fit,burnin=i)
#
# Plot the resulting fit.
#
plot(samp.fit,labels=TRUE,rand.eff="receiver")
plot(samp.fit,pie=TRUE,rand.eff="receiver")
plot(samp.fit,what="pmean",rand.eff="receiver")
plot(samp.fit,what="cloud",rand.eff="receiver")
plot(samp.fit,what="density",rand.eff="receiver")
plot(samp.fit,what=5,rand.eff="receiver")

## Not run: 
# Fit a 3D latent space model to Sampson's Monks
samp.fit3 <- ergmm(samplike ~ euclidean(d=3))

# Plot the first two principal components of the
# latent space positions
plot(samp.fit,use.rgl=FALSE)
# Plot the resulting fit in 3D
plot(samp.fit,use.rgl=TRUE)

## End(Not run)

#
# Using Sampson's Monk data, let's fit a 
# simple latent position model
#
data(sampson)
#
# Using Sampson's Monk data, let's fit a
# latent clustering random effects model
# Store the burn-in.
samp.fit <- ergmm(samplike ~ euclidean(d=2, G=3)+rreceiver,
                  control=ergmm.control(store.burnin=TRUE))
#
# See if we have convergence in the MCMC
mcmc.diagnostics(samp.fit)
# We can also plot the burn-in:
for(i in samp.fit$control$pilot.runs) mcmc.diagnostics(samp.fit,burnin=i)
#
# Plot the resulting fit.
#
plot(samp.fit,labels=TRUE,rand.eff="receiver")
plot(samp.fit,pie=TRUE,rand.eff="receiver")
plot(samp.fit,what="pmean",rand.eff="receiver")
plot(samp.fit,what="cloud",rand.eff="receiver")
plot(samp.fit,what="density",rand.eff="receiver")
plot(samp.fit,what=5,rand.eff="receiver")

## Not run: 
# Fit a 3D latent space model to Sampson's Monks
samp.fit3 <- ergmm(samplike ~ euclidean(d=3))

# Plot the first two principal components of the
# latent space positions
plot(samp.fit,use.rgl=FALSE)
# Plot the resulting fit in 3D
plot(samp.fit,use.rgl=TRUE)

## End(Not run)

Predicted Dyad Values for an ERGMM.

Description

Returns a matrix of expected dyad values based on an ERGMM fit.

Usage

## S3 method for class 'ergmm'
predict(object, ..., type = "post")
## S3 method for class 'ergmm'
predict(object, ..., type = "post")

Arguments

`object`	An obejct of class `ergmm`.
`...`	Additional arguments. Currently unused.
`type`	One of "mkl", "start", "mle", "pmean", "mkl", "pmode", "post", an index of the iteration to use, or a list, for the configuration of parameters based on which the prediction is made. An exception is "post", which computes the expected dyad values integrated over the posterior.

Value

A sociomatrix of predicted values. Note that predictions are made for unobserved values (whether structural zeros or unobserved dyads).

Examples



data(sampson)
monks.fit<-ergmm(samplike~euclidean(d=2,G=3),tofit="mcmc")
heatmap(predict(monks.fit),Rowv=NA,Colv=NA)


data(sampson)
monks.fit<-ergmm(samplike~euclidean(d=2,G=3),tofit="mcmc")
heatmap(predict(monks.fit),Rowv=NA,Colv=NA)

Receiver covariate effect

Description

Deprecated for networks without self-loops. Use nodeicov-ergmTerm, nodeifactor-ergmTerm, nodecov-ergmTerm or nodefactor-ergmTerm instead.

If the attribute is numeric, this term adds one covariate to the model equaling attrname(i). If the attribute is not numeric or force.factor==TRUE, this term adds $p-1$ covariates to the model, where $p$ is the number of unique values of attrname. The $k$ th such covariate has the value attrname(i) == value(k+1), where value(k) is the $k$ th smallest unique value of the attrname attribute. This term only makes sense if the network is directed.

Important: This term works in latentnet's ergmm() only. Using it in ergm() will result in an error.

Usage

# binary: receivercov(attrname, force.factor=FALSE, mean=0, var=9)

# valued: receivercov(attrname, force.factor=FALSE, mean=0, var=9)
# binary: receivercov(attrname, force.factor=FALSE, mean=0, var=9)

# valued: receivercov(attrname, force.factor=FALSE, mean=0, var=9)

Arguments

`attrname`	a character string giving the name of an attribute in the network's vertex attribute list.
`force.factor`	logical, indicating if `attrname`'s value should be interpreted as categorical even if numeric.
`mean`, `var`	prior mean and variance.

Note

This term can only be used with directed networks.

Random receiver effect

Description

Adds a random receiver effect to the model, with normal prior centered around $0$ and a variance that is estimated. Can only be used on directed networks.

Important: This term works in latentnet's ergmm() only. Using it in ergm() will result in an error.

Usage

# binary: rreceiver(var=1, var.df=3)

# valued: rreceiver(var=1, var.df=3)
# binary: rreceiver(var=1, var.df=3)

# valued: rreceiver(var=1, var.df=3)

Arguments

`var`	The scale parameter for the scale-inverse-chi-squared prior distribution of the receiver effect variance. To set it in the `prior` argument to `ergmm`, use `receiver.var`.
`var.df`	The degrees of freedom parameter for the scale-inverse-chi-squared prior distribution of the receiver effect variance. To set it in the `prior` argument to `ergmm`, use `receiver.var.df`.

Details

The following parameters are associated with this term:

receiver: Numeric vector of values of each vertex's random receiver effect.
receiver.var: Random receiver effect's variance.

Note

This term can only be used with directed networks.

Random sender effect

Description

Adds a random sender effect to the model, with normal prior centered around $0$ and a variance that is estimated. Can only be used on directed networks.

Important: This term works in latentnet's ergmm() only. Using it in ergm() will result in an error.

Usage

# binary: rsender(var=1, var.df=3)

# valued: rsender(var=1, var.df=3)
# binary: rsender(var=1, var.df=3)

# valued: rsender(var=1, var.df=3)

Arguments

`var`	The scale parameter for the scale-inverse-chi-squared prior distribution of the sender effect variance. To set it in the `prior` argument to `ergmm`, use `sender.var`.
`var.df`	The degrees of freedom parameter for the scale-inverse-chi-squared prior distribution of the sender effect variance. To set it in the `prior` argument to `ergmm`, use `sender.var.df`.

Details

The following parameters are associated with this term:

sender: Numeric vector of values of each vertex's random sender effect.
sender.var: Random sender effect's variance.

Note

This term can only be used with directed networks.

Random sociality effect

Description

Adds a random sociality effect to the model, with normal prior centered around $0$ and a variance that is estimated. Can only be used on directed networks.

Important: This term works in latentnet's ergmm() only. Using it in ergm() will result in an error.

Usage

# binary: rsociality(var=1, var.df=3)

# valued: rsociality(var=1, var.df=3)
# binary: rsociality(var=1, var.df=3)

# valued: rsociality(var=1, var.df=3)

Arguments

`var`	The scale parameter for the scale-inverse-chi-squared prior distribution of the sociality effect variance. To set it in the `prior` argument to `ergmm`, use `sociality.var`.
`var.df`	The degrees of freedom parameter for the scale-inverse-chi-squared prior distribution of the sociality effect variance. To set it in the `prior` argument to `ergmm`, use `sociality.var.df`.

Details

The following parameters are associated with this term:

sociality: Numeric vector of values of each vertex's random sociality effect.
sociality.var: Random sociality effect's variance.

Sender covariate effect

Description

Deprecated for networks without self-loops. Use nodeocov-ergmTerm, nodeofactor-ergmTerm, nodecov-ergmTerm or nodefactor-ergmTerm instead.

Important: This term works in latentnet's ergmm() only. Using it in ergm() will result in an error.

Usage

# binary: sendercov(attrname, force.factor=FALSE, mean=0, var=9)

# valued: sendercov(attrname, force.factor=FALSE, mean=0, var=9)
# binary: sendercov(attrname, force.factor=FALSE, mean=0, var=9)

# valued: sendercov(attrname, force.factor=FALSE, mean=0, var=9)

Arguments

`attrname`	a character string giving the name of an attribute in the network's vertex attribute list.
`force.factor`	logical, indicating if `attrname`'s value should be interpreted as categorical even if numeric.
`mean`, `var`	prior mean and variance.

Note

This term can only be used with directed networks.

Draw from the distribution of an Exponential Random Graph Mixed Model

Description

If passed a ergmm fit object, simulate is used to simulate networks from the posterior of an exponetial random graph mixed model fit. Alternatively, a ergmm.model can be passed to simulate based on a particular parametr configuration.

Usage

## S3 method for class 'ergmm'
simulate(object, nsim = 1, seed = NULL, ...)

## S3 method for class 'ergmm.model'
simulate(object, nsim = 1, seed = NULL, par, prior = list(), ...)
## S3 method for class 'ergmm'
simulate(object, nsim = 1, seed = NULL, ...)

## S3 method for class 'ergmm.model'
simulate(object, nsim = 1, seed = NULL, par, prior = list(), ...)

Arguments

`object`	either an object of class `ergmm` for posterior simulation, or an object of class `ergmm.model` for a specific model.
`nsim`	number of networks to draw (independently)
`seed`	random seed to use; defaults to using the current state of the random number generator
`...`	Additional arguments. Currently unused.
`par`	a list with the parameter configuration based on which to simulate
`prior`	a list with the prior distribution parameters that deviate from their defaults

Details

A sample of networks is randomly drawn from the specified model. If a needed value of par is missing, it is generated from its prior distribution.

Value

If nsim = 1, simulate returns an object of class network. Otherwise, an object of class network.series that is a list consisting of the following elements:

`$formula`	The formula used to generate the sample.
`$networks`	A list of the generated networks.

Examples


#
# Fit a short MCMC run: just the MCMC.
#
data(sampson)
gest <- ergmm(samplike ~ euclidean(d=2,G=3),
              control=ergmm.control(burnin=100,interval=5,sample.size=100),tofit="mcmc")
#
# Draw from the posterior
#
g.sim <- simulate(gest)
plot(g.sim)
#
# Draw from the first draw from the posterior
#
g.sim <- with(gest,simulate(model,par=sample[[1]],prior=prior))
plot(g.sim)
#
# Fit a short MCMC run: just the MCMC.
#
data(sampson)
gest <- ergmm(samplike ~ euclidean(d=2,G=3),
              control=ergmm.control(burnin=100,interval=5,sample.size=100),tofit="mcmc")
#
# Draw from the posterior
#
g.sim <- simulate(gest)
plot(g.sim)
#
# Draw from the first draw from the posterior
#
g.sim <- with(gest,simulate(model,par=sample[[1]],prior=prior))
plot(g.sim)

Sociality covariate effect

Description

Deprecated for networks without self-loops. Use nodecov-ergmTerm or nodefactor-ergmTerm instead.

Important: This term works in latentnet's ergmm() only. Using it in ergm() will result in an error.

Usage

# binary: socialitycov(attrname, force.factor=FALSE, mean=0, var=9)

# valued: socialitycov(attrname, force.factor=FALSE, mean=0, var=9)
# binary: socialitycov(attrname, force.factor=FALSE, mean=0, var=9)

# valued: socialitycov(attrname, force.factor=FALSE, mean=0, var=9)

Arguments

`attrname`	a character string giving the name of an attribute in the network's vertex attribute list.
`force.factor`	logical, indicating if `attrname`'s value should be interpreted as categorical even if numeric.
`mean`, `var`	prior mean and variance.

ERGMM Fit Summaries

Description

summary.ergmm prodcues a summary of an ergmm object, including point estimates, standard errors, and BIC calculation.

Usage

## S3 method for class 'ergmm'
summary(
  object,
  point.est = c(if (!is.null(object[["mle"]])) "mle", if (!is.null(object[["sample"]]))
    c("pmean", "mkl")),
  quantiles = c(0.025, 0.975),
  se = "mle" %in% point.est,
  bic.eff.obs = c("ties", "dyads", "actors"),
  ...
)
## S3 method for class 'ergmm'
summary(
  object,
  point.est = c(if (!is.null(object[["mle"]])) "mle", if (!is.null(object[["sample"]]))
    c("pmean", "mkl")),
  quantiles = c(0.025, 0.975),
  se = "mle" %in% point.est,
  bic.eff.obs = c("ties", "dyads", "actors"),
  ...
)

Arguments

`object`	An `ergmm` object to be summarized.
`point.est`	Point estimates to compute: a character vector with some subset of `"mle"`, `"pmean"`, `"mkl"`, and `"pmode"`. Defaults to a concatenation of `"mle"` (if fit), `"pmean"`, and `"mkl"` (if MCMC was run).
`quantiles`	Posterior quantiles (credible intervals) to compute.
`se`	Whether to compute standard errors. Defaults to `TRUE` if MLE was fit.
`...`	Additional arguments.
`eff.obs`, `bic.eff.obs`	What effective sample size to use for BIC calculation? `"ties"` the number of non-missing ties in the network. This is the approach recommended by Handcock et al. (2007) and the default. Not well-defined for valued networks. `"dyads"` the number of non-missing dyads (potential ties) in the network. `"actors"` the number of actors in the network. The default prior to 2.7.0. a number to specify a specific sample size. `NULL` Don't compute the BIC at all. Mostly for internal use.

Details

Note that BIC computed for the random effects models uses the same formualtion as Handcock et al., so it is likely correct, but has not been peer-reviewed.

This BIC can be (reasonably) safely used to select the number of clusters or which fixed effects to include in the model. It is not clear whether it is appropriate to use this BIC to select the dimension of latent space and whether or not to include random actor effects. These considerations are independent of the bug described below.

Prior to version 2.7.0, there was a bug in BIC calculation that used $p + n(d+r+s)$ as the number of parameters in the likelihood (where $p$ is the number of fixed effects, $n$ the number of actors, $d$ , the latent space dimension, and $r$ and $s$ indicators of presence of sender and receiver (or sociality) effects). This value should have been just $p$ .

The following applications could have produced different results:

Using the BIC to select latent space dimension.
Using the BIC to decide whether or not to include random effects.

The following applications could not (i.e., would be off by a constant):

Using the BIC to select the number of clusters.
Using the BIC to select the fixed effects to be used.

Value

For summary, an object of class summary.ergmm. A print method is available.

The BICs are available as the element "bic" of the object returned.

bic.ergmm returns the BIC for the model directly.

References

Chris Fraley and Adrian E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611-631.

Examples



data(sampson)
# Fit the model for cluster sizes 1 through 4:
fits<-list(
           ergmm(samplike~euclidean(d=2,G=1)),
           ergmm(samplike~euclidean(d=2,G=2)),
           ergmm(samplike~euclidean(d=2,G=3)),
           ergmm(samplike~euclidean(d=2,G=4))
           )

## Not run: 
# Optionally, plot all fits.
lapply(fits,plot)

## End(Not run)

# Compute the BICs for the fits and plot them:
(bics<-reshape(
    as.data.frame(t(sapply(fits,
                           function(x)c(G=x$model$G,unlist(bic.ergmm(x))[c("Y","Z","overall")])))),
    list(c("Y","Z","overall")),idvar="G",v.names="BIC",timevar="Component",
    times=c("likelihood","clustering","overall"),direction="long"
    ))

with(bics,interaction.plot(G,Component,BIC,type="b",xlab="Clusters", ylab="BIC"))

# Summarize and plot whichever fit has the lowest overall BIC:
bestG<-with(bics[bics$Component=="overall",],G[which.min(BIC)])
summary(fits[[bestG]])
plot(fits[[bestG]])


data(sampson)
# Fit the model for cluster sizes 1 through 4:
fits<-list(
           ergmm(samplike~euclidean(d=2,G=1)),
           ergmm(samplike~euclidean(d=2,G=2)),
           ergmm(samplike~euclidean(d=2,G=3)),
           ergmm(samplike~euclidean(d=2,G=4))
           )

## Not run: 
# Optionally, plot all fits.
lapply(fits,plot)

## End(Not run)

# Compute the BICs for the fits and plot them:
(bics<-reshape(
    as.data.frame(t(sapply(fits,
                           function(x)c(G=x$model$G,unlist(bic.ergmm(x))[c("Y","Z","overall")])))),
    list(c("Y","Z","overall")),idvar="G",v.names="BIC",timevar="Component",
    times=c("likelihood","clustering","overall"),direction="long"
    ))

with(bics,interaction.plot(G,Component,BIC,type="b",xlab="Clusters", ylab="BIC"))

# Summarize and plot whichever fit has the lowest overall BIC:
bestG<-with(bics[bics$Component=="overall",],G[which.min(BIC)])
summary(fits[[bestG]])
plot(fits[[bestG]])

Read Highland Tribes

Description

A network of political alliances and enmities among the 16 Gahuku-Gama sub-tribes of Eastern Central Highlands of New Guinea, documented by Read (1954).

Format

An undirected network object with no loops, having the following attributes:

%v% "vertex.names": Character attribute with names of tribes.
%e% "pos": Logical attribute indicating an alliance relationship.
%e% "neg": Logical attribute indicating a hostile relationship ("rova").
%e% "sign": Numeric attribute coding -1 for enmity, 0 for no relationship, and 1 for alliance.
%e% "sign.012": Numeric attribute coding 0 for enmity, 1 for no relationship, and 2 for alliance.

Because of limitations of network objects, the object itself is a complete graph, and is thus meaningless if used directly or plotted.

Details

This network shows 3 clusters.

Source

http://vlado.fmf.uni-lj.si/pub/networks/data/UciNet/UciData.htm#gama, with corrections from Read (1954).

References

Taken from UCINET IV, which cites the following: Hage P. and Harary F. (1983). Structural models in anthropology. Cambridge: Cambridge University Press. (See p 56-60). Read K. (1954). Cultures of the central highlands, New Guinea. Southwestern Journal of Anthropology, 10, 1-43.

Examples



data(tribes)
# Only model positive ties:
tribes.fit<-ergmm(tribes~euclidean(d=2,G=3),response="pos")
# Edge color must be set manually, for green ties to represent alliance
# and for red ties to represent enmity.
plot(tribes.fit,edge.col=as.matrix(tribes,"pos",m="a")*3+as.matrix(tribes,"neg",m="a")*2,pie=TRUE)
# Model both positive and negative ties:
tribes.fit3<-ergmm(tribes~euclidean(d=2,G=3),response="sign.012",
                   family="binomial.logit",fam.par=list(trials=2))
# Edge color must be set manually, for green ties to represent alliance
# and for red ties to represent enmity.
plot(tribes.fit3,edge.col=as.matrix(tribes,"pos",m="a")*3+as.matrix(tribes,"neg",m="a")*2,pie=TRUE)


data(tribes)
# Only model positive ties:
tribes.fit<-ergmm(tribes~euclidean(d=2,G=3),response="pos")
# Edge color must be set manually, for green ties to represent alliance
# and for red ties to represent enmity.
plot(tribes.fit,edge.col=as.matrix(tribes,"pos",m="a")*3+as.matrix(tribes,"neg",m="a")*2,pie=TRUE)
# Model both positive and negative ties:
tribes.fit3<-ergmm(tribes~euclidean(d=2,G=3),response="sign.012",
                   family="binomial.logit",fam.par=list(trials=2))
# Edge color must be set manually, for green ties to represent alliance
# and for red ties to represent enmity.
plot(tribes.fit3,edge.col=as.matrix(tribes,"pos",m="a")*3+as.matrix(tribes,"neg",m="a")*2,pie=TRUE)

Package 'latentnet'

Help Index

latentnet: Latent Position and Cluster Models for Statistical Networks

Description

Details

Author(s)

References

See Also

Convert an ERGMM Object to an MCMC list object for Diagnostics.

Description

Usage

Arguments

Details

Value

See Also

Examples

Bilinear (inner-product) latent space, with optional clustering

Description

Usage

Arguments

Details

See Also

Keywords

Auxiliary for Controlling ERGMM Fitting

Description

Usage

Arguments

Value

See Also

Examples

Southern Women Data Set (Davis) as a bipartite “network” object

Description

Details

Source

References

See Also

Examples

Fit a Latent Space Random Graph Model

Description

Usage

Arguments

Value

Specifying fixed effects

References

See Also

Examples

Class of Fitted Exponential Random Graph Mixed Models

Description

Details

See Also

Edge Weight Distribution Families

Description

Details

Family-link combinations

Draw a pie chart at a specified location.

Description

Usage

Arguments

Author(s)

See Also

Examples

A List of ERGMM Parameter Configuration

Description

Usage

Arguments

Details

See Also

Auxiliary for Setting the ERGMM Prior

Description

Usage

Arguments

Value

See Also

Euclidean distance latent space, with optional clustering

Description

Usage

Arguments

Details

See Also

Keywords