Package 'Bergm' reference manual

Title:	Bayesian Exponential Random Graph Models
Description:	Bayesian analysis for exponential random graph models using advanced computational algorithms. More information can be found at: <https://acaimo.github.io/Bergm/>.
Authors:	Alberto Caimo [aut, cre], Lampros Bouranis [aut], Robert Krause [aut] Nial Friel [ctb]
Maintainer:	Alberto Caimo <[email protected]>
License:	GPL (>= 2)
Version:	5.0.7
Built:	2024-09-13 09:54:21 UTC
Source:	https://github.com/acaimo/bergm

Bayesian exponential random graph models

Description

Bergm provides a range of tools to analyse Bayesian exponential random graph models using advanced computational methods.

Parameter estimation for Bayesian ERGMs

Description

Function to fit Bayesian exponential random graphs models using the approximate exchange algorithm.

Usage

bergm(
  formula,
  prior.mean = NULL,
  prior.sigma = NULL,
  burn.in = 100,
  main.iters = 1000,
  aux.iters = 1000,
  nchains = NULL,
  gamma = 0.5,
  V.proposal = 0.0025,
  startVals = NULL,
  offset.coef = NULL,
  constraints = NULL,
  thin = 1,
  cut.reject = FALSE,
  saveEveryX = NULL,
  saveEveryXName = 'partialBergmEstimate.rda',
  ...
)
bergm(
  formula,
  prior.mean = NULL,
  prior.sigma = NULL,
  burn.in = 100,
  main.iters = 1000,
  aux.iters = 1000,
  nchains = NULL,
  gamma = 0.5,
  V.proposal = 0.0025,
  startVals = NULL,
  offset.coef = NULL,
  constraints = NULL,
  thin = 1,
  cut.reject = FALSE,
  saveEveryX = NULL,
  saveEveryXName = 'partialBergmEstimate.rda',
  ...
)

Arguments

`formula`	formula; an `ergm` formula object, of the form <network> ~ <model terms> where <network> is a `network` object and <model terms> are `ergm-terms`.
`prior.mean`	vector; mean vector of the multivariate Normal prior. By default set to a vector of 0's.
`prior.sigma`	square matrix; variance/covariance matrix for the multivariate Normal prior. By default set to a diagonal matrix with every diagonal entry equal to 100.
`burn.in`	count; number of burn-in iterations for every chain of the population.
`main.iters`	count; number of iterations for every chain of the population.
`aux.iters`	count; number of auxiliary iterations used for network simulation.
`nchains`	count; number of chains of the population MCMC. By default set to twice the model dimension (number of model terms).
`gamma`	scalar; parallel adaptive direction sampling move factor.
`V.proposal`	count; diagonal entry for the multivariate Normal proposal. By default set to 0.0025.
`startVals`	vector; optional starting values for the parameter estimation.
`offset.coef`	vector; A vector of coefficients for the offset terms.
`constraints`	formula; A formula specifying one or more constraints on the support of the distribution of the networks being modeled, using syntax similar to the formula argument, on the right-hand side. Multiple constraints may be given, separated by “+” and “-” operators. (See `ergm` constraints for the explanation of their semantics.) Together with the model terms in the formula and the reference measure, the constraints define the distribution of networks being modeled.
`thin`	count; The thinning interval between consecutive observations.
`cut.reject`	logical; The thinning interval between consecutive observations.
`saveEveryX`	count; If not NULL, the posterior and obtained imputation (only if nImp > 0) will be saved to your working directory at every X iterations. Note that this slows down estimation and thus X should not be set too low. By default, the saved data will be in 'partialBergmEstimate.rda' and will be overwritten every X iterations and by other calls of bergm() or bergmM().
`saveEveryXName`	character; The Name of the partial estimation object. If you run multiple bergm() or bergmM() calls in the same working directory you should change this name so that the calls do not override each other.
`saveEveryXName`	logical; By default FALSE. If TRUE, missing network and attribute data are imputed after every iteration. This leads to significantly longer estimation times, but potentially overall better estimation. It is recommended to initially estimate with imputeAllItr = FALSE to get reasonable starting values and reduce overall estimation time.
`...`	additional arguments, to be passed to lower-level functions.

References

Caimo, A. and Friel, N. (2011), "Bayesian Inference for Exponential Random Graph Models," Social Networks, 33(1), 41-55. https://arxiv.org/abs/1007.5192

Caimo, A. and Friel, N. (2014), "Bergm: Bayesian Exponential Random Graphs in R," Journal of Statistical Software, 61(2), 1-25. https://www.jstatsoft.org/article/view/v061i02

Examples

## Not run: 
# Load the florentine marriage network
data(florentine)

# Posterior parameter estimation:
p.flo <- bergm(flomarriage ~ edges + kstar(2),
               burn.in    = 50,
               aux.iters  = 500,
               main.iters = 3000,
               gamma      = 1.2)

# Posterior summaries:
summary(p.flo)

## End(Not run)
## Not run: 
# Load the florentine marriage network
data(florentine)

# Posterior parameter estimation:
p.flo <- bergm(flomarriage ~ edges + kstar(2),
               burn.in    = 50,
               aux.iters  = 500,
               main.iters = 3000,
               gamma      = 1.2)

# Posterior summaries:
summary(p.flo)

## End(Not run)

Calibrating misspecified Bayesian ERGMs

Description

Function to transform a sample from the pseudo-posterior to one that is approximately sampled from the intractable posterior distribution.

Usage

bergmC(
  formula,
  prior.mean = NULL,
  prior.sigma = NULL,
  burn.in = 10000,
  main.iters = 40000,
  aux.iters = 3000,
  V.proposal = 1.5,
  thin = 1,
  rm.iters = 500,
  rm.a = 0.001,
  rm.alpha = 0,
  n.aux.draws = 400,
  aux.thin = 50,
  estimate = c("MLE", "CD"),
  seed = 1,
  ...
)
bergmC(
  formula,
  prior.mean = NULL,
  prior.sigma = NULL,
  burn.in = 10000,
  main.iters = 40000,
  aux.iters = 3000,
  V.proposal = 1.5,
  thin = 1,
  rm.iters = 500,
  rm.a = 0.001,
  rm.alpha = 0,
  n.aux.draws = 400,
  aux.thin = 50,
  estimate = c("MLE", "CD"),
  seed = 1,
  ...
)

Arguments

`formula`	formula; an `ergm` formula object, of the form <network> ~ <model terms> where <network> is a `network` object and <model terms> are `ergm-terms`.
`prior.mean`	vector; mean vector of the multivariate Normal prior. By default set to a vector of 0's.
`prior.sigma`	square matrix; variance/covariance matrix for the multivariate Normal prior. By default set to a diagonal matrix with every diagonal entry equal to 100.
`burn.in`	count; number of burn-in iterations at the beginning of an MCMC run for the pseudo-posterior estimation.
`main.iters`	count; number of MCMC iterations after burn-in for the pseudo-posterior estimation.
`aux.iters`	count; number of auxiliary iterations used for drawing the first network from the ERGM likelihood (Robbins-Monro). See `control.simulate.formula`.
`V.proposal`	count; diagonal entry for the multivariate Normal proposal. By default set to 1.5.
`thin`	count; thinning interval used in the simulation for the pseudo-posterior estimation. The number of MCMC iterations must be divisible by this value.
`rm.iters`	count; number of iterations for the Robbins-Monro stochastic approximation algorithm.
`rm.a`	scalar; constant for sequence alpha_n (Robbins-Monro).
`rm.alpha`	scalar; noise added to gradient (Robbins-Monro).
`n.aux.draws`	count; number of auxiliary networks drawn from the ERGM likelihood (Robbins-Monro). See `control.simulate.formula`.
`aux.thin`	count; number of auxiliary iterations between network draws after the first network is drawn (Robbins-Monro). See `control.simulate.formula`.
`estimate`	If "MLE" (the default), then an approximate maximum likelihood estimator is used as a starting point in the Robbins-Monro algorithm. If "CD" , the Monte-Carlo contrastive divergence estimate is returned. See `ergm`.
`seed`	integer; seed for the random number generator. See `set.seed`.
`...`	Additional arguments, to be passed to the ergm function. See `ergm`.

References

Bouranis, L., Friel, N., & Maire, F. (2017). Efficient Bayesian inference for exponential random graph models by correcting the pseudo-posterior distribution. Social Networks, 50, 98-108. https://arxiv.org/abs/1510.00934

Examples

## Not run: 
# Load the florentine marriage network
data(florentine)
                                 
# Calibrated pseudo-posterior:
cpp.flo <- bergmC(flomarriage ~ edges + kstar(2),
                  aux.iters  = 500,
                  burn.in    = 500,
                  main.iters = 10000,
                  V.proposal = 2.5)

# Posterior summaries:
summary(cpp.flo)

## End(Not run)

## Not run: 
# Load the florentine marriage network
data(florentine)
                                 
# Calibrated pseudo-posterior:
cpp.flo <- bergmC(flomarriage ~ edges + kstar(2),
                  aux.iters  = 500,
                  burn.in    = 500,
                  main.iters = 10000,
                  V.proposal = 2.5)

# Posterior summaries:
summary(cpp.flo)

## End(Not run)

Parameter estimation for Bayesian ERGMs under missing data

Description

Function to fit Bayesian exponential random graphs models under missing data using the approximate exchange algorithm. The function can also handle missing data in the attributes. Do note that this imputation is not following Koskinen et al. 2013 and resulting parameters are no longer following an ERGM distribution but are from a mixture distribution. The attribute imputation algorithm is thus not guranteed to deliver proper ERGM results and should be used with caution.

Usage

bergmM(
  formula,
  burn.in = 100,
  main.iters = 1000,
  aux.iters = 1000,
  prior.mean = NULL,
  prior.sigma = NULL,
  nchains = NULL,
  gamma = 0.5,
  V.proposal = 0.0025,
  seed = NULL,
  startVals = NULL,
  offset.coef = NULL,
  constraints = NULL,
  thin = 1,
  saveEveryX = NULL,
  saveEveryXName = 'partialBergmEstimate.rda',
  imputeAllItr = FALSE,
  imputeLast = TRUE,
  nImp = NULL,
  missingUpdate = NULL,
  imputeData = NULL,
  attributeNames = NULL,
  miceIt = 5,
  onlyKeepImputation = FALSE,
  ...
)
bergmM(
  formula,
  burn.in = 100,
  main.iters = 1000,
  aux.iters = 1000,
  prior.mean = NULL,
  prior.sigma = NULL,
  nchains = NULL,
  gamma = 0.5,
  V.proposal = 0.0025,
  seed = NULL,
  startVals = NULL,
  offset.coef = NULL,
  constraints = NULL,
  thin = 1,
  saveEveryX = NULL,
  saveEveryXName = 'partialBergmEstimate.rda',
  imputeAllItr = FALSE,
  imputeLast = TRUE,
  nImp = NULL,
  missingUpdate = NULL,
  imputeData = NULL,
  attributeNames = NULL,
  miceIt = 5,
  onlyKeepImputation = FALSE,
  ...
)

Arguments

`formula`	formula; an `ergm` formula object, of the form <network> ~ <model terms> where <network> is a `network` object and <model terms> are `ergm-terms`.
`burn.in`	count; number of burn-in iterations for every chain of the population.
`main.iters`	count; number of iterations for every chain of the population.
`aux.iters`	count; number of auxiliary iterations used for network simulation.
`prior.mean`	vector; mean vector of the multivariate Normal prior. By default set to a vector of 0's. Note that several ergm.terms add more than one parameter to the model. You need to adjust your priors accordingly
`prior.sigma`	square matrix; variance/covariance matrix for the multivariate Normal prior. By default set to a diagonal matrix with every diagonal entry equal to 100. Note that several ergm.terms add more than one parameter to the model. You need to adjust your priors accordingly
`nchains`	count; number of chains of the population MCMC. By default set to twice the model dimension (number of model terms).
`gamma`	scalar; parallel adaptive direction sampling move factor.
`V.proposal`	count; diagonal entry for the multivariate Normal proposal. By default set to 0.0025.
`seed`	count; Random number seed for the Bergm estimation.
`startVals`	vector; Optional starting values for the parameter estimation. Note that several ergm.terms add more than one parameter to the model. You need to adjust your priors accordingly
`offset.coef`	vector; A vector of coefficients for the offset terms.
`constraints`	formula; A formula specifying one or more constraints on the support of the distribution of the networks being modeled, using syntax similar to the formula argument, on the right-hand side. Multiple constraints may be given, separated by “+” and “-” operators. (See `ergm` constraints for the explanation of their semantics.) Together with the model terms in the formula and the reference measure, the constraints define the distribution of networks being modeled.
`thin`	count; The thinning interval between consecutive observations.
`cut.reject`	logical; The thinning interval between consecutive observations.
`saveEveryX`	count; If not NULL, the posterior and obtained imputation (only if nImp > 0) will be saved to your working directory at every X iterations. Note that this slows down estimation and thus X should not be set too low. By default, the saved data will be in 'partialBergmEstimate.rda' and will be overwritten every X iterations and by other calls of bergm() or bergmM().
`saveEveryXName`	character; The Name of the partial estimation object. If you run multiple bergm() or bergmM() calls in the same working directory you should change this name so that the calls do not override each other.
`saveEveryXName`	logical; By default FALSE. If TRUE, missing network and attribute data are imputed after every iteration. This leads to significantly longer estimation times, but potentially overall better estimation. It is recommended to initially estimate with imputeAllItr = FALSE to get reasonable starting values and reduce overall estimation time.
`imputeLast`	logical; By default TRUE and in line with Koskinen et al. 2010. If FALSE, network imputations will be performed with a random draw from the so far accepted parameter values.
`nImp`	count; default TRUE. By default bergm()/bergmM() will save the last accepted theta to the posterior if the new proposal is rejected. This artificially will increase the auto-correlation of consecutive parameters.
`missingUpdate`	count; Number of tie updates in each imputation step. By default equal to number of missing ties. Smaller numbers increase speed. Larger numbers lead to better sampling.
`imputeData`	data.frame; A data.frame with all attribute variables that should be imputed and additional attributes that should be used for the imputation. All non-numeric variables need to be specified as.factors(). Names of vertex.attributes and corressponding variable names in the imputeData must be identical.
`attributeNames`	character vector; A vector with the names of all variables that need to be imputed. These names need to be identical with the vertex.attributes and must be part of the names of the `imputeData` data.frame.
`miceIt`	count; By default 5. Number of iterations in the MICE imputation. Larger numbers will lead to better attribute imputation but increase estimation time.
`onlyKeepImputation`	logical; By defualt FALSE. Should only imputations be returned, and no bergmM() estimate (only recommended after you made sure that the model estimates properly). This can save memory.
`...`	additional arguments, to be passed to lower-level functions.

References

Caimo, A. and Friel, N. (2011), "Bayesian Inference for Exponential Random Graph Models," Social Networks, 33(1), 41-55. https://arxiv.org/abs/1007.5192

Caimo, A. and Friel, N. (2014), "Bergm: Bayesian Exponential Random Graphs in R," Journal of Statistical Software, 61(2), 1-25. https://www.jstatsoft.org/v61/i02

Koskinen, J.H., Robins, G.L., Pattison, P.E. (2010), "Analysing exponential random graph (p-star) models with missing data using Bayesian data augmentation," Statistical Methodology 7(3), 366-384.

Koskinen, J. H., Robins, G. L., Wang, P., & Pattison, P. E. (2013). "Bayesian analysis for partially observed network data, missing ties, attributes and actors." Social networks, 35(4), 514-527.

Krause, R.W., Huisman, M., Steglich, C., Snijders, T.A. (2020), "Missing data in cross-sectional networks-An extensive comparison of missing data treatment methods", Social Networks 62: 99-112.

Examples

## Not run: 
# Load the florentine marriage network
data(florentine)

# Create missing data
set.seed(14021994)
n <- dim(flomarriage[, ])[1]
missNode <- sample(1:n, 1)
flomarriage[missNode, ] <- NA
flomarriage[, missNode] <- NA

# Posterior parameter estimation:
m.flo <- bergmM(flomarriage ~ edges + kstar(2),
                burn.in    = 50,
                aux.iters  = 500,
                main.iters = 1000,
                gamma      = 1.2,
                nImp       = 5)

# Posterior summaries:
summary(m.flo)

## End(Not run)
## Not run: 
# Load the florentine marriage network
data(florentine)

# Create missing data
set.seed(14021994)
n <- dim(flomarriage[, ])[1]
missNode <- sample(1:n, 1)
flomarriage[missNode, ] <- NA
flomarriage[, missNode] <- NA

# Posterior parameter estimation:
m.flo <- bergmM(flomarriage ~ edges + kstar(2),
                burn.in    = 50,
                aux.iters  = 500,
                main.iters = 1000,
                gamma      = 1.2,
                nImp       = 5)

# Posterior summaries:
summary(m.flo)

## End(Not run)

Bayesian goodness-of-fit diagnostics for ERGMs

Description

Function to calculate summaries for degree, minimum geodesic distances, and edge-wise shared partner distributions to diagnose the Bayesian goodness-of-fit of exponential random graph models.

Usage

bgof(
  x,
  sample.size = 100,
  aux.iters = 10000,
  n.deg = NULL,
  n.dist = NULL,
  n.esp = NULL,
  n.ideg = NULL,
  n.odeg = NULL,
  ...
)
bgof(
  x,
  sample.size = 100,
  aux.iters = 10000,
  n.deg = NULL,
  n.dist = NULL,
  n.esp = NULL,
  n.ideg = NULL,
  n.odeg = NULL,
  ...
)

Arguments

`x`	an `R` object of class `bergm`.
`sample.size`	count; number of networks to be simulated and compared to the observed network.
`aux.iters`	count; number of iterations used for network simulation.
`n.deg`	count; used to plot only the first `n.deg`-1 degree distributions. By default no restrictions on the number of degree distributions is applied.
`n.dist`	count; used to plot only the first `n.dist`-1 geodesic distances distributions. By default no restrictions on the number of geodesic distances distributions is applied.
`n.esp`	count; used to plot only the first `n.esp`-1 edge-wise shared partner distributions. By default no restrictions on the number of edge-wise shared partner distributions is applied.
`n.ideg`	count; used to plot only the first `n.ideg`-1 in-degree distributions. By default no restrictions on the number of in-degree distributions is applied.
`n.odeg`	count; used to plot only the first `n.odeg`-1 out-degree distributions. By default no restrictions on the number of out-degree distributions is applied.
`...`	additional arguments, to be passed to lower-level functions.

References

Caimo, A. and Friel, N. (2011), "Bayesian Inference for Exponential Random Graph Models," Social Networks, 33(1), 41-55. https://arxiv.org/abs/1007.5192

Caimo, A. and Friel, N. (2014), "Bergm: Bayesian Exponential Random Graphs in R," Journal of Statistical Software, 61(2), 1-25. https://www.jstatsoft.org/v61/i02

Examples

## Not run: 
# Load the florentine marriage network
data(florentine)

# Posterior parameter estimation:
p.flo <- bergm(flomarriage ~ edges + kstar(2),
               burn.in    = 50,
               aux.iters  = 500,
               main.iters = 1000,
               gamma      = 1.2)

# Bayesian goodness-of-fit test:
bgof(p.flo,
     aux.iters   = 500,
     sample.size = 30,
     n.deg       = 10,
     n.dist      = 9,
     n.esp       = 6)

## End(Not run)
## Not run: 
# Load the florentine marriage network
data(florentine)

# Posterior parameter estimation:
p.flo <- bergm(flomarriage ~ edges + kstar(2),
               burn.in    = 50,
               aux.iters  = 500,
               main.iters = 1000,
               gamma      = 1.2)

# Bayesian goodness-of-fit test:
bgof(p.flo,
     aux.iters   = 500,
     sample.size = 30,
     n.deg       = 10,
     n.dist      = 9,
     n.esp       = 6)

## End(Not run)

Adjustment of ERGM pseudolikelihood

Description

Function to estimate the transformation parameters for adjusting the pseudolikelihood function.

Usage

ergmAPL(
  formula,
  aux.iters = NULL,
  n.aux.draws = NULL,
  aux.thin = NULL,
  ladder = NULL,
  estimate = c("MLE", "CD"),
  seed = 1,
  ...
)
ergmAPL(
  formula,
  aux.iters = NULL,
  n.aux.draws = NULL,
  aux.thin = NULL,
  ladder = NULL,
  estimate = c("MLE", "CD"),
  seed = 1,
  ...
)

Arguments

`formula`	formula; an `ergm` formula object, of the form <network> ~ <model terms> where <network> is a `network` object and <model terms> are `ergm-terms`.
`aux.iters`	count; number of auxiliary iterations used for drawing the first network from the ERGM likelihood. See `control.simulate.formula`.
`n.aux.draws`	count; Number of auxiliary networks drawn from the ERGM likelihood. See `control.simulate.formula`.
`aux.thin`	count; Number of auxiliary iterations between network draws after the first network is drawn. See `control.simulate.formula`.
`ladder`	count; Length of temperature ladder (>=3).
`estimate`	If "MLE" (the default), then an approximate maximum likelihood estimator is returned. If "CD" , the Monte-Carlo contrastive divergence estimate is returned. See `ergm`.
`seed`	integer; seed for the random number generator. See `set.seed`.
`...`	Additional arguments, to be passed to the ergm function. See `ergm`.

References

Bouranis, L., Friel, N., & Maire, F. (2018). Bayesian model selection for exponential random graph models via adjusted pseudolikelihoods. Journal of Computational and Graphical Statistics, 27(3), 516-528. https://arxiv.org/abs/1706.06344

Wrapper function for evidence estimation

Description

Function to estimate the evidence (marginal likelihood) with Chib and Jeliazkov's method or Power posteriors, based on the adjusted pseudolikelihood function.

Usage

evidence(evidence.method = c("CJ", "PP"), ...)
evidence(evidence.method = c("CJ", "PP"), ...)

Arguments

`evidence.method`	vector Method to estimate the marginal likelihood. Options are: `"CJ"`, in which case the marginal likelihood is estimated with Chib and Jeliazkov's method; `"PP"`, in which case the marginal likelihood is estimated with Power posteriors.
`...`	further arguments to be passed. See `evidenceCJ` and `evidencePP`.

References

Examples

## Not run: 
# Load the florentine marriage network:
data(florentine)
                                                
# MCMC sampling and evidence estimation:
CJE <- evidence(evidence.method = "CJ",
                formula     = flomarriage ~ edges + kstar(2),
                main.iters  = 30000,
                burn.in     = 2000,
                aux.iters   = 1000,
                num.samples = 25000,
                V.proposal  = 2.5,
                ladder      = 100,
                seed        = 1)
                                   
# Posterior summaries:
summary(CJE)

# MCMC diagnostics plots:
plot(CJE)
    
# Log-evidence (marginal likelihood) estimate:
CJE$log.evidence

## End(Not run)

## Not run: 
# Load the florentine marriage network:
data(florentine)
                                                
# MCMC sampling and evidence estimation:
CJE <- evidence(evidence.method = "CJ",
                formula     = flomarriage ~ edges + kstar(2),
                main.iters  = 30000,
                burn.in     = 2000,
                aux.iters   = 1000,
                num.samples = 25000,
                V.proposal  = 2.5,
                ladder      = 100,
                seed        = 1)
                                   
# Posterior summaries:
summary(CJE)

# MCMC diagnostics plots:
plot(CJE)
    
# Log-evidence (marginal likelihood) estimate:
CJE$log.evidence

## End(Not run)

Evidence estimation via Chib and Jeliazkov's method

Description

Function to estimate the evidence (marginal likelihood) with Chib and Jeliazkov's method, based on the adjusted pseudolikelihood function.

Usage

evidenceCJ(
  formula,
  prior.mean = NULL,
  prior.sigma = NULL,
  aux.iters = 1000,
  n.aux.draws = 5,
  aux.thin = 50,
  ladder = 30,
  main.iters = 30000,
  burn.in = 5000,
  thin = 1,
  V.proposal = 1.5,
  num.samples = 25000,
  seed = 1,
  estimate = c("MLE", "CD"),
  ...
)
evidenceCJ(
  formula,
  prior.mean = NULL,
  prior.sigma = NULL,
  aux.iters = 1000,
  n.aux.draws = 5,
  aux.thin = 50,
  ladder = 30,
  main.iters = 30000,
  burn.in = 5000,
  thin = 1,
  V.proposal = 1.5,
  num.samples = 25000,
  seed = 1,
  estimate = c("MLE", "CD"),
  ...
)

Arguments

`formula`	formula; an `ergm` formula object, of the form <network> ~ <model terms> where <network> is a `network` object and <model terms> are `ergm-terms`.
`prior.mean`	vector; mean vector of the multivariate Normal prior. By default set to a vector of 0's.
`prior.sigma`	square matrix; variance/covariance matrix for the multivariate Normal prior. By default set to a diagonal matrix with every diagonal entry equal to 100.
`aux.iters`	count; number of auxiliary iterations used for drawing the first network from the ERGM likelihood. See `control.simulate.formula` and `ergmAPL`.
`n.aux.draws`	count; number of auxiliary networks drawn from the ERGM likelihood. See `control.simulate.formula` and `ergmAPL`.
`aux.thin`	count; number of auxiliary iterations between network draws after the first network is drawn. See `control.simulate.formula` and `ergmAPL`.
`ladder`	count; length of temperature ladder (>=3). See `ergmAPL`.
`main.iters`	count; number of MCMC iterations after burn-in for the adjusted pseudo-posterior estimation.
`burn.in`	count; number of burn-in iterations at the beginning of an MCMC run for the adjusted pseudo-posterior estimation.
`thin`	count; thinning interval used in the simulation for the adjusted pseudo-posterior estimation. The number of MCMC iterations must be divisible by this value.
`V.proposal`	count; diagonal entry for the multivariate Normal proposal. By default set to 1.5.
`num.samples`	integer; number of samples used in the marginal likelihood estimate. Must be lower than `main.iters` - `burnin`.
`seed`	integer; seed for the random number generator. See `set.seed` and `MCMCmetrop1R`.
`estimate`	If "MLE" (the default), then an approximate maximum likelihood estimator is returned. If "CD" , the Monte-Carlo contrastive divergence estimate is returned. See `ergm`.
`...`	additional arguments, to be passed to the ergm function. See `ergm` and `ergmAPL`.

References

Caimo, A., & Friel, N. (2013). Bayesian model selection for exponential random graph models. Social Networks, 35(1), 11-24. https://arxiv.org/abs/1201.2337

Examples

## Not run: 
# Load the florentine marriage network:
data(florentine)
                                                
# MCMC sampling and evidence estimation:
CJE <- evidenceCJ(flomarriage ~ edges + kstar(2),
                  main.iters  = 2000,
                  burn.in     = 200,
                  aux.iters   = 500,
                  num.samples = 25000,
                  V.proposal  = 2.5)
                                   
# Posterior summaries:
summary(CJE)

# MCMC diagnostics plots:
plot(CJE)
    
# Log-evidence (marginal likelihood) estimate:
CJE$log.evidence

## End(Not run)

## Not run: 
# Load the florentine marriage network:
data(florentine)
                                                
# MCMC sampling and evidence estimation:
CJE <- evidenceCJ(flomarriage ~ edges + kstar(2),
                  main.iters  = 2000,
                  burn.in     = 200,
                  aux.iters   = 500,
                  num.samples = 25000,
                  V.proposal  = 2.5)
                                   
# Posterior summaries:
summary(CJE)

# MCMC diagnostics plots:
plot(CJE)
    
# Log-evidence (marginal likelihood) estimate:
CJE$log.evidence

## End(Not run)

Evidence estimation via power posteriors

Description

Function to estimate the evidence (marginal likelihood) with Power posteriors, based on the adjusted pseudolikelihood function.

Usage

evidencePP(
  formula,
  prior.mean = NULL,
  prior.sigma = NULL,
  aux.iters = 1000,
  n.aux.draws = 50,
  aux.thin = 50,
  ladder = 30,
  main.iters = 20000,
  burn.in = 5000,
  thin = 1,
  V.proposal = 1.5,
  seed = 1,
  temps = NULL,
  estimate = c("MLE", "CD"),
  ...
)
evidencePP(
  formula,
  prior.mean = NULL,
  prior.sigma = NULL,
  aux.iters = 1000,
  n.aux.draws = 50,
  aux.thin = 50,
  ladder = 30,
  main.iters = 20000,
  burn.in = 5000,
  thin = 1,
  V.proposal = 1.5,
  seed = 1,
  temps = NULL,
  estimate = c("MLE", "CD"),
  ...
)

Arguments

`formula`	formula; an `ergm` formula object, of the form <network> ~ <model terms> where <network> is a `network` object and <model terms> are `ergm-terms`.
`prior.mean`	vector; mean vector of the multivariate Normal prior. By default set to a vector of 0's.
`prior.sigma`	square matrix; variance/covariance matrix for the multivariate Normal prior. By default set to a diagonal matrix with every diagonal entry equal to 100.
`aux.iters`	count; number of auxiliary iterations used for drawing the first network from the ERGM likelihood. See `control.simulate.formula` and `ergmAPL`.
`n.aux.draws`	count; number of auxiliary networks drawn from the ERGM likelihood. See `control.simulate.formula` and `ergmAPL`.
`aux.thin`	count; number of auxiliary iterations between network draws after the first network is drawn. See `control.simulate.formula` and `ergmAPL`.
`ladder`	count; length of temperature ladder (>=3). See `ergmAPL`.
`main.iters`	count; number of MCMC iterations after burn-in for the adjusted pseudo-posterior estimation.
`burn.in`	count; number of burn-in iterations at the beginning of an MCMC run for the adjusted pseudo-posterior estimation.
`thin`	count; thinning interval used in the simulation for the adjusted pseudo-posterior estimation. The number of MCMC iterations must be divisible by this value.
`V.proposal`	count; diagonal entry for the multivariate Normal proposal. By default set to 1.5.
`seed`	integer; seed for the random number generator. See `set.seed` and `MCMCmetrop1R`.
`temps`	numeric vector; inverse temperature ladder, $t \in [0,1]$ .
`estimate`	If "MLE" (the default), then an approximate maximum likelihood estimator is returned. If "CD" , the Monte-Carlo contrastive divergence estimate is returned. See `ergm`.
`...`	additional arguments, to be passed to the ergm function. See `ergm` and `ergmAPL`.

References

Examples

## Not run: 
# Load the florentine marriage network:
data(florentine)

PPE <- evidencePP(flomarriage ~ edges + kstar(2),
                  aux.iters   = 500, 
                  aux.thin    = 50,   
                  main.iters  = 2000,
                  burn.in     = 100,
                  V.proposal  = 2.5)
                                   
# Posterior summaries:
summary(PPE)

# MCMC diagnostics plots:
plot(PPE)
  
# Log-evidence (marginal likelihood) estimate:             
PPE$log.evidence

## End(Not run)

## Not run: 
# Load the florentine marriage network:
data(florentine)

PPE <- evidencePP(flomarriage ~ edges + kstar(2),
                  aux.iters   = 500, 
                  aux.thin    = 50,   
                  main.iters  = 2000,
                  burn.in     = 100,
                  V.proposal  = 2.5)
                                   
# Posterior summaries:
summary(PPE)

# MCMC diagnostics plots:
plot(PPE)
  
# Log-evidence (marginal likelihood) estimate:             
PPE$log.evidence

## End(Not run)

Lazega lawyers network data

Description

Lazega lawyers network data

Usage

lazega
lazega

Format

An oject of class network.

Source

This network dataset comes from a network study of corporate law partnership that was carried out in a Northeastern US corporate law firm in New England from 1988 to 1991. It represents collaborative relations among the 36 attorneys (partners and associates) of this firm. Nodal attributes include: Age, Gender, Office, Practice, School, and Years.

References

Lazega, E. (2001), "The Collegial Phenomenon: The Social Mechanisms of Cooperation Among Peers in a Corporate Law Partnership," Oxford University Press.

Examples

## Not run: 
 par(mfrow = c(1, 2), oma = rep(0, 4))
 CC <- hcl.colors(3, "Teal")
 set.seed(22)
 plot(lazega,
      vertex.col = CC[lazega %v% "Office"], 
     vertex.cex = 2)
 legend("topright",
        pch    = 21,
        pt.bg  = CC,
        legend = c("Boston", "Hartford", "Providence"),
        title  = "OFFICE")

## End(Not run)
## Not run: 
 par(mfrow = c(1, 2), oma = rep(0, 4))
 CC <- hcl.colors(3, "Teal")
 set.seed(22)
 plot(lazega,
      vertex.col = CC[lazega %v% "Office"], 
     vertex.cex = 2)
 legend("topright",
        pch    = 21,
        pt.bg  = CC,
        legend = c("Boston", "Hartford", "Providence"),
        title  = "OFFICE")

## End(Not run)

Plot BERGM posterior output

Description

This function creates MCMC diagnostic plots for bergm objects.

Usage

## S3 method for class 'bergm'
plot(x, ...)
## S3 method for class 'bergm'
plot(x, ...)

Arguments

`x`	an `R` object of class `bergm`.
`...`	additional arguments, to be passed to lower-level functions.

Examples

## Not run: 
# Load the florentine marriage network
data(florentine)

# Posterior parameter estimation:
p.flo <- bergm(flomarriage ~ edges + kstar(2),
               burn.in    = 50,
               aux.iters  = 500,
               main.iters = 1000,
               gamma      = 1.2)

# MCMC diagnostics plots:
plot(p.flo)

## End(Not run)

## Not run: 
# Load the florentine marriage network
data(florentine)

# Posterior parameter estimation:
p.flo <- bergm(flomarriage ~ edges + kstar(2),
               burn.in    = 50,
               aux.iters  = 500,
               main.iters = 1000,
               gamma      = 1.2)

# MCMC diagnostics plots:
plot(p.flo)

## End(Not run)

Summary of BERGM posterior output

Description

This function summarises MCMC output for bergm objects.

Usage

## S3 method for class 'bergm'
summary(object, ...)
## S3 method for class 'bergm'
summary(object, ...)

Arguments

`object`	an `R` object of class `bergm`.
`returnTable`	logical; By default FALSE. Should the results table be returned.
`...`	additional arguments, to be passed to lower-level functions.

Package 'Bergm'

Help Index

Bayesian exponential random graph models

Description

Parameter estimation for Bayesian ERGMs

Description

Usage

Arguments

References

Examples

Calibrating misspecified Bayesian ERGMs

Description

Usage

Arguments

References

Examples

Parameter estimation for Bayesian ERGMs under missing data

Description

Usage

Arguments

References

Examples

Bayesian goodness-of-fit diagnostics for ERGMs

Description

Usage

Arguments

References

Examples

Adjustment of ERGM pseudolikelihood

Description

Usage

Arguments

References

Wrapper function for evidence estimation

Description

Usage

Arguments

References

Examples

Evidence estimation via Chib and Jeliazkov's method

Description

Usage

Arguments

References

Examples

Evidence estimation via power posteriors

Description

Usage

Arguments

References

Examples

Lazega lawyers network data

Description

Usage

Format

Source

References

Examples

Plot BERGM posterior output

Description

Usage

Arguments

Examples

Summary of BERGM posterior output

Description

Usage

Arguments