Conduct Goodness-of-Fit Diagnostics for a Signed ERGM

Description

Computes the goodness-of-fit (GoF) for a fitted signed exponential random graph model (SERGM). The function simulates new networks using the fitted model and compares key network statistics from the observed network with those from the simulated ones.

Usage

GoF(model, nsim = 200, seed = NULL)

Arguments

Details

The following diagnostics are plotted:

• Positive degree distribution

• Negative degree distribution

• Edgewise shared enemies distribution (positive edges)

• Edgewise shared enemies distribution (negative edges)

• Edgewise shared friends distribution (positive edges)

• Edgewise shared friends distribution (negative edges)

Value

Produces six diagnostic boxplots comparing observed and simulated statistics for the fitted model.

See Also

ergm, mple_sign

Examples

data("tribes")
fit <- mple_sign(tribes ~ Pos(~edges) + Neg(~edges))
GoF(fit, nsim = 100)

Evaluation of negative edges

Description

Evaluates the terms in 'formula' of the negative edges and sums the results elementwise.

Usage

# binary: Neg(formula)

Arguments

Evaluation of positive edges

Description

Evaluates the terms in 'formula' of the positive edges and sums the results elementwise.

Usage

# binary: Pos(formula)

Arguments

Delayed edgewise shared enemies

Description

This term adds one network statistic to the model that counts the number of positive or negative edges in the current network whose endpoints had exactly 'd' shared enemies (common negative ties) in the previous network.

For directed networks, different definitions of shared enemies can be specified using the 'type' argument. For undirected networks, only one configuration applies.

Usage

# binary: delese(d = 1, base = "+", type = "OTP")

Arguments

Details

For each edge in the current network (positive or negative, depending on 'base'), this term checks how many nodes were connected negatively to both endpoints in the previous network.

Delayed edgewise shared friends

Description

This term adds one network statistic to the model that counts the number of positive or negative edges in the current network whose endpoints had exactly 'd' shared friends (common positive ties) in the previous network.

For directed networks, different definitions of shared friends can be specified using the 'type' argument. For undirected networks, only one configuration applies.

Usage

# binary: delese(d = 1, base = "+", type = "OTP")

Arguments

Details

For each edge in the current network (positive or negative, depending on 'base'), this term checks how many nodes were connected positively to both endpoints in the previous network.

Delayed node matching on attribute (lag-1)

Description

Create a nodematch term where node attributes come from the previous network's node attribute 'attr'. The previous network used is the one indexed by the current 'GroupID' (equivalent to 'lag=1' previously). This constructs a temporary node attribute 'delnodecov_<attr>' on the current network (copied from the previous net) and calls 'nodematch' on that attribute.

Usage

# binary: delnodematch(attr)

Arguments

Delayed reciprocity

Description

For the current network layer 'base', this term equals 1 for each directed edge i->j currently present where the reverse edge j->i was present in the previous network's same layer. The previous network used is the one indexed by the current 'GroupID' (i.e., the behaviour is the same as the prior 'lag=1' implementation). The term is provided as an edgecov (1/0).

Usage

# binary: delrecip(base)

Arguments

Geometrically weighted delayed edgewise shared enemies

Description

This term calculates the number of shared enemies based on the previous network. It then applies a geometric transformation to these counts to reduce the influence of large counts. Specifically, if the decay parameter decay is provided, the weighting function used is

f(k; decay) = 1 - (1 - e^{-decay})^k,

where k is the count of shared partners and decay controls how quickly the weight decreases as counts increase.

Usage

# binary: gwdelese(decay, base, type = "OTP")

Arguments

Geometrically weighted delayed edgewise shared friends

Description

This term calculates the number of shared friends based on the previous network. It then applies a geometric transformation to these counts to reduce the influence of large counts. Specifically, if the decay parameter decay is provided, the weighting function used is

f(k; decay) = 1 - (1 - e^{-decay})^k,

where k is the count of shared partners and decay controls how quickly the weight decreases as counts increase. If decay is no

Usage

# binary: gwdelesf(decay, base, type = "OTP")

Arguments

Default MH algorithm respecting the layer constraint

Description

Stratifies the population of dyads edge status: those having ties and those having no ties (hence T/NT). This is useful for improving performance in sparse networks, because it gives at least 50% chance of proposing a toggle of an existing edge.

Details

Multilayer network to single layer network.

Description

Turn a multilayer network object into a single layer network object.

Usage

UnLayer(net, color_pos = "#008000", color_neg = "#E3000F", neg.lty = 2)

Arguments

Value

Single layer network object or a list of network objects for dynamic.sign.

See Also

network.sign

Examples

data("tribes")
tribes_sgl <- UnLayer(tribes)

Dyadwise shared enemies

Description

This term adds one network statistic to the model for each element in 'd' where the i th such statistic equals the number of dyads in the network with exactly 'd[i]' shared enemies. For a directed network, multiple shared enemies definitions are possible.

Usage

# binary: dse(d, type="OTP", in_order=FALSE)

Shared partner types

While there is only one shared partner configuration in the undirected case, nine distinct configurations are possible for directed graphs, selected using the 'type' argument. Currently, terms may be defined with respect to five of these configurations; they are defined here as follows (using terminology from Butts (2008) and the 'relevent' package): - Outgoing Two-path ('"OTP"'): vertex k is an OTP shared partner of ordered pair (i,j) iff i \to k \to j. Also known as "transitive shared partner". - Incoming Two-path ('"ITP"'): vertex k is an ITP shared partner of ordered pair (i,j) iff j \to k \to i. Also known as "cyclical shared partner" - Reciprocated Two-path ('"RTP"'): vertex k is an RTP shared partner of ordered pair (i,j) iff i \leftrightarrow k \leftrightarrow j. - Outgoing Shared Partner ('"OSP"'): vertex k is an OSP shared partner of ordered pair (i,j) iff i \to k, j \to k. - Incoming Shared Partner ('"ISP"'): vertex k is an ISP shared partner of ordered pair (i,j) iff k \to i, k \to j. By default, outgoing two-paths ('"OTP"') are calculated. Note that Robins et al. (2009) define closely related statistics to several of the above, using slightly different terminology.

Note

This term takes an additional term option (see ['options?ergm'][ergm-options]), 'cache.sp', controlling whether the implementation will cache the number of shared partners for each dyad in the network; this is usually enabled by default.

See Also

['ergmTerm'] for index of model terms currently visible to the package.

Keywords

directed, undirected, binary

Dyadwise shared friends

Description

This term adds one network statistic to the model for each element in 'd' where the i th such statistic equals the number of dyads in the network with exactly 'd[i]' shared friends. For a directed network, multiple shared friends definitions are possible.

Usage

# binary: dsf(d, type="OTP", in_order=FALSE)

Shared partner types

While there is only one shared partner configuration in the undirected case, nine distinct configurations are possible for directed graphs, selected using the 'type' argument. Currently, terms may be defined with respect to five of these configurations; they are defined here as follows (using terminology from Butts (2008) and the 'relevent' package): - Outgoing Two-path ('"OTP"'): vertex k is an OTP shared partner of ordered pair (i,j) iff i \to k \to j. Also known as "transitive shared partner". - Incoming Two-path ('"ITP"'): vertex k is an ITP shared partner of ordered pair (i,j) iff j \to k \to i. Also known as "cyclical shared partner" - Reciprocated Two-path ('"RTP"'): vertex k is an RTP shared partner of ordered pair (i,j) iff i \leftrightarrow k \leftrightarrow j. - Outgoing Shared Partner ('"OSP"'): vertex k is an OSP shared partner of ordered pair (i,j) iff i \to k, j \to k. - Incoming Shared Partner ('"ISP"'): vertex k is an ISP shared partner of ordered pair (i,j) iff k \to i, k \to j. By default, outgoing two-paths ('"OTP"') are calculated. Note that Robins et al. (2009) define closely related statistics to several of the above, using slightly different terminology.

Note

This term takes an additional term option (see ['options?ergm'][ergm-options]), 'cache.sp', controlling whether the implementation will cache the number of shared partners for each dyad in the network; this is usually enabled by default.

See Also

['ergmTerm'] for index of model terms currently visible to the package.

Keywords

directed, undirected, binary

ergm.sign: A Package for Exponential Random Graph Models for Signed Networks

Description

The ergm.sign package implements tools to simulate and estimate Signed Exponential Random Graph Models and Temporal Signed Exponential Random Graph Models.

Author(s)

Marc Schalberger

Edgewise shared enemies

Description

This term adds one network statistic to the model for each element in 'd' where the i th such statistic equals the number of edges in the network with exactly 'd[i]' shared enemies. For a directed network, multiple shared enemy definitions are possible.

Usage

# binary: ese(d, type="OTP", L.base=NULL, in_order=FALSE)

Arguments

Shared partner types

While there is only one shared partner configuration in the undirected case, nine distinct configurations are possible for directed graphs, selected using the 'type' argument. Currently, terms may be defined with respect to five of these configurations; they are defined here as follows (using terminology from Butts (2008) and the 'relevent' package): - Outgoing Two-path ('"OTP"'): vertex k is an OTP shared partner of ordered pair (i,j) iff i \to k \to j. Also known as "transitive shared partner". - Incoming Two-path ('"ITP"'): vertex k is an ITP shared partner of ordered pair (i,j) iff j \to k \to i. Also known as "cyclical shared partner" - Reciprocated Two-path ('"RTP"'): vertex k is an RTP shared partner of ordered pair (i,j) iff i \leftrightarrow k \leftrightarrow j. - Outgoing Shared Partner ('"OSP"'): vertex k is an OSP shared partner of ordered pair (i,j) iff i \to k, j \to k. - Incoming Shared Partner ('"ISP"'): vertex k is an ISP shared partner of ordered pair (i,j) iff k \to i, k \to j. By default, outgoing two-paths ('"OTP"') are calculated. Note that Robins et al. (2009) define closely related statistics to several of the above, using slightly different terminology.

Note

This term takes an additional term option (see ['options?ergm'][ergm-options]), 'cache.sp', controlling whether the implementation will cache the number of shared partners for each dyad in the network; this is usually enabled by default.

See Also

['ergmTerm'] for index of model terms currently visible to the package.

Keywords

directed, undirected, binary

Edgewise shared friends

Description

This term adds one network statistic to the model for each element in 'd' where the i th such statistic equals the number of edges in the network with exactly 'd[i]' shared friends. For a directed network, multiple shared friend definitions are possible.

Usage

# binary: esf(d, type="OTP", L.base=NULL, in_order=FALSE)

Arguments

Shared partner types

While there is only one shared partner configuration in the undirected case, nine distinct configurations are possible for directed graphs, selected using the 'type' argument. Currently, terms may be defined with respect to five of these configurations; they are defined here as follows (using terminology from Butts (2008) and the 'relevent' package): - Outgoing Two-path ('"OTP"'): vertex k is an OTP shared partner of ordered pair (i,j) iff i \to k \to j. Also known as "transitive shared partner". - Incoming Two-path ('"ITP"'): vertex k is an ITP shared partner of ordered pair (i,j) iff j \to k \to i. Also known as "cyclical shared partner" - Reciprocated Two-path ('"RTP"'): vertex k is an RTP shared partner of ordered pair (i,j) iff i \leftrightarrow k \leftrightarrow j. - Outgoing Shared Partner ('"OSP"'): vertex k is an OSP shared partner of ordered pair (i,j) iff i \to k, j \to k. - Incoming Shared Partner ('"ISP"'): vertex k is an ISP shared partner of ordered pair (i,j) iff k \to i, k \to j. By default, outgoing two-paths ('"OTP"') are calculated. Note that Robins et al. (2009) define closely related statistics to several of the above, using slightly different terminology.

Note

This term takes an additional term option (see ['options?ergm'][ergm-options]), 'cache.sp', controlling whether the implementation will cache the number of shared partners for each dyad in the network; this is usually enabled by default.

See Also

['ergmTerm'] for index of model terms currently visible to the package.

Keywords

directed, undirected, binary

Logical layer constraint

Description

This layer-aware constraint limits the sample space to those networks for which the specified logical layers are unchanged

Usage

# fixL(Ls)

See Also

['ergmConstraint'] for index of constraints and hints currently visible to the package.

Geometrically weighted dyadwise shared enemies distribution

Description

This term adds one network statistic to the model equal to the geometrically weighted dyadwise shared enemies distribution with decay parameter. Note that the GWDSE statistic is equal to the sum of GWNSE plus GWESE. For a directed network, multiple shared friend definitions are possible.

Usage

# binary: gwdse(decay, fixed=FALSE, cutoff=30, type="OTP", in_order=FALSE)

Arguments

Shared partner types

While there is only one shared partner configuration in the undirected case, nine distinct configurations are possible for directed graphs, selected using the 'type' argument. Currently, terms may be defined with respect to five of these configurations; they are defined here as follows (using terminology from Butts (2008) and the 'relevent' package): - Outgoing Two-path ('"OTP"'): vertex k is an OTP shared partner of ordered pair (i,j) iff i \to k \to j. Also known as "transitive shared partner". - Incoming Two-path ('"ITP"'): vertex k is an ITP shared partner of ordered pair (i,j) iff j \to k \to i. Also known as "cyclical shared partner" - Reciprocated Two-path ('"RTP"'): vertex k is an RTP shared partner of ordered pair (i,j) iff i \leftrightarrow k \leftrightarrow j. - Outgoing Shared Partner ('"OSP"'): vertex k is an OSP shared partner of ordered pair (i,j) iff i \to k, j \to k. - Incoming Shared Partner ('"ISP"'): vertex k is an ISP shared partner of ordered pair (i,j) iff k \to i, k \to j. By default, outgoing two-paths ('"OTP"') are calculated. Note that Robins et al. (2009) define closely related statistics to several of the above, using slightly different terminology.

Note

This term takes an additional term option (see ['options?ergm'][ergm-options]), 'cache.sp', controlling whether the implementation will cache the number of shared partners for each dyad in the network; this is usually enabled by default.

See Also

['ergmTerm'] for index of model terms currently visible to the package.

Keywords

directed, undirected, binary

Geometrically weighted dyadwise shared friends distribution

Description

This term adds one network statistic to the model equal to the geometrically weighted dyadwise shared friends distribution with decay parameter. Note that the GWDSF statistic is equal to the sum of GWNSF plus GWESF. For a directed network, multiple shared friend definitions are possible.

Usage

# binary: gwdsf(decay, fixed=FALSE, cutoff=30, type="OTP", in_order=FALSE)

Arguments

Shared partner types

While there is only one shared partner configuration in the undirected case, nine distinct configurations are possible for directed graphs, selected using the 'type' argument. Currently, terms may be defined with respect to five of these configurations; they are defined here as follows (using terminology from Butts (2008) and the 'relevent' package): - Outgoing Two-path ('"OTP"'): vertex k is an OTP shared partner of ordered pair (i,j) iff i \to k \to j. Also known as "transitive shared partner". - Incoming Two-path ('"ITP"'): vertex k is an ITP shared partner of ordered pair (i,j) iff j \to k \to i. Also known as "cyclical shared partner" - Reciprocated Two-path ('"RTP"'): vertex k is an RTP shared partner of ordered pair (i,j) iff i \leftrightarrow k \leftrightarrow j. - Outgoing Shared Partner ('"OSP"'): vertex k is an OSP shared partner of ordered pair (i,j) iff i \to k, j \to k. - Incoming Shared Partner ('"ISP"'): vertex k is an ISP shared partner of ordered pair (i,j) iff k \to i, k \to j. By default, outgoing two-paths ('"OTP"') are calculated. Note that Robins et al. (2009) define closely related statistics to several of the above, using slightly different terminology.

Note

This term takes an additional term option (see ['options?ergm'][ergm-options]), 'cache.sp', controlling whether the implementation will cache the number of shared partners for each dyad in the network; this is usually enabled by default.

See Also

['ergmTerm'] for index of model terms currently visible to the package.

Keywords

directed, undirected, binary

Geometrically weighted edgewise shared enemy distribution

Description

This term adds a statistic equal to the geometrically weighted edgewise (not dyadwise) shared enemy distribution with decay parameter. For a directed network, multiple shared enemy definitions are possible.

Usage

# binary: gwese(decay, fixed=FALSE, cutoff=30, type="OTP", base=NULL, in_order=FALSE)

Arguments

Shared partner types

While there is only one shared partner configuration in the undirected case, nine distinct configurations are possible for directed graphs, selected using the 'type' argument. Currently, terms may be defined with respect to five of these configurations; they are defined here as follows (using terminology from Butts (2008) and the 'relevent' package): - Outgoing Two-path ('"OTP"'): vertex k is an OTP shared partner of ordered pair (i,j) iff i \to k \to j. Also known as "transitive shared partner". - Incoming Two-path ('"ITP"'): vertex k is an ITP shared partner of ordered pair (i,j) iff j \to k \to i. Also known as "cyclical shared partner" - Reciprocated Two-path ('"RTP"'): vertex k is an RTP shared partner of ordered pair (i,j) iff i \leftrightarrow k \leftrightarrow j. - Outgoing Shared Partner ('"OSP"'): vertex k is an OSP shared partner of ordered pair (i,j) iff i \to k, j \to k. - Incoming Shared Partner ('"ISP"'): vertex k is an ISP shared partner of ordered pair (i,j) iff k \to i, k \to j. By default, outgoing two-paths ('"OTP"') are calculated. Note that Robins et al. (2009) define closely related statistics to several of the above, using slightly different terminology.

Note

This term takes an additional term option (see ['options?ergm'][ergm-options]), 'cache.sp', controlling whether the implementation will cache the number of shared partners for each dyad in the network; this is usually enabled by default.

See Also

['ergmTerm'] for index of model terms currently visible to the package.

Keywords

directed, undirected, binary

Geometrically weighted edgewise shared friend distribution

Description

This term adds a statistic equal to the geometrically weighted edgewise (not dyadwise) shared friend distribution with decay parameter. For a directed network, multiple shared friend definitions are possible.

Usage

# binary: gwesf(decay, fixed=FALSE, cutoff=30, type="OTP", base=NULL, in_order=FALSE)

Arguments

Shared partner types

While there is only one shared partner configuration in the undirected case, nine distinct configurations are possible for directed graphs, selected using the 'type' argument. Currently, terms may be defined with respect to five of these configurations; they are defined here as follows (using terminology from Butts (2008) and the 'relevent' package): - Outgoing Two-path ('"OTP"'): vertex k is an OTP shared partner of ordered pair (i,j) iff i \to k \to j. Also known as "transitive shared partner". - Incoming Two-path ('"ITP"'): vertex k is an ITP shared partner of ordered pair (i,j) iff j \to k \to i. Also known as "cyclical shared partner" - Reciprocated Two-path ('"RTP"'): vertex k is an RTP shared partner of ordered pair (i,j) iff i \leftrightarrow k \leftrightarrow j. - Outgoing Shared Partner ('"OSP"'): vertex k is an OSP shared partner of ordered pair (i,j) iff i \to k, j \to k. - Incoming Shared Partner ('"ISP"'): vertex k is an ISP shared partner of ordered pair (i,j) iff k \to i, k \to j. By default, outgoing two-paths ('"OTP"') are calculated. Note that Robins et al. (2009) define closely related statistics to several of the above, using slightly different terminology.

Note

This term takes an additional term option (see ['options?ergm'][ergm-options]), 'cache.sp', controlling whether the implementation will cache the number of shared partners for each dyad in the network; this is usually enabled by default.

See Also

['ergmTerm'] for index of model terms currently visible to the package.

Keywords

directed, undirected, binary

Geometrically weighted non-edgewise shared enemey distribution

Description

This term adds a statistic equal to the geometrically weighted nonedgewise (that is, over dyads that do not have an edge) shared enemy distribution with decay parameter. For a directed network, multiple shared enemy definitions are possible.

Usage

# binary: gwnse(decay, fixed=FALSE, cutoff=30, type="OTP", base=NULL, in_order=FALSE)

Arguments

Shared partner types

While there is only one shared partner configuration in the undirected case, nine distinct configurations are possible for directed graphs, selected using the 'type' argument. Currently, terms may be defined with respect to five of these configurations; they are defined here as follows (using terminology from Butts (2008) and the 'relevent' package): - Outgoing Two-path ('"OTP"'): vertex k is an OTP shared partner of ordered pair (i,j) iff i \to k \to j. Also known as "transitive shared partner". - Incoming Two-path ('"ITP"'): vertex k is an ITP shared partner of ordered pair (i,j) iff j \to k \to i. Also known as "cyclical shared partner" - Reciprocated Two-path ('"RTP"'): vertex k is an RTP shared partner of ordered pair (i,j) iff i \leftrightarrow k \leftrightarrow j. - Outgoing Shared Partner ('"OSP"'): vertex k is an OSP shared partner of ordered pair (i,j) iff i \to k, j \to k. - Incoming Shared Partner ('"ISP"'): vertex k is an ISP shared partner of ordered pair (i,j) iff k \to i, k \to j. By default, outgoing two-paths ('"OTP"') are calculated. Note that Robins et al. (2009) define closely related statistics to several of the above, using slightly different terminology.

Note

This term takes an additional term option (see ['options?ergm'][ergm-options]), 'cache.sp', controlling whether the implementation will cache the number of shared partners for each dyad in the network; this is usually enabled by default.

See Also

['ergmTerm'] for index of model terms currently visible to the package.

Keywords

directed, undirected, binary

Geometrically weighted non-edgewise shared friend distribution

Description

This term adds a statistic equal to the geometrically weighted nonedgewise (that is, over dyads that do not have an edge) shared friend distribution with decay parameter. For a directed network, multiple shared friend definitions are possible.

Usage

# binary: gwnsf(decay, fixed=FALSE, cutoff=30, type="OTP", base=NULL, in_order=FALSE)

Arguments

Shared partner types

While there is only one shared partner configuration in the undirected case, nine distinct configurations are possible for directed graphs, selected using the 'type' argument. Currently, terms may be defined with respect to five of these configurations; they are defined here as follows (using terminology from Butts (2008) and the 'relevent' package): - Outgoing Two-path ('"OTP"'): vertex k is an OTP shared partner of ordered pair (i,j) iff i \to k \to j. Also known as "transitive shared partner". - Incoming Two-path ('"ITP"'): vertex k is an ITP shared partner of ordered pair (i,j) iff j \to k \to i. Also known as "cyclical shared partner" - Reciprocated Two-path ('"RTP"'): vertex k is an RTP shared partner of ordered pair (i,j) iff i \leftrightarrow k \leftrightarrow j. - Outgoing Shared Partner ('"OSP"'): vertex k is an OSP shared partner of ordered pair (i,j) iff i \to k, j \to k. - Incoming Shared Partner ('"ISP"'): vertex k is an ISP shared partner of ordered pair (i,j) iff k \to i, k \to j. By default, outgoing two-paths ('"OTP"') are calculated. Note that Robins et al. (2009) define closely related statistics to several of the above, using slightly different terminology.

Note

This term takes an additional term option (see ['options?ergm'][ergm-options]), 'cache.sp', controlling whether the implementation will cache the number of shared partners for each dyad in the network; this is usually enabled by default.

See Also

['ergmTerm'] for index of model terms currently visible to the package.

Keywords

directed, undirected, binary

Fit an ERGM with MPLE using a logistic regression model

Description

Returns a fitted logistic regression model used to calculate the maximum pseudolikelihood estimate (MPLE) of an exponential random graph model (ERGM).

Usage

mple_sign(formula, control = control.ergm(), seed = NULL, ...)

Arguments

Details

The MPLE is calculated by first computing matrices of positive and negative change statistics. These are then used to estimate the MPLE via logistic regression. Optionally, the covariance can be estimated using the Godambe method.

Value

An object of class ergm.

See Also

ergmMPLE, ergm, glm

Examples

data(tribes)
mple_sign(tribes ~ Pos(~edges) + Neg(~edges))

Create Signed Network Object

Description

Turn adjacency matrices or edgelists into static or dynamic signed networks.

Usage

network.sign(
  mat = NULL,
  pos.mat = NULL,
  neg.mat = NULL,
  directed = FALSE,
  loops = FALSE,
  matrix.type = c("adjacency", "edgelist"),
  vertex.names = NULL,
  vertex.attr = NULL,
  dual.sign = FALSE,
  timepoints = NULL,
  tie.breaker = c("zero", "positive", "negative", "first", "last"),
  ...
)

Arguments

Value

A signed network of class 'static.sign' or 'dynamic.sign'.

Combine Signed Networks into a Multi- or Dynamic-Network Object

Description

Creates a composite network object from multiple signed networks, suitable for ERGM modeling. Can represent either a multilayer or dynamic signed network structure.

Usage

networks.sign(..., dynamic = FALSE, dual.sign = FALSE)

Arguments

Value

A combined network object of class "multi.sign" or "dynamic.sign", with the appropriate ERGM constraint formula.

Examples

data("tribes")
multi_net <- networks.sign(tribes, tribes)
dyn_net <- networks.sign(list(tribes, tribes), dynamic = TRUE)

Non-edgewise shared enemies

Description

This term adds one network statistic to the model for each element in 'd' where the i th such statistic equals the number of non-edges in the network with exactly 'd[i]' shared enemies. For a directed network, multiple shared enemy definitions are possible.

Usage

# binary: nse(d, type="OTP", base=NULL, in_order=FALSE)

Arguments

Shared partner types

While there is only one shared partner configuration in the undirected case, nine distinct configurations are possible for directed graphs, selected using the 'type' argument. Currently, terms may be defined with respect to five of these configurations; they are defined here as follows (using terminology from Butts (2008) and the 'relevent' package): - Outgoing Two-path ('"OTP"'): vertex k is an OTP shared partner of ordered pair (i,j) iff i \to k \to j. Also known as "transitive shared partner". - Incoming Two-path ('"ITP"'): vertex k is an ITP shared partner of ordered pair (i,j) iff j \to k \to i. Also known as "cyclical shared partner" - Reciprocated Two-path ('"RTP"'): vertex k is an RTP shared partner of ordered pair (i,j) iff i \leftrightarrow k \leftrightarrow j. - Outgoing Shared Partner ('"OSP"'): vertex k is an OSP shared partner of ordered pair (i,j) iff i \to k, j \to k. - Incoming Shared Partner ('"ISP"'): vertex k is an ISP shared partner of ordered pair (i,j) iff k \to i, k \to j. By default, outgoing two-paths ('"OTP"') are calculated. Note that Robins et al. (2009) define closely related statistics to several of the above, using slightly different terminology.

Note

This term takes an additional term option (see ['options?ergm'][ergm-options]), 'cache.sp', controlling whether the implementation will cache the number of shared partners for each dyad in the network; this is usually enabled by default.

See Also

['ergmTerm'] for index of model terms currently visible to the package.

Keywords

directed, undirected, binary

Non-edgewise shared friends

Description

This term adds one network statistic to the model for each element in 'd' where the i th such statistic equals the number of non-edges in the network with exactly 'd[i]' shared friends. For a directed network, multiple shared friend definitions are possible.

Usage

# binary: nsf(d, type="OTP", base=NULL, in_order=FALSE)

Arguments

Shared partner types

While there is only one shared partner configuration in the undirected case, nine distinct configurations are possible for directed graphs, selected using the 'type' argument. Currently, terms may be defined with respect to five of these configurations; they are defined here as follows (using terminology from Butts (2008) and the 'relevent' package): - Outgoing Two-path ('"OTP"'): vertex k is an OTP shared partner of ordered pair (i,j) iff i \to k \to j. Also known as "transitive shared partner". - Incoming Two-path ('"ITP"'): vertex k is an ITP shared partner of ordered pair (i,j) iff j \to k \to i. Also known as "cyclical shared partner" - Reciprocated Two-path ('"RTP"'): vertex k is an RTP shared partner of ordered pair (i,j) iff i \leftrightarrow k \leftrightarrow j. - Outgoing Shared Partner ('"OSP"'): vertex k is an OSP shared partner of ordered pair (i,j) iff i \to k, j \to k. - Incoming Shared Partner ('"ISP"'): vertex k is an ISP shared partner of ordered pair (i,j) iff k \to i, k \to j. By default, outgoing two-paths ('"OTP"') are calculated. Note that Robins et al. (2009) define closely related statistics to several of the above, using slightly different terminology.

Note

This term takes an additional term option (see ['options?ergm'][ergm-options]), 'cache.sp', controlling whether the implementation will cache the number of shared partners for each dyad in the network; this is usually enabled by default.

See Also

['ergmTerm'] for index of model terms currently visible to the package.

Keywords

directed, undirected, binary

Visualization for Dynamic Signed Networks

Description

plot.dynamic.sign() visualizes a dynamic signed network over multiple timepoints.

Usage

## S3 method for class 'dynamic.sign'
plot(
  x,
  col_pos = "#008000",
  col_neg = "#E3000F",
  neg.lty = 1,
  inv_weights = TRUE,
  time = NULL,
  titles = NULL,
  fix.pos = TRUE,
  ...
)

Arguments

Value

A list of plots, one for each selected timepoint.

Layout

Uses a force-directed graph layout based on stress majorization, implemented in the graphlayouts package via layout_with_stress(). Similar to Kamada-Kawai, but generally faster and with better results.

Visualization for Signed Networks

Description

Functions to visualize signed networks in static or dynamic form.

Usage

## S3 method for class 'static.sign'
plot(
  x,
  col_pos = "#008000",
  col_neg = "#E3000F",
  neg.lty = 1,
  inv_weights = TRUE,
  coord = NULL,
  ...
)

Arguments

Value

A plot of the signed network.

Layout

Uses a force-directed graph layout based on stress majorization, implemented in the graphlayouts package via layout_with_stress(). Similar to Kamada-Kawai, but generally faster and with better results.

Static signed networks

plot.static.sign() visualizes a single (static) signed network.

References

Gansner ER, Koren Y, North S (2004). “Graph drawing by stress majorization.” In International Symposium on Graph Drawing, 239–250. Springer.

See Also

UnLayer, layout_with_stress

Examples

data("tribes")
plot(tribes, col_pos = "green", col_neg = "red")

Propose a randomly selected dyad to toggle, respecting the layer constraint

Description

Propose a randomly selected dyad to toggle

Details

Conflict Events in Syrian Civil War

Description

A dynamic network of combat events in the Syrian civil war between 2017 and 2025. The raw data comes from the Armed Conflict Location & Event Data Project Raleigh et al. (2010).

Format

An undirected dynamic.sign object with no loops and eight timepoints.

References

Fritz C, Mehrl M, Thurner PW, Kauermann G (2023). “All that glitters is not gold: Relational events models with spurious events.” Network Science, 11(2), 184–204., Raleigh C, Linke r, Hegre H, Karlsen J (2010). “Introducing ACLED: An armed conflict location and event dataset.” Journal of peace research, 47(5), 651–660.

Examples

data(rebels)

Conflict Events in Syrian Civil War

Description

A pooled dynamic network of combat events in the Syrian civil war between 2017 and 2019 with 4 timepoints. The raw data comes from the Armed Conflict Location & Event Data Project Raleigh et al. (2010).

Format

An undirected dynamic.sign object with no loops and eight timepoints.

References

Fritz C, Mehrl M, Thurner PW, Kauermann G (2023). “All that glitters is not gold: Relational events models with spurious events.” Network Science, 11(2), 184–204., Raleigh C, Linke r, Hegre H, Karlsen J (2010). “Introducing ACLED: An armed conflict location and event dataset.” Journal of peace research, 47(5), 651–660.

Examples

data(rebels)

Statnet Control

Description

A utility to facilitate argument completion of control lists, reexported from 'statnet.common'.

Currently recognised control parameters

This list is updated as packages are loaded and unloaded.

Package ergm

control.ergm

drop, init, init.method, main.method, force.main, main.hessian, checkpoint, resume, MPLE.samplesize, init.MPLE.samplesize, MPLE.type, MPLE.maxit, MPLE.nonvar, MPLE.nonident, MPLE.nonident.tol, MPLE.covariance.samplesize, MPLE.covariance.method, MPLE.covariance.sim.burnin, MPLE.covariance.sim.interval, MPLE.check, MPLE.constraints.ignore, MCMC.prop, MCMC.prop.weights, MCMC.prop.args, MCMC.interval, MCMC.burnin, MCMC.samplesize, MCMC.effectiveSize, MCMC.effectiveSize.damp, MCMC.effectiveSize.maxruns, MCMC.effectiveSize.burnin.pval, MCMC.effectiveSize.burnin.min, MCMC.effectiveSize.burnin.max, MCMC.effectiveSize.burnin.nmin, MCMC.effectiveSize.burnin.nmax, MCMC.effectiveSize.burnin.PC, MCMC.effectiveSize.burnin.scl, MCMC.effectiveSize.order.max, MCMC.return.stats, MCMC.runtime.traceplot, MCMC.maxedges, MCMC.addto.se, MCMC.packagenames, SAN.maxit, SAN.nsteps.times, SAN, MCMLE.termination, MCMLE.maxit, MCMLE.conv.min.pval, MCMLE.confidence, MCMLE.confidence.boost, MCMLE.confidence.boost.threshold, MCMLE.confidence.boost.lag, MCMLE.NR.maxit, MCMLE.NR.reltol, obs.MCMC.mul, obs.MCMC.samplesize.mul, obs.MCMC.samplesize, obs.MCMC.effectiveSize, obs.MCMC.interval.mul, obs.MCMC.interval, obs.MCMC.burnin.mul, obs.MCMC.burnin, obs.MCMC.prop, obs.MCMC.prop.weights, obs.MCMC.prop.args, obs.MCMC.impute.min_informative, obs.MCMC.impute.default_density, MCMLE.min.depfac, MCMLE.sampsize.boost.pow, MCMLE.MCMC.precision, MCMLE.MCMC.max.ESS.frac, MCMLE.metric, MCMLE.method, MCMLE.dampening, MCMLE.dampening.min.ess, MCMLE.dampening.level, MCMLE.steplength.margin, MCMLE.steplength, MCMLE.steplength.parallel, MCMLE.sequential, MCMLE.density.guard.min, MCMLE.density.guard, MCMLE.effectiveSize, obs.MCMLE.effectiveSize, MCMLE.interval, MCMLE.burnin, MCMLE.samplesize.per_theta, MCMLE.samplesize.min, MCMLE.samplesize, obs.MCMLE.samplesize.per_theta, obs.MCMLE.samplesize.min, obs.MCMLE.samplesize, obs.MCMLE.interval, obs.MCMLE.burnin, MCMLE.steplength.solver, MCMLE.last.boost, MCMLE.steplength.esteq, MCMLE.steplength.miss.sample, MCMLE.steplength.min, MCMLE.effectiveSize.interval_drop, MCMLE.save_intermediates, MCMLE.nonvar, MCMLE.nonident, MCMLE.nonident.tol, SA.phase1_n, SA.initial_gain, SA.nsubphases, SA.min_iterations, SA.max_iterations, SA.phase3_n, SA.interval, SA.burnin, SA.samplesize, CD.samplesize.per_theta, obs.CD.samplesize.per_theta, CD.nsteps, CD.multiplicity, CD.nsteps.obs, CD.multiplicity.obs, CD.maxit, CD.conv.min.pval, CD.NR.maxit, CD.NR.reltol, CD.metric, CD.method, CD.dampening, CD.dampening.min.ess, CD.dampening.level, CD.steplength.margin, CD.steplength, CD.adaptive.epsilon, CD.steplength.esteq, CD.steplength.miss.sample, CD.steplength.min, CD.steplength.parallel, CD.steplength.solver, loglik, term.options, seed, parallel, parallel.type, parallel.version.check, parallel.inherit.MT, ...

control.ergm.bridge

bridge.nsteps, bridge.target.se, bridge.bidirectional, drop, MCMC.burnin, MCMC.burnin.between, MCMC.interval, MCMC.samplesize, obs.MCMC.burnin, obs.MCMC.burnin.between, obs.MCMC.interval, obs.MCMC.samplesize, MCMC.prop, MCMC.prop.weights, MCMC.prop.args, obs.MCMC.prop, obs.MCMC.prop.weights, obs.MCMC.prop.args, MCMC.maxedges, MCMC.packagenames, term.options, seed, parallel, parallel.type, parallel.version.check, parallel.inherit.MT, ...

control.ergm.godfather

term.options

control.ergm3

drop, init, init.method, main.method, force.main, main.hessian, checkpoint, resume, MPLE.samplesize, init.MPLE.samplesize, MPLE.type, MPLE.maxit, MPLE.nonvar, MPLE.nonident, MPLE.nonident.tol, MPLE.covariance.samplesize, MPLE.covariance.method, MPLE.covariance.sim.burnin, MPLE.covariance.sim.interval, MPLE.check, MPLE.constraints.ignore, MCMC.prop, MCMC.prop.weights, MCMC.prop.args, MCMC.interval, MCMC.burnin, MCMC.samplesize, MCMC.effectiveSize, MCMC.effectiveSize.damp, MCMC.effectiveSize.maxruns, MCMC.effectiveSize.burnin.pval, MCMC.effectiveSize.burnin.min, MCMC.effectiveSize.burnin.max, MCMC.effectiveSize.burnin.nmin, MCMC.effectiveSize.burnin.nmax, MCMC.effectiveSize.burnin.PC, MCMC.effectiveSize.burnin.scl, MCMC.effectiveSize.order.max, MCMC.return.stats, MCMC.runtime.traceplot, MCMC.maxedges, MCMC.addto.se, MCMC.packagenames, SAN.maxit, SAN.nsteps.times, SAN, MCMLE.termination, MCMLE.maxit, MCMLE.conv.min.pval, MCMLE.confidence, MCMLE.confidence.boost, MCMLE.confidence.boost.threshold, MCMLE.confidence.boost.lag, MCMLE.NR.maxit, MCMLE.NR.reltol, obs.MCMC.mul, obs.MCMC.samplesize.mul, obs.MCMC.samplesize, obs.MCMC.effectiveSize, obs.MCMC.interval.mul, obs.MCMC.interval, obs.MCMC.burnin.mul, obs.MCMC.burnin, obs.MCMC.prop, obs.MCMC.prop.weights, obs.MCMC.prop.args, obs.MCMC.impute.min_informative, obs.MCMC.impute.default_density, MCMLE.min.depfac, MCMLE.sampsize.boost.pow, MCMLE.MCMC.precision, MCMLE.MCMC.max.ESS.frac, MCMLE.metric, MCMLE.method, MCMLE.dampening, MCMLE.dampening.min.ess, MCMLE.dampening.level, MCMLE.steplength.margin, MCMLE.steplength, MCMLE.steplength.parallel, MCMLE.sequential, MCMLE.density.guard.min, MCMLE.density.guard, MCMLE.effectiveSize, obs.MCMLE.effectiveSize, MCMLE.interval, MCMLE.burnin, MCMLE.samplesize.per_theta, MCMLE.samplesize.min, MCMLE.samplesize, obs.MCMLE.samplesize.per_theta, obs.MCMLE.samplesize.min, obs.MCMLE.samplesize, obs.MCMLE.interval, obs.MCMLE.burnin, MCMLE.steplength.solver, MCMLE.last.boost, MCMLE.steplength.esteq, MCMLE.steplength.miss.sample, MCMLE.steplength.min, MCMLE.effectiveSize.interval_drop, MCMLE.save_intermediates, MCMLE.nonvar, MCMLE.nonident, MCMLE.nonident.tol, SA.phase1_n, SA.initial_gain, SA.nsubphases, SA.min_iterations, SA.max_iterations, SA.phase3_n, SA.interval, SA.burnin, SA.samplesize, CD.samplesize.per_theta, obs.CD.samplesize.per_theta, CD.nsteps, CD.multiplicity, CD.nsteps.obs, CD.multiplicity.obs, CD.maxit, CD.conv.min.pval, CD.NR.maxit, CD.NR.reltol, CD.metric, CD.method, CD.dampening, CD.dampening.min.ess, CD.dampening.level, CD.steplength.margin, CD.steplength, CD.adaptive.epsilon, CD.steplength.esteq, CD.steplength.miss.sample, CD.steplength.min, CD.steplength.parallel, CD.steplength.solver, loglik, term.options, seed, parallel, parallel.type, parallel.version.check, parallel.inherit.MT, ...

control.gof.ergm

nsim, MCMC.burnin, MCMC.interval, MCMC.batch, MCMC.prop, MCMC.prop.weights, MCMC.prop.args, MCMC.maxedges, MCMC.packagenames, MCMC.runtime.traceplot, network.output, seed, parallel, parallel.type, parallel.version.check, parallel.inherit.MT

control.gof.formula

nsim, MCMC.burnin, MCMC.interval, MCMC.batch, MCMC.prop, MCMC.prop.weights, MCMC.prop.args, MCMC.maxedges, MCMC.packagenames, MCMC.runtime.traceplot, network.output, seed, parallel, parallel.type, parallel.version.check, parallel.inherit.MT

control.logLik.ergm

bridge.nsteps, bridge.target.se, bridge.bidirectional, drop, MCMC.burnin, MCMC.interval, MCMC.samplesize, obs.MCMC.samplesize, obs.MCMC.interval, obs.MCMC.burnin, MCMC.prop, MCMC.prop.weights, MCMC.prop.args, obs.MCMC.prop, obs.MCMC.prop.weights, obs.MCMC.prop.args, MCMC.maxedges, MCMC.packagenames, term.options, seed, parallel, parallel.type, parallel.version.check, parallel.inherit.MT, ...

control.san

SAN.maxit, SAN.tau, SAN.invcov, SAN.invcov.diag, SAN.nsteps.alloc, SAN.nsteps, SAN.samplesize, SAN.prop, SAN.prop.weights, SAN.prop.args, SAN.packagenames, SAN.ignore.finite.offsets, term.options, seed, parallel, parallel.type, parallel.version.check, parallel.inherit.MT

control.simulate

MCMC.burnin, MCMC.interval, MCMC.prop, MCMC.prop.weights, MCMC.prop.args, MCMC.batch, MCMC.effectiveSize, MCMC.effectiveSize.damp, MCMC.effectiveSize.maxruns, MCMC.effectiveSize.burnin.pval, MCMC.effectiveSize.burnin.min, MCMC.effectiveSize.burnin.max, MCMC.effectiveSize.burnin.nmin, MCMC.effectiveSize.burnin.nmax, MCMC.effectiveSize.burnin.PC, MCMC.effectiveSize.burnin.scl, MCMC.effectiveSize.order.max, MCMC.maxedges, MCMC.packagenames, MCMC.runtime.traceplot, network.output, term.options, parallel, parallel.type, parallel.version.check, parallel.inherit.MT, ...

control.simulate.ergm

MCMC.burnin, MCMC.interval, MCMC.scale, MCMC.prop, MCMC.prop.weights, MCMC.prop.args, MCMC.batch, MCMC.effectiveSize, MCMC.effectiveSize.damp, MCMC.effectiveSize.maxruns, MCMC.effectiveSize.burnin.pval, MCMC.effectiveSize.burnin.min, MCMC.effectiveSize.burnin.max, MCMC.effectiveSize.burnin.nmin, MCMC.effectiveSize.burnin.nmax, MCMC.effectiveSize.burnin.PC, MCMC.effectiveSize.burnin.scl, MCMC.effectiveSize.order.max, MCMC.maxedges, MCMC.packagenames, MCMC.runtime.traceplot, network.output, term.options, parallel, parallel.type, parallel.version.check, parallel.inherit.MT, ...

control.simulate.formula

MCMC.burnin, MCMC.interval, MCMC.prop, MCMC.prop.weights, MCMC.prop.args, MCMC.batch, MCMC.effectiveSize, MCMC.effectiveSize.damp, MCMC.effectiveSize.maxruns, MCMC.effectiveSize.burnin.pval, MCMC.effectiveSize.burnin.min, MCMC.effectiveSize.burnin.max, MCMC.effectiveSize.burnin.nmin, MCMC.effectiveSize.burnin.nmax, MCMC.effectiveSize.burnin.PC, MCMC.effectiveSize.burnin.scl, MCMC.effectiveSize.order.max, MCMC.maxedges, MCMC.packagenames, MCMC.runtime.traceplot, network.output, term.options, parallel, parallel.type, parallel.version.check, parallel.inherit.MT, ...

control.simulate.formula.ergm

MCMC.burnin, MCMC.interval, MCMC.prop, MCMC.prop.weights, MCMC.prop.args, MCMC.batch, MCMC.effectiveSize, MCMC.effectiveSize.damp, MCMC.effectiveSize.maxruns, MCMC.effectiveSize.burnin.pval, MCMC.effectiveSize.burnin.min, MCMC.effectiveSize.burnin.max, MCMC.effectiveSize.burnin.nmin, MCMC.effectiveSize.burnin.nmax, MCMC.effectiveSize.burnin.PC, MCMC.effectiveSize.burnin.scl, MCMC.effectiveSize.order.max, MCMC.maxedges, MCMC.packagenames, MCMC.runtime.traceplot, network.output, term.options, parallel, parallel.type, parallel.version.check, parallel.inherit.MT, ...

Package ergm.multi

control.gofN

nsim, obs.twostage, array.max, simulate, obs.simulate, parallel, parallel.type, parallel.version.check, parallel.inherit.MT

control.gofN.ergm

nsim, obs.twostage, array.max, simulate, obs.simulate, parallel, parallel.type, parallel.version.check, parallel.inherit.MT

Package tergm

control.simulate.formula.tergm

MCMC.burnin.min, MCMC.burnin.max, MCMC.burnin.pval, MCMC.burnin.add, MCMC.prop, MCMC.prop.weights, MCMC.prop.args, MCMC.maxedges, MCMC.maxchanges, term.options, MCMC.packagenames

control.simulate.network

MCMC.burnin.min, MCMC.burnin.max, MCMC.burnin.pval, MCMC.burnin.add, MCMC.prop.form, MCMC.prop.diss, MCMC.prop.weights.form, MCMC.prop.weights.diss, MCMC.prop.args.form, MCMC.prop.args.diss, MCMC.maxedges, MCMC.maxchanges, term.options, MCMC.packagenames

control.simulate.stergm

MCMC.burnin.min, MCMC.burnin.max, MCMC.burnin.pval, MCMC.burnin.add, MCMC.prop.form, MCMC.prop.diss, MCMC.prop.weights.form, MCMC.prop.weights.diss, MCMC.prop.args.form, MCMC.prop.args.diss, MCMC.maxedges, MCMC.maxchanges, term.options, MCMC.packagenames

control.simulate.tergm

MCMC.burnin.min, MCMC.burnin.max, MCMC.burnin.pval, MCMC.burnin.add, MCMC.prop, MCMC.prop.weights, MCMC.prop.args, MCMC.maxedges, MCMC.maxchanges, term.options, MCMC.packagenames

control.stergm

init.form, init.diss, init.method, force.main, MCMC.prop.form, MCMC.prop.diss, MCMC.prop.weights.form, MCMC.prop.args.form, MCMC.prop.weights.diss, MCMC.prop.args.diss, MCMC.maxedges, MCMC.maxchanges, MCMC.packagenames, CMLE.MCMC.burnin, CMLE.MCMC.interval, CMLE.ergm, CMLE.form.ergm, CMLE.diss.ergm, CMLE.NA.impute, CMLE.term.check.override, EGMME.main.method, EGMME.initialfit.control, EGMME.MCMC.burnin.min, EGMME.MCMC.burnin.max, EGMME.MCMC.burnin.pval, EGMME.MCMC.burnin.add, MCMC.burnin, MCMC.burnin.mul, SAN.maxit, SAN.nsteps.times, SAN, SA.restarts, SA.burnin, SA.plot.progress, SA.max.plot.points, SA.plot.stats, SA.init.gain, SA.gain.decay, SA.runlength, SA.interval.mul, SA.init.interval, SA.min.interval, SA.max.interval, SA.phase1.minruns, SA.phase1.tries, SA.phase1.jitter, SA.phase1.max.q, SA.phase1.backoff.rat, SA.phase2.levels.max, SA.phase2.levels.min, SA.phase2.max.mc.se, SA.phase2.repeats, SA.stepdown.maxn, SA.stepdown.p, SA.stop.p, SA.stepdown.ct, SA.phase2.backoff.rat, SA.keep.oh, SA.keep.min.runs, SA.keep.min, SA.phase2.jitter.mul, SA.phase2.maxreljump, SA.guard.mul, SA.par.eff.pow, SA.robust, SA.oh.memory, SA.refine, SA.se, SA.phase3.samplesize.runs, SA.restart.on.err, term.options, seed, parallel, parallel.type, parallel.version.check, parallel.inherit.MT, ...

control.tergm

init, init.method, force.main, MCMC.prop, MCMC.prop.weights, MCMC.prop.args, MCMC.maxedges, MCMC.maxchanges, MCMC.packagenames, CMLE.MCMC.burnin, CMLE.MCMC.interval, CMLE.ergm, CMLE.NA.impute, CMLE.term.check.override, EGMME.main.method, EGMME.initialfit.control, EGMME.MCMC.burnin.min, EGMME.MCMC.burnin.max, EGMME.MCMC.burnin.pval, EGMME.MCMC.burnin.add, MCMC.burnin, MCMC.burnin.mul, SAN.maxit, SAN.nsteps.times, SAN, SA.restarts, SA.burnin, SA.plot.progress, SA.max.plot.points, SA.plot.stats, SA.init.gain, SA.gain.decay, SA.runlength, SA.interval.mul, SA.init.interval, SA.min.interval, SA.max.interval, SA.phase1.minruns, SA.phase1.tries, SA.phase1.jitter, SA.phase1.max.q, SA.phase1.backoff.rat, SA.phase2.levels.max, SA.phase2.levels.min, SA.phase2.max.mc.se, SA.phase2.repeats, SA.stepdown.maxn, SA.stepdown.p, SA.stop.p, SA.stepdown.ct, SA.phase2.backoff.rat, SA.keep.oh, SA.keep.min.runs, SA.keep.min, SA.phase2.jitter.mul, SA.phase2.maxreljump, SA.guard.mul, SA.par.eff.pow, SA.robust, SA.oh.memory, SA.refine, SA.se, SA.phase3.samplesize.runs, SA.restart.on.err, term.options, seed, parallel, parallel.type, parallel.version.check, parallel.inherit.MT

control.tergm.godfather

term.options

See Also

[statnet.common::snctrl()]

Common Sponsor Data for Syrian Civil War Factions

Description

A data frame containing binary indicators for whether each faction in the Syrian civil war is sponsored by a common external actor.

Format

A matrix with 68 rows and 68 columns.

References

Fritz C, Mehrl M, Thurner PW, Kauermann G (2023). “All that glitters is not gold: Relational events models with spurious events.” Network Science, 11(2), 184–204.

Examples

data(sponsor)

Network Attributes for Signed Networks

Description

Print descriptive statistics of a signed network.

Usage

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

## S3 method for class 'dynamic.sign'
summary(object, time = NULL, names = NULL, ...)

Arguments

Value

A data frame or matrix with network attributes.

Static signed networks

summary.static.sign() summarizes a single (static) signed network.

See Also

network.sign, UnLayer

Examples

data("tribes")
summary(tribes)

Summary formula method for dynamic signed networks

Description

Calculates statistics for dynamic.sign objects at specified timepoints.

Usage

## S3 method for class 'dynamic.sign'
summary_formula(object, at, ..., basis = NULL)

Arguments

Value

Matrix of statistics for each timepoint.

Read Highland Tribes

Description

A static 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 static.sign object with no loops.

References

Taken from UCINET IV, which cites the following: Hage P, Harary F (1983). Structural Models in Anthropology, Cambridge Studies in Social and Cultural Anthropology. Cambridge University Press. ISBN 9780521273114., Read KE (1954). “Cultures of the central highlands, New Guinea.” Southwestern Journal of Anthropology, 10(1), 1–43.

Examples

data(tribes)