Description Usage Arguments Details Value References See Also Examples

`tergm`

is used for finding Temporal ERGMs' (TERGMs) and Separable Temporal ERGMs' (STERGMs)
Conditional MLE (CMLE) (Krivitsky and Handcock, 2010) and Equilibrium
Generalized Method of Moments Estimator (EGMME) (Krivitsky, 2009).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 |

`formula` |
an ERGM formula. |

`constraints` |
A one-sided formula specifying one or more constraints on
the support of the distribution of the networks being modeled, using syntax
similar to the The default is See the ERGM constraints documentation for the
constraints implemented in the Note that not all possible combinations of constraints are supported. |

`estimate` |
One of "EGMME" for Equilibrium Generalized Method of Moments Estimation, based on a single network with some temporal information and making an assumption that it is a product of a TERGM process running to its stationary (equilibrium) distribution; "CMLE" for Conditional Maximum Likelihood Estimation, modeling a transition between two networks, or "CMPLE" for Conditional Maximum PseudoLikelihood Estimation, using MPLE instead of MLE. CMPLE is extremely inaccurate at this time. |

`times` |
For CMLE and CMPLE estimation, times or indexes at
which the networks whose transition is to be modeled are
observed. Default to |

`offset.coef` |
Numeric vector to specify offset parameters. |

`targets` |
One-sided |

`target.stats` |
A vector specifying the values of the |

`SAN.offsets` |
Offset coefficients (if any) to use during the SAN run. |

`eval.loglik` |
Whether or not to calculate the log-likelihood
of a CMLE TERGM fit. See |

`control` |
A list of control parameters for algorithm tuning.
Constructed using |

`verbose` |
logical or integer; if TRUE or positive, the program will print out progress information. Higher values result in more output. |

`...` |
Additional arguments, to be passed to lower-level functions. |

**Model Terms** See `ergm`

and `ergm-terms`

for
details. At this time, only linear ERGM terms are allowed.

For a brief demonstration, please see the tergm package vignette:

`browseVignettes(package='tergm')`

A more detailed tutorial is available on the statnet wiki: https://statnet.org/Workshops/tergm/tergm_tutorial.html

`tergm`

returns an object of class `tergm`

that
inherits from `ergm`

and has the usual methods (`coef.ergm()`

,
`summary.ergm()`

, `mcmc.diagnostics()`

, etc.) implemented for
it. Note that `gof()`

only works for the CMLE method.

Krackhardt, D and Handcock, MS (2006) Heider vs Simmel: Emergent features in dynamic structures. ICML Workshop on Statistical Network Analysis. Springer, Berlin, Heidelberg, 2006.

Hanneke S, Fu W, and Xing EP (2010). Discrete
Temporal Models of Social Networks. *Electronic Journal of Statistics*,
2010, 4, 585-605.
doi: 10.1214/09-EJS548

Krivitsky P.N. and Handcock M.S. (2014) A Separable Model for Dynamic Networks. *Journal of the Royal Statistical Society, Series B*, 76(1): 29-46. doi: 10.1111/rssb.12014

Krivitsky, P.N. (2012). Modeling of Dynamic Networks based on
Egocentric Data with Durational Information. *Pennsylvania State
University Department of Statistics Technical Report*, 2012(2012-01).
http://stat.psu.edu/research/technical-report-files/2012-technical-reports/modeling-of-dynamic-networks-based-on-egocentric-data-with-durational-information

`ergm()`

, `network()`

, `%v%`

, `%n%`

, `ergm-terms`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | ```
## Not run:
# EGMME Example
par(ask=FALSE)
n<-30
g0<-network.initialize(n,dir=FALSE)
# edges, degree(1), mean.age
target.stats<-c( n*1/2, n*0.6, 20)
dynfit<-tergm(g0 ~ Form(~edges + degree(1)) + Diss(~edges),
targets = ~edges+degree(1)+mean.age,
target.stats=target.stats, estimate="EGMME",
control=control.tergm(SA.plot.progress=TRUE))
par(ask=TRUE)
mcmc.diagnostics(dynfit)
summary(dynfit)
## End(Not run)
# CMLE Example
data(samplk)
# Fit a transition from Time 1 to Time 2
samplk12 <- tergm(list(samplk1, samplk2)~
Form(~edges+mutual+transitiveties+cyclicalties)+
Diss(~edges+mutual+transitiveties+cyclicalties),
estimate="CMLE")
mcmc.diagnostics(samplk12)
summary(samplk12)
samplk12.gof <- gof(samplk12)
samplk12.gof
plot(samplk12.gof)
plot(samplk12.gof, plotlogodds=TRUE)
# Fit a transition from Time 1 to Time 2 and from Time 2 to Time 3 jointly
samplk123 <- tergm(list(samplk1, samplk2, samplk3)~
Form(~edges+mutual+transitiveties+cyclicalties)+
Diss(~edges+mutual+transitiveties+cyclicalties),
estimate="CMLE")
mcmc.diagnostics(samplk123)
summary(samplk123)
``` |

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