RSiena-package: Simulation Investigation for Empirical Network Analysis
In RSiena: Siena - Simulation Investigation for Empirical Network Analysis
Description
Details
Author(s)
References
See Also
Examples
Description
Fits statistical models to longitudinal sets of networks, and to
longitudinal sets of networks and behavioral variables.
Not only one-mode networks but also two-mode networks
and multivariate networks are allowed.
The models are stochastic actor-oriented models.
Package "RSienaTest" is the development version, and
is distributed through R-Forge, see
http://r-forge.r-project.org/R/?group_id=461.
Package "RSiena" is the official release.
Details
The main flow of operations of this package is as follows.
Data objects can be created from matrices and
vectors using sienaDependent, coCovar,
varCovar, coDyadCovar, etc.,
and finally sienaDataCreate.
Effects are selected using an sienaEffects object,
which can be created using getEffects
and may be further specified by includeEffects,
setEffect, and includeInteraction.
Control of the estimation algorithm requires a
sienaAlgorithm object that
defines the settings (parameters) of the algorithm,
and which can be created by sienaAlgorithmCreate.
Function siena07 is used to fit a model.
Function sienaGOF can be used for studying goodness of fit.
A general introduction to the method is available in the tutorial paper
Snijders, van de Bunt, and Steglich (2010).
Next to the help pages, more detailed help is available in the manual
(see below) and a lot of information is at the website (also see below).
Package: RSiena
Type: Package
Version: 1.2-27
Date: 2020-09-16
Depends: R (>= 2.15.0)
Imports: Matrix, lattice, parallel, MASS, methods
Suggests: xtable, network, tools, codetools, tcltk, knitr, rmarkdown
SystemRequirements: GNU make
License: GPL-3
LazyData: yes
NeedsCompilation: yes
BuildResaveData: no
Author(s)
Ruth Ripley, Krists Boitmanis, Tom Snijders, Felix Schoenenberger,
Nynke Niezink.
Contributions by Josh Lospinoso, Charlotte Greenan, Christian Steglich,
Johan Koskinen, Mark Ortmann, Natalie Indlekofer,
Christoph Stadtfeld, Per Block, Marion Hoffman, Michael Schweinberger, and Robert Hellpap.
Maintainer: Tom A.B. Snijders <tom.snijders@nuffield.ox.ac.uk>
References
Schweinberger, Michael, and Snijders, Tom A.B. (2007).
Markov models for digraph panel data:
Monte Carlo-based derivative estimation.
Computational Statistics and Data Analysis 51, 4465–4483.
Snijders, Tom A.B. (2001).
The statistical evaluation of social network dynamics.
Sociological Methodology 31, 361-395.
Snijders, Tom A.B. (2017). Stochastic Actor-Oriented Models for Network
Dynamics.
Annual Review of Statistics and Its Application 4, 343–363.
Snijders, Tom A.B., van de Bunt, Gerhard G., and
Steglich, Christian E.G. (2010).
Introduction to actor-based models for network dynamics.
Social Networks 32, 44–60.
Snijders, Tom A.B., Steglich, Christian E.G., and Schweinberger,
Michael (2007). Modeling the co-evolution of networks and behavior.
Pp. 41–71 in Longitudinal models in the behavioral
and related sciences,
edited by Kees van Montfort, Han Oud and Albert Satorra; Lawrence Erlbaum.
Steglich, Christian E.G., Snijders, Tom A.B., and
Pearson, Michael A. (2010). Dynamic networks and behavior:
Separating selection from influence. Sociological Methodology
40, 329–393.
The manual: http://www.stats.ox.ac.uk/~snijders/siena/RSiena_Manual.pdf
The website: http://www.stats.ox.ac.uk/~snijders/siena/.
See Also
Examples
1
2
3
4
5
6
7
8
mynet1 <- sienaDependent(array(c(tmp3, tmp4), dim=c(32, 32, 2)))
mydata <- sienaDataCreate(mynet1)
myeff <- getEffects(mydata)
myeff <- includeEffects(myeff, transTrip)
myeff
myalgorithm <- sienaAlgorithmCreate(nsub=3, n3=200)
ans <- siena07(myalgorithm, data=mydata, effects=myeff, batch=TRUE)
summary(ans)
Example output
Warning message:
no DISPLAY variable so Tk is not available
effectName include fix test initialValue parm
1 transitive triplets TRUE FALSE FALSE 0 0
effectName include fix test initialValue parm
1 basic rate parameter mynet1 TRUE FALSE FALSE 4.80941 0
2 outdegree (density) TRUE FALSE FALSE -0.56039 0
3 reciprocity TRUE FALSE FALSE 0.00000 0
4 transitive triplets TRUE FALSE FALSE 0.00000 0
Start phase 0
theta: -0.56 0.00 0.00
Start phase 1
Phase 1 Iteration 1 Progress: 0%
Phase 1 Iteration 2 Progress: 0%
Phase 1 Iteration 3 Progress: 0%
Phase 1 Iteration 4 Progress: 0%
Phase 1 Iteration 5 Progress: 0%
Phase 1 Iteration 10 Progress: 1%
Phase 1 Iteration 15 Progress: 1%
Phase 1 Iteration 20 Progress: 2%
Phase 1 Iteration 25 Progress: 2%
Phase 1 Iteration 30 Progress: 2%
Phase 1 Iteration 35 Progress: 3%
Phase 1 Iteration 40 Progress: 3%
Phase 1 Iteration 45 Progress: 4%
Phase 1 Iteration 50 Progress: 4%
theta: -1.203 0.464 0.198
Start phase 2.1
Phase 2 Subphase 1 Iteration 1 Progress: 16%
Phase 2 Subphase 1 Iteration 2 Progress: 16%
theta -1.368 0.784 0.253
ac 0.354 2.301 0.823
Phase 2 Subphase 1 Iteration 3 Progress: 16%
Phase 2 Subphase 1 Iteration 4 Progress: 16%
theta -1.604 1.054 0.354
ac 0.137 1.651 0.392
Phase 2 Subphase 1 Iteration 5 Progress: 16%
Phase 2 Subphase 1 Iteration 6 Progress: 17%
theta -1.738 1.263 0.336
ac 0.0924 1.6154 0.3982
Phase 2 Subphase 1 Iteration 7 Progress: 17%
Phase 2 Subphase 1 Iteration 8 Progress: 17%
theta -1.74 1.39 0.28
ac -0.1394 0.6914 0.0132
Phase 2 Subphase 1 Iteration 9 Progress: 17%
Phase 2 Subphase 1 Iteration 10 Progress: 17%
theta -1.803 1.490 0.301
ac -0.1410 0.7703 0.0159
theta -1.736 1.331 0.316
ac -0.1559 -0.0063 -0.2163
theta: -1.736 1.331 0.316
Start phase 2.2
Phase 2 Subphase 2 Iteration 1 Progress: 34%
Phase 2 Subphase 2 Iteration 2 Progress: 34%
Phase 2 Subphase 2 Iteration 3 Progress: 34%
Phase 2 Subphase 2 Iteration 4 Progress: 34%
Phase 2 Subphase 2 Iteration 5 Progress: 35%
Phase 2 Subphase 2 Iteration 6 Progress: 35%
Phase 2 Subphase 2 Iteration 7 Progress: 35%
Phase 2 Subphase 2 Iteration 8 Progress: 35%
Phase 2 Subphase 2 Iteration 9 Progress: 35%
Phase 2 Subphase 2 Iteration 10 Progress: 35%
theta -1.749 1.379 0.311
ac -0.00179 -0.02674 -0.17410
theta: -1.749 1.379 0.311
Start phase 2.3
Phase 2 Subphase 3 Iteration 1 Progress: 55%
Phase 2 Subphase 3 Iteration 2 Progress: 55%
Phase 2 Subphase 3 Iteration 3 Progress: 55%
Phase 2 Subphase 3 Iteration 4 Progress: 56%
Phase 2 Subphase 3 Iteration 5 Progress: 56%
Phase 2 Subphase 3 Iteration 6 Progress: 56%
Phase 2 Subphase 3 Iteration 7 Progress: 56%
Phase 2 Subphase 3 Iteration 8 Progress: 56%
Phase 2 Subphase 3 Iteration 9 Progress: 56%
Phase 2 Subphase 3 Iteration 10 Progress: 56%
theta -1.757 1.311 0.321
ac -0.204 -0.207 -0.290
theta: -1.757 1.311 0.321
Start phase 3
Estimates, standard errors and convergence t-ratios
Estimate Standard Convergence
Error t-ratio
Rate parameters:
0 Rate parameter 3.1093 ( 0.4637 )
1. eval outdegree (density) -1.7574 ( 0.2411 ) 0.0083
2. eval reciprocity 1.3108 ( 0.3888 ) 0.0261
3. eval transitive triplets 0.3214 ( 0.0692 ) 0.0700
Overall maximum convergence ratio: 0.1212
Total of 530 iteration steps.
Covariance matrix of estimates (correlations below diagonal)
0.058 -0.044 -0.010
-0.473 0.151 -0.004
-0.613 -0.148 0.005
Derivative matrix of expected statistics X by parameters:
63.686 34.523 224.692
23.901 25.757 102.238
168.066 99.844 875.403
Covariance matrix of X (correlations below diagonal):
71.452 40.771 270.699
0.700 47.445 185.353
0.852 0.716 1411.229
RSiena documentation built on Sept. 24, 2020, 3 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Details Author(s) References See Also Examples
Description
Fits statistical models to longitudinal sets of networks, and to longitudinal sets of networks and behavioral variables. Not only one-mode networks but also two-mode networks and multivariate networks are allowed. The models are stochastic actor-oriented models.
Package "RSienaTest" is the development version, and
is distributed through R-Forge, see
http://r-forge.r-project.org/R/?group_id=461.
Package "RSiena" is the official release.
Details
The main flow of operations of this package is as follows.
Data objects can be created from matrices and
vectors using sienaDependent, coCovar,
varCovar, coDyadCovar, etc.,
and finally sienaDataCreate.
Effects are selected using an sienaEffects object,
which can be created using getEffects
and may be further specified by includeEffects,
setEffect, and includeInteraction.
Control of the estimation algorithm requires a
sienaAlgorithm object that
defines the settings (parameters) of the algorithm,
and which can be created by sienaAlgorithmCreate.
Function siena07 is used to fit a model.
Function sienaGOF can be used for studying goodness of fit.
A general introduction to the method is available in the tutorial paper Snijders, van de Bunt, and Steglich (2010). Next to the help pages, more detailed help is available in the manual (see below) and a lot of information is at the website (also see below).
| Package: | RSiena |
| Type: | Package |
| Version: | 1.2-27 |
| Date: | 2020-09-16 |
| Depends: | R (>= 2.15.0) |
| Imports: | Matrix, lattice, parallel, MASS, methods |
| Suggests: | xtable, network, tools, codetools, tcltk, knitr, rmarkdown |
| SystemRequirements: | GNU make |
| License: | GPL-3 |
| LazyData: | yes |
| NeedsCompilation: | yes |
| BuildResaveData: | no |
Author(s)
Ruth Ripley, Krists Boitmanis, Tom Snijders, Felix Schoenenberger, Nynke Niezink. Contributions by Josh Lospinoso, Charlotte Greenan, Christian Steglich, Johan Koskinen, Mark Ortmann, Natalie Indlekofer, Christoph Stadtfeld, Per Block, Marion Hoffman, Michael Schweinberger, and Robert Hellpap.
Maintainer: Tom A.B. Snijders <tom.snijders@nuffield.ox.ac.uk>
References
Schweinberger, Michael, and Snijders, Tom A.B. (2007). Markov models for digraph panel data: Monte Carlo-based derivative estimation. Computational Statistics and Data Analysis 51, 4465–4483.
Snijders, Tom A.B. (2001). The statistical evaluation of social network dynamics. Sociological Methodology 31, 361-395.
Snijders, Tom A.B. (2017). Stochastic Actor-Oriented Models for Network Dynamics. Annual Review of Statistics and Its Application 4, 343–363.
Snijders, Tom A.B., van de Bunt, Gerhard G., and Steglich, Christian E.G. (2010). Introduction to actor-based models for network dynamics. Social Networks 32, 44–60.
Snijders, Tom A.B., Steglich, Christian E.G., and Schweinberger, Michael (2007). Modeling the co-evolution of networks and behavior. Pp. 41–71 in Longitudinal models in the behavioral and related sciences, edited by Kees van Montfort, Han Oud and Albert Satorra; Lawrence Erlbaum.
Steglich, Christian E.G., Snijders, Tom A.B., and Pearson, Michael A. (2010). Dynamic networks and behavior: Separating selection from influence. Sociological Methodology 40, 329–393.
The manual: http://www.stats.ox.ac.uk/~snijders/siena/RSiena_Manual.pdf
The website: http://www.stats.ox.ac.uk/~snijders/siena/.
See Also
Examples
1 2 3 4 5 6 7 8 | mynet1 <- sienaDependent(array(c(tmp3, tmp4), dim=c(32, 32, 2)))
mydata <- sienaDataCreate(mynet1)
myeff <- getEffects(mydata)
myeff <- includeEffects(myeff, transTrip)
myeff
myalgorithm <- sienaAlgorithmCreate(nsub=3, n3=200)
ans <- siena07(myalgorithm, data=mydata, effects=myeff, batch=TRUE)
summary(ans)
|
Example output
Warning message:
no DISPLAY variable so Tk is not available
effectName include fix test initialValue parm
1 transitive triplets TRUE FALSE FALSE 0 0
effectName include fix test initialValue parm
1 basic rate parameter mynet1 TRUE FALSE FALSE 4.80941 0
2 outdegree (density) TRUE FALSE FALSE -0.56039 0
3 reciprocity TRUE FALSE FALSE 0.00000 0
4 transitive triplets TRUE FALSE FALSE 0.00000 0
Start phase 0
theta: -0.56 0.00 0.00
Start phase 1
Phase 1 Iteration 1 Progress: 0%
Phase 1 Iteration 2 Progress: 0%
Phase 1 Iteration 3 Progress: 0%
Phase 1 Iteration 4 Progress: 0%
Phase 1 Iteration 5 Progress: 0%
Phase 1 Iteration 10 Progress: 1%
Phase 1 Iteration 15 Progress: 1%
Phase 1 Iteration 20 Progress: 2%
Phase 1 Iteration 25 Progress: 2%
Phase 1 Iteration 30 Progress: 2%
Phase 1 Iteration 35 Progress: 3%
Phase 1 Iteration 40 Progress: 3%
Phase 1 Iteration 45 Progress: 4%
Phase 1 Iteration 50 Progress: 4%
theta: -1.203 0.464 0.198
Start phase 2.1
Phase 2 Subphase 1 Iteration 1 Progress: 16%
Phase 2 Subphase 1 Iteration 2 Progress: 16%
theta -1.368 0.784 0.253
ac 0.354 2.301 0.823
Phase 2 Subphase 1 Iteration 3 Progress: 16%
Phase 2 Subphase 1 Iteration 4 Progress: 16%
theta -1.604 1.054 0.354
ac 0.137 1.651 0.392
Phase 2 Subphase 1 Iteration 5 Progress: 16%
Phase 2 Subphase 1 Iteration 6 Progress: 17%
theta -1.738 1.263 0.336
ac 0.0924 1.6154 0.3982
Phase 2 Subphase 1 Iteration 7 Progress: 17%
Phase 2 Subphase 1 Iteration 8 Progress: 17%
theta -1.74 1.39 0.28
ac -0.1394 0.6914 0.0132
Phase 2 Subphase 1 Iteration 9 Progress: 17%
Phase 2 Subphase 1 Iteration 10 Progress: 17%
theta -1.803 1.490 0.301
ac -0.1410 0.7703 0.0159
theta -1.736 1.331 0.316
ac -0.1559 -0.0063 -0.2163
theta: -1.736 1.331 0.316
Start phase 2.2
Phase 2 Subphase 2 Iteration 1 Progress: 34%
Phase 2 Subphase 2 Iteration 2 Progress: 34%
Phase 2 Subphase 2 Iteration 3 Progress: 34%
Phase 2 Subphase 2 Iteration 4 Progress: 34%
Phase 2 Subphase 2 Iteration 5 Progress: 35%
Phase 2 Subphase 2 Iteration 6 Progress: 35%
Phase 2 Subphase 2 Iteration 7 Progress: 35%
Phase 2 Subphase 2 Iteration 8 Progress: 35%
Phase 2 Subphase 2 Iteration 9 Progress: 35%
Phase 2 Subphase 2 Iteration 10 Progress: 35%
theta -1.749 1.379 0.311
ac -0.00179 -0.02674 -0.17410
theta: -1.749 1.379 0.311
Start phase 2.3
Phase 2 Subphase 3 Iteration 1 Progress: 55%
Phase 2 Subphase 3 Iteration 2 Progress: 55%
Phase 2 Subphase 3 Iteration 3 Progress: 55%
Phase 2 Subphase 3 Iteration 4 Progress: 56%
Phase 2 Subphase 3 Iteration 5 Progress: 56%
Phase 2 Subphase 3 Iteration 6 Progress: 56%
Phase 2 Subphase 3 Iteration 7 Progress: 56%
Phase 2 Subphase 3 Iteration 8 Progress: 56%
Phase 2 Subphase 3 Iteration 9 Progress: 56%
Phase 2 Subphase 3 Iteration 10 Progress: 56%
theta -1.757 1.311 0.321
ac -0.204 -0.207 -0.290
theta: -1.757 1.311 0.321
Start phase 3
Estimates, standard errors and convergence t-ratios
Estimate Standard Convergence
Error t-ratio
Rate parameters:
0 Rate parameter 3.1093 ( 0.4637 )
1. eval outdegree (density) -1.7574 ( 0.2411 ) 0.0083
2. eval reciprocity 1.3108 ( 0.3888 ) 0.0261
3. eval transitive triplets 0.3214 ( 0.0692 ) 0.0700
Overall maximum convergence ratio: 0.1212
Total of 530 iteration steps.
Covariance matrix of estimates (correlations below diagonal)
0.058 -0.044 -0.010
-0.473 0.151 -0.004
-0.613 -0.148 0.005
Derivative matrix of expected statistics X by parameters:
63.686 34.523 224.692
23.901 25.757 102.238
168.066 99.844 875.403
Covariance matrix of X (correlations below diagonal):
71.452 40.771 270.699
0.700 47.445 185.353
0.852 0.716 1411.229
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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