Creates an object with specifications for the algorithm for parameter
estimation in RSiena.
sienaAlgorithmCreate() and sienaModelCreate()
are identical functions; the second name was
used from the start of the RSiena package, but the first name
indicates more precisely the purpose of this function.
sienaAlgorithmCreate(fn, projname = "Siena", MaxDegree = NULL, Offset = NULL,
useStdInits = FALSE, n3 = 1000, nsub = 4, n2start = NULL,
dolby=TRUE, maxlike = FALSE, diagonalize=0.2*!maxlike,
condvarno = 0, condname = "", firstg = 0.2, reduceg = 0.5,
cond = NA, findiff = FALSE, seed = NULL,
pridg=0.05, prcdg=0.05, prper=0.2, pripr=0.3, prdpr=0.3,
prirms=0.05, prdrms=0.05, maximumPermutationLength=40,
minimumPermutationLength=2, initialPermutationLength=20,
modelType=NULL, behModelType=NULL, mult=5, simOnly=FALSE, localML=FALSE,
truncation=5, doubleAveraging=0, standardizeVar=(diagonalize<1), lessmem="FALSE)sienaModelCreate(fn, projname = "Siena", MaxDegree = NULL, Offset = NULL,
useStdInits = FALSE, n3 = 1000, nsub = 4, n2start = NULL,
dolby=TRUE, maxlike = FALSE, diagonalize=0.2*!maxlike,
condvarno = 0, condname = "", firstg = 0.2, reduceg = 0.5,
cond = NA, findiff = FALSE, seed = NULL,
pridg=0.05, prcdg=0.05, prper=0.2, pripr=0.3, prdpr=0.3,
prirms=0.05, prdrms=0.05, maximumPermutationLength=40,
minimumPermutationLength=2, initialPermutationLength=20,
modelType=NULL, behModelType=NULL, mult=5, simOnly=FALSE, localML=FALSE,
truncation=5, doubleAveraging=0, standardizeVar=(diagonalize
Function to do one simulation in the Robbins-Monro algorithm. Not to be touched.
Character string name of project; the output file will be
called projname.txt. No embedded spaces!!!
If projname=NULL, output will be written to a file in the temporary
session directory, created as tempfile(Siena).
Named vector of maximum degree values for corresponding networks. Allows to restrict the model to networks with degrees not higher than this maximum. Names should be the names of all dependent network variables, in the same order as in the Siena data set. Default as well as value 0 imply no restrictions.
Named vector of offset values for symmetric networks with
modelType = 3 (M.1), and for universal setting in Settings model.
Names should be the names of all dependent network variables,
in the same order as in the Siena data set.
Default NULL implies values 0.
Boolean. If TRUE, the initial values in the effects object will be ignored and default values used instead. If FALSE, the initial values in the effects object will be used.
Number of iterations in phase 3. For regular use with the Method of Moments, n3=1000 mostly suffices. For use in publications and for Maximum Likelihood, at least n3=3000 is advised. Sometimes much higher values are required for stable estimation of standard errors.
Number of subphases in phase 2.
Minimum number of interations in subphase 1 of phase 2;
default is 2.52*(p+7),
where p = number of estimated parameters.
Boolean. Should there be noise reduction by regression on augmented data score. In most cases dolby=TRUE yields better convergence, but takes some extra computing time; if convergence is problematic, however, dolby=FALSE may be tried. Just use whatever works best.
Whether to use maximum likelihood method or Method of Moments estimation.
Number between 0 and 1 (bounds included), values outside this interval will be truncated; for diagonalize=0 the complete estimated derivative matrix will be used for updates in the Robbins-Monro procedure; for diagonalize=1 only the diagonal entries will be used; for values between 0 and 1, the weighted average will be used with weight diagonalize for the diagonalized matrix. Has no effect for ML estimation. Higher values are more stable, lower values potentially more efficient. Default: for ML estimation, diagonalize=0; for MoM estimation, diagonalize = 1.0.
If cond (conditional simulation), the
sequential number of the network
or behavior variable on which to condition.
If conditional, the name of the dependent variable on
which to condition. Use one or other of condname or
condvarno to specify the variable.
Initial value of scaling ("gain") parameter for updates in the Robbins-Monro procedure.
Reduction factor for scaling ("gain") parameter for updates in the Robbins-Monro procedure (MoM only).
Boolean. Only relevant for Method of Moments
simulation/estimation.
If TRUE, use conditional simulation; if FALSE, unconditional simulation.
If missing, decision is deferred until siena07,
when it is set to TRUE if there is only one dependent variable,
FALSE otherwise.
Boolean: If TRUE, estimate derivatives using finite differences. If FALSE, use scores.
Integer. Starting value of random seed. Not used if parallel testing.
Real number. Probability used in Metropolis-Hastings routine in ML estimation. See Siena_Algorithms.pdf.
Real number. Probability used in Metropolis-Hastings routine in ML estimation. See Siena_Algorithms.pdf.
Real number. Probability used in Metropolis-Hastings routine in ML estimation. See Siena_Algorithms.pdf.
Real number. Probability used in Metropolis-Hastings routine in ML estimation. See Siena_Algorithms.pdf.
Real number. Probability used in Metropolis-Hastings routine in ML estimation. See Siena_Algorithms.pdf.
Real number. Probability used in Metropolis-Hastings routine in ML estimation. See Siena_Algorithms.pdf.
Real number. Probability used in Metropolis-Hastings routine in ML estimation. See Siena_Algorithms.pdf.
Maximum length of permutation in steps in ML estimation.
Minimum length of permutation in steps in ML estimation.
Initial length of permutation in steps in ML estimation.
Named vector indicating the type of model to be fitted for
dependent network variables. Possible values are:
1=directed, 2:6 for symmetric networks only: 2=dictatorial forcing (D.1),
3=Initiative model with reciprocal confirmation (M.1),
4=Pairwise dictatorial forcing model (D.2),
5=Pairwise mutual model (M.2), 6=Pairwise joint model (C.2).
Names should be the names of all dependent network variables,
in the same order as in the Siena data set.
See Snijders and Pickup (2016) for the meanings of these models.
Default NULL implies 1 for directed or two-mode, 2 for symmetric.
Named vector indicating the type of model to be fitted for
behavioral dependent variables. Possible values are:
1=standard (restricted), 2=absorbing.
Names should be the names of all dependent behavioral variables,
in the same order as in the Siena data set.
Default NULL implies values 1.
Multiplication factor for maximum likelihood and Bayes. Number of
steps per iteration is set to this multiple of the total distance
between the observations at start and finish of the wave.
Decreasing mult below a certain value has no further effect.
This can be either a number (which needs to be positive) or a vector
of numbers, of length equal to the number of basic rate parameters in the
model, i.e., the number of periods times the number of dependent
variables.
Logical: If TRUE, then the calculation of the covariance
matrix and standard errors of the estimates at the end of
Phase 3 of the estimation algorithm in function siena07 is skipped.
This is suitable if nsub=0 and siena07 is used only for the
purpose of simulation.
Logical: If TRUE, and maxlike, then calculations are
sped up for models with all local effects.
Used for step truncation in the Robbins Monro algorithm (applied to deviate/(standard deviation)).
subphase after which double averaging is used in the Robbins Monro algorithm, which probably increases algorithm efficiency.
Logical: whether to limit deviations used in Robbins-Monro updates to unit variances.
Logical: whether to reduce storage during operation of
siena07, and of the object produced, by leaving out arrays
by iteration and by period of simulated statistics sf2 and scores
ssc.
if lessMem=TRUE, it will be impossible to run
sienaTimeTest or sienaGOF on
the object produced by siena07.
Returns an object of class sienaAlgorithm containing
values implied by the parameters.
Model specification is done via this object for
siena07.
This function creates an object with the elements required to control the
Robbins-Monro algorithm. Those not
available as arguments can be changed manually where desired.
Further information about the implementation of the algorithm is in
http://www.stats.ox.ac.uk/~snijders/siena/Siena_algorithms.pdf.
Some of the examples use projname=NULL; this is just for the sake of
checking the examples, not necessarily intended for normal use.
For the model types: Tom A. B. Snijders and Mark Pickup, Stochastic Actor-Oriented Models for Network Dynamics. In: Jennifer N. Victor, Mark Lubell and Alexander H. Montgomery, Oxford Handbook of Political Networks. Oxford University Press, 2016.
# NOT RUN {
myAlgorithm <- sienaAlgorithmCreate(projname="NetworkDyn")
StdAlgorithm <- sienaAlgorithmCreate(projname="NetworkDyn", useStdInits=TRUE)
CondAlgorithm <- sienaAlgorithmCreate(projname="NetworkDyn", condvarno=1, cond=TRUE)
Max10Algorithm <- sienaAlgorithmCreate(projname="NetworkDyn", MaxDegree=c(mynet=10),
modelType=c(mynet=1))
Beh2Algorithm <- sienaAlgorithmCreate(projname="NetBehDyn", behModelType=c(mybeh=2))
# where mynet is the name of the network object created by sienaDependent(),
# and mybeh the name of the behavior object created by the same function.
# }
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