Description Usage Arguments Details Value Author(s) References See Also Examples
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  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<1),
lessMem=FALSE)

fn 
Function to do one simulation in the RobbinsMonro algorithm. Not to be touched. 
projname 
Character string name of project; the output file will be called projname.out. No embedded spaces!!! 
MaxDegree 
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. 
Offset 
Named vector of offset values for symmetric networks with

useStdInits 
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. 
n3 
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. 
nsub 
Number of subphases in phase 2. 
n2start 
Minimum number of interations in subphase 1 of phase 2;
default is 
dolby 
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. 
maxlike 
Whether to use maximum likelihood method or Method of Moments estimation. 
diagonalize 
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 RobbinsMonro 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. 
condvarno 
If 
condname 
If conditional, the name of the dependent variable on
which to condition. Use one or other of 
firstg 
Initial value of scaling ("gain") parameter for updates in the RobbinsMonro procedure. 
reduceg 
Reduction factor for scaling ("gain") parameter for updates in the RobbinsMonro procedure (MoM only). 
cond 
Boolean. Only relevant for Method of Moments
simulation/estimation.
If TRUE, use conditional simulation; if FALSE, unconditional simulation.
If missing, decision is deferred until 
findiff 
Boolean: If TRUE, estimate derivatives using finite differences. If FALSE, use scores. 
seed 
Integer. Starting value of random seed. Not used if parallel testing. 
pridg 
Real number. Probability used in MetropolisHastings routine in ML estimation. See Siena_Algorithms.pdf. 
prcdg 
Real number. Probability used in MetropolisHastings routine in ML estimation. See Siena_Algorithms.pdf. 
prper 
Real number. Probability used in MetropolisHastings routine in ML estimation. See Siena_Algorithms.pdf. 
pripr 
Real number. Probability used in MetropolisHastings routine in ML estimation. See Siena_Algorithms.pdf. 
prdpr 
Real number. Probability used in MetropolisHastings routine in ML estimation. See Siena_Algorithms.pdf. 
prirms 
Real number. Probability used in MetropolisHastings routine in ML estimation. See Siena_Algorithms.pdf. 
prdrms 
Real number. Probability used in MetropolisHastings routine in ML estimation. See Siena_Algorithms.pdf. 
maximumPermutationLength 
Maximum length of permutation in steps in ML estimation. 
minimumPermutationLength 
Minimum length of permutation in steps in ML estimation. 
initialPermutationLength 
Initial length of permutation in steps in ML estimation. 
modelType 
Named vector indicating the type of model to be fitted for
dependent network variables. Possible values are: 
behModelType 
Named vector indicating the type of model to be fitted for
behavioral dependent variables. Possible values are: 
mult 
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 
simOnly 
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 
localML 
Logical: If TRUE, and 
truncation 
Used for step truncation in the Robbins Monro algorithm (applied to deviate/(standard deviation)). 
doubleAveraging 
subphase after which double averaging is used in the Robbins Monro algorithm, which probably increases algorithm efficiency. 
standardizeVar 
Logical: whether to limit deviations used in RobbinsMonro updates to unit variances. 
lessMem 
Logical: whether to reduce storage during operation of

Model specification is done via this object for
siena07
.
This function creates an object with the elements required to control the
RobbinsMonro 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.
Returns an object of class sienaAlgorithm
containing
values implied by the parameters.
Ruth Ripley and Tom A.B. Snijders
For the model types:
Tom A. B. Snijders and Mark Pickup,
Stochastic ActorOriented Models for Network Dynamics.
In: Jennifer N. Victor, Mark Lubell and Alexander H. Montgomery,
Oxford Handbook of Political Networks. Oxford University Press, 2016.
1 2 3 4 5 6 7 8  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),
behModelType=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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