Functions to assess the relative importance of effects at observation moments
The function sienaRI returns the relative importance of effects
of a SAOM according to the measure of relative importance described in
Section 3.1 of Indlekofer and Brandes (2013).
The measure is based on the influence of effects on potential tie change or
behavior change decisions of individual actors at the given observation
moments.
It takes the data as well as the complete model specification into account.
Therefore, necessary arguments are the analysed data given as a siena
data object as well as the complete model specification represented either
by an estimated sienaFit object or by the triple consisting of a
suitable parameter vector theta and the corresponding
sienaAlgorithm and sienaEffects objects.
Entropy-based effect sizes as in Snijders (2004) and the within-ego
standard deviations of change statistics are also computed.
If getChangeStats=TRUE, the arrays of change statistics are stored
in the sienaRI object.
sienaRI(data, ans=NULL, theta=NULL, algorithm=NULL, effects=NULL,
getChangeStats=FALSE)
## S3 method for class 'sienaRI'
print(x, printSigma=FALSE,...)
## S3 method for class 'sienaRI'
plot(x, actors = NULL, col = NULL, addPieChart = FALSE,
radius = 1, width = NULL, height = NULL, legend = TRUE,
legendColumns = NULL, legendHeight = NULL,
cex.legend = NULL, cex.names = NULL,...)data |
|
ans |
|
theta |
Vector of parameter values of effects included in the model.
Length of |
algorithm |
|
effects |
|
getChangeStats |
Boolean: If |
x |
|
printSigma |
Boolean: If |
actors |
vector of integers: set of actors to be included in the plot;
if |
col |
Colors used in the plot. If |
addPieChart |
Boolean: If |
radius |
Radius of pie charts. Only effective if |
width |
Width of the plot. If |
height |
Height of the plot. If |
legend |
Boolean: if |
legendColumns |
Number of columns in legend. If |
legendHeight |
Height of legend. If |
cex.legend |
Specifies the relative font size of legend labels. |
cex.names |
Specifies the relative font size of bar graph labels. |
... |
Other arguments. |
sienaRI takes the data as well as the complete model specification
into account. Therefore, necessary arguments are the analyzed data given as a
siena data object as well as the complete model specification represented
either by an estimated sienaFit object or by the triple consisting of a
suitable parameter vector theta and the corresponding
sienaAlgorithm and sienaEffects objects.
A sienaFit object contains all necessary information on the model
specification, in particular, the vector of parameter values ans$theta,
the used algorithm for estimation ans$x, and information on included
model effects ans$effects.
If ans is a valid sienaFit object, the calculations of relative
importances are based on ans$theta, ans$x, and
ans$effects. Alternatively, the necessary information can be given
directly as a suitable parameter vector theta, a sienaAlgorithm
object, and a sienaEffects object. In this case, ans has to be
unspecified, i.e., ans=NULL.
Note that sienaRI works only with Method of Moments
(i.e., for sienaAlgorithm objects with maxlike = FALSE)
and that it does not yet work for endowment or creation effects
(i.e., included effects have to be of type eval),
and also not for models with interaction effects.
It does not allow two-mode (bipartite) networks as dependent variables;
but these can be represented as one-mode networks using structural zeros.
If the network is non-directed, the relative importances and entropy-based
'degrees of certainty' are calculated for modelType=2
('forcing'; see sienaAlgorithmCreate).
If there are any missing tie values in the network data set, they are imputed by
initial zeros and Last Observation Carried Forward. Structural zeros and ones
are replaced by NA and treated as impossible choices in the probability
vectors and ignored in the standard deviations; but the change statistics
for these dyads still are given in changeStatistics (if requested).
If the model contains only one dependent variable, sienaRI returns
an object of class sienaRI. Otherwise, it returns a list of objects
of class sienaRI, each corresponding to one dependent variable.
A returned sienaRI object stores the expected relative importances of
effects of one dependent variable at observation moments as defined in
Section 3.1 of Indlekofer and Brandes (2013).
A sienaRI object is a list with the following components.
For the components referred to as lists themselves, these are lists
corresponding to the observation moments.
dependentVariable
the name of the corresponding dependent variable.
effectNames
the names of considered effects.
RIActors
a list that contains the expected relative importances of effects for each potential actor decision at observation moments. This is equation (3) in Indlekofer and Brandes (2013).
expectedRI
a list that contains the expected
relative importances of effects aggregated over all actors for each
network observation. These are the averages of the actor related
values in RIActors.
This is equation (4) in Indlekofer and Brandes (2013).
IActors
a list that contains the expected importances of effects for each potential actor decision at observation moments. This is the numerator of equation (3) in Indlekofer and Brandes (2013).
expectedI
is a list that contains the expected
importances of effects
aggregated over all actors in each observation.
More precisely, it contains the averages of the actor related values
in IActors.
absoluteSumActors
a list that contains the sum of the (unstandardized) L1-differences calculated for each potential actor decision at observation moments. This is the denominator of equation (3) in Indlekofer and Brandes (2013).
RHActors
a list that contains the degree of certainty in
the potential ministep taken by an actor at the observation moments;
this is R_H(i,x) of formula (6) in Snijders (2004).
The mean over actors of these degrees of certainty, given by
formula (7) in Snijders (2004), is printed by the
print method for sienaRI objects.
sigma
a list of effects by ego matrices of the values of the within-ego
standard deviations of the change statistics. Their averages (over egos)
are printed if printSigma=TRUE.
changeStatistics
a list of arrays (effects by alters by egos)
containing for each observation moment,
the values of the change statistics for toggling the tie from actor
to ego; this is produced only if getChangeStats=TRUE.
Natalie Indlekofer, some additions by Tom Snijders
Indlekofer, Natalie and Brandes, Ulrik (2013). Relative Importance of Effects in Stochastic Actor-oriented Models. Network Science, 1 (3), 278-304.
Snijders, Tom A.B. (2004). Explained Variation in Dynamic Network Models. Mathematics and Social Sciences, 168 (4), 31-41.
myalgorithm <- sienaAlgorithmCreate(nsub=1, n3=50, projname=NULL)
mynet1 <- sienaDependent(array(c(tmp3, tmp4), dim=c(32, 32, 2)))
mydata <- sienaDataCreate(mynet1)
myeff <- getEffects(mydata)
myeff <- includeEffects(myeff, density, recip, transTies, nbrDist2)
ans <- siena07(myalgorithm, data=mydata, effects=myeff, batch=TRUE)
RI <- sienaRI(mydata, ans)
RI
## Not run:
plot(RI, addPieChart=TRUE)
plot(RI, actors=1:20, addPieChart=TRUE, radius=1.08)
## End(Not run)
myalgorithm <- sienaAlgorithmCreate(nsub=1, n3=50, projname=NULL)
mynet2 <- sienaDependent(array(c(s502, s503), dim=c(50, 50, 2)))
mybeh <- sienaDependent(s50a[,2:3], type="behavior")
mydata2 <- sienaDataCreate(mynet2, mybeh)
myeff2 <- getEffects(mydata2)
myeff2 <- includeEffects(myeff2, density, recip, transTies)
ans2 <- siena07(myalgorithm, data=mydata2, effects=myeff2, batch=TRUE)
# Use only the parameters for the evaluation function:
theta.eval <- ans2$theta[myeff2$type[myeff2$include]=="eval"]
RI <- sienaRI(mydata2, theta=theta.eval, algorithm=myalgorithm,
effects = myeff2)
RI
## Not run:
plot(RI[[2]], col = c("red", "green"), legend=FALSE)
plot(RI[[1]], addPieChart = TRUE, legendColumns=2)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.