sienaRI: Functions to assess the relative importance of effects at...
In RSiena: Siena - Simulation Investigation for Empirical Network Analysis
Description
Usage
Arguments
Details
Value
Author(s)
References
Examples
Description
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.
Usage
1
2
3
4
5
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7
8
9
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,...)
Arguments
data
siena data object representing the analyzed data and
resulting from a call to sienaDataCreate.
ans
sienaFit object resulting from a call to
siena07. The 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).
theta
Vector of parameter values of effects included in the model.
Length of theta has to be equal to the number of
included effects.
algorithm
sienaAlgorithm object as resulting from a call to
sienaAlgorithmCreate. Works only for estimation
by Method of Moments (i.e., if maxlike = FALSE).
effects
sienaEffects object specifying which effects are
included the model. Note that sienaRI does not yet work
for endowment or creation effect, i.e., included effects have to
be of type eval (or rate).
getChangeStats
Boolean: If TRUE, an array of change statistics
is added to the sienaRI object.
x
sienaRI object resulting from a call to sienaRI.
printSigma
Boolean: If TRUE, average within-ego standard
deviations of change statistics ('sigma'), are included in the print.
actors
vector of integers: set of actors to be included in the plot;
if NULL, all actors.
col
Colors used in the plot. If col=NULL a default color
scheme is used.
addPieChart
Boolean: If TRUE, pie charts of aggregated relative
importances for the complete set of actors will be added to the plot.
radius
Radius of pie charts. Only effective if addPieCharts = TRUE.
width
Width of the plot. If width=NULL a default value is used.
height
Height of the plot. If height=NULL a default value is used.
legend
Boolean: if TRUE a legend is added to the plot.
x$effectNames are used as labels.
legendColumns
Number of columns in legend. If legendColumns=NULL
a default value is used. Only effective if legend=TRUE.
legendHeight
Height of legend. If legendHeight=NULL a default
value is used. Only effective if legend=TRUE.
cex.legend
Specifies the relative font size of legend labels.
cex.names
Specifies the relative font size of bar graph labels.
...
Other arguments.
Details
sienaRI takes the data as well as the complete model specification
into account. Therefore, necessary arguments are the analyzed data given as a
siena data object as well as the complete model specification represented
either by an estimated sienaFit object or by the triple consisting of a
suitable parameter vector theta and the corresponding
sienaAlgorithm and sienaEffects objects.
A sienaFit object contains all necessary information on the model
specification, in particular, the vector of parameter values ans$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).
Value
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.
Author(s)
Natalie Indlekofer, some additions by Tom Snijders
References
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.
Examples
1
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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
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)
Example output
Warning message:
no DISPLAY variable so Tk is not available
siena07 will create an output file /work/tmp/tmp/RtmpH1ueQx/Siena3698a74769b390.txt .
This is a temporary file for this R session.
effectName include fix test initialValue parm
1 outdegree (density) TRUE FALSE FALSE -0.56039 0
2 reciprocity TRUE FALSE FALSE 0.00000 0
3 transitive ties TRUE FALSE FALSE 0.00000 0
4 number of actors at dist 2 TRUE FALSE FALSE 0.00000 0
Start phase 0
theta: -0.56 0.00 0.00 0.00
Start phase 1
Phase 1 Iteration 1 Progress: 0%
Phase 1 Iteration 2 Progress: 0%
Phase 1 Iteration 3 Progress: 1%
Phase 1 Iteration 4 Progress: 1%
Phase 1 Iteration 5 Progress: 1%
Phase 1 Iteration 10 Progress: 2%
Phase 1 Iteration 15 Progress: 3%
Phase 1 Iteration 20 Progress: 4%
Phase 1 Iteration 25 Progress: 5%
Phase 1 Iteration 30 Progress: 6%
Phase 1 Iteration 35 Progress: 7%
Phase 1 Iteration 40 Progress: 8%
Phase 1 Iteration 45 Progress: 9%
Phase 1 Iteration 50 Progress: 9%
theta: -0.7531 0.5537 0.2647 -0.0822
Start phase 2.1
Phase 2 Subphase 1 Iteration 1 Progress: 48%
Phase 2 Subphase 1 Iteration 2 Progress: 48%
theta -0.804 0.855 0.390 -0.131
ac -0.276 1.366 3.693 0.157
Phase 2 Subphase 1 Iteration 3 Progress: 48%
Phase 2 Subphase 1 Iteration 4 Progress: 48%
theta -1.061 1.820 0.752 -0.303
ac 0.139 1.124 1.323 0.203
Phase 2 Subphase 1 Iteration 5 Progress: 48%
Phase 2 Subphase 1 Iteration 6 Progress: 49%
theta -1.445 2.425 1.053 -0.371
ac 0.881 0.836 1.371 0.361
Phase 2 Subphase 1 Iteration 7 Progress: 49%
Phase 2 Subphase 1 Iteration 8 Progress: 49%
theta -1.730 2.515 1.170 -0.358
ac 0.866 0.740 1.083 0.362
Phase 2 Subphase 1 Iteration 9 Progress: 49%
Phase 2 Subphase 1 Iteration 10 Progress: 49%
theta -2.050 2.679 1.304 -0.316
ac 0.861 0.465 1.097 0.327
theta -2.204 2.179 1.474 -0.264
ac 0.0302 -0.2485 -0.1302 -0.2562
theta: -2.204 2.179 1.474 -0.264
Start phase 3
Expected relative importance of effects for dependent variable 'mynet1' at observation moments:
wave 1 wave 2
1. eval outdegree (density) 0.3490 0.3096
2. eval reciprocity 0.2228 0.2206
3. eval transitive ties 0.1771 0.2259
4. eval number of actors at dist 2 0.2511 0.2439
Expected importance of effects for this dependent variable:
1. eval outdegree (density) 0.8611 0.7320
2. eval reciprocity 0.5441 0.5303
3. eval transitive ties 0.5184 0.6259
4. eval number of actors at dist 2 0.5891 0.5081
R_H ('degree of certainty') 0.2726 0.2483
siena07 will create an output file /work/tmp/tmp/RtmpH1ueQx/Siena3698a72b52b6c1.txt .
This is a temporary file for this R session.
effectName include fix test initialValue parm
1 outdegree (density) TRUE FALSE FALSE -1.44421 0
2 reciprocity TRUE FALSE FALSE 0.00000 0
3 transitive ties TRUE FALSE FALSE 0.00000 0
Start phase 0
theta: 4.329 -1.444 0.000 0.000 0.849 0.316 0.000
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: 1%
Phase 1 Iteration 5 Progress: 1%
Phase 1 Iteration 10 Progress: 1%
Phase 1 Iteration 15 Progress: 2%
Phase 1 Iteration 20 Progress: 3%
Phase 1 Iteration 25 Progress: 4%
Phase 1 Iteration 30 Progress: 4%
Phase 1 Iteration 35 Progress: 5%
Phase 1 Iteration 40 Progress: 6%
Phase 1 Iteration 45 Progress: 7%
Phase 1 Iteration 50 Progress: 7%
theta: 4.5425 -1.7103 0.7776 0.3807 0.9140 0.1854 -0.0819
Start phase 2.1
Phase 2 Subphase 1 Iteration 1 Progress: 59%
Phase 2 Subphase 1 Iteration 2 Progress: 59%
theta 4.607 -1.875 1.354 0.663 1.057 0.132 -0.141
ac 1.791 0.488 1.705 2.282 1.257 1.106 0.647
Phase 2 Subphase 1 Iteration 3 Progress: 59%
Phase 2 Subphase 1 Iteration 4 Progress: 59%
theta 4.6518 -2.1916 2.0351 0.7737 1.4830 0.0431 -0.3163
ac 0.270 1.488 1.411 0.620 1.369 1.255 0.684
Phase 2 Subphase 1 Iteration 5 Progress: 59%
Phase 2 Subphase 1 Iteration 6 Progress: 59%
theta 4.80607 -2.46159 2.49309 0.99108 1.79058 0.00464 -0.39247
ac -0.0717 1.3668 1.2873 0.5206 0.9508 1.0438 0.6731
Phase 2 Subphase 1 Iteration 7 Progress: 59%
Phase 2 Subphase 1 Iteration 8 Progress: 60%
theta 4.904 -2.608 2.514 0.973 1.747 0.201 -0.429
ac -0.045 1.433 1.325 0.657 0.979 1.121 0.484
Phase 2 Subphase 1 Iteration 9 Progress: 60%
Phase 2 Subphase 1 Iteration 10 Progress: 60%
theta 4.890 -2.722 2.531 1.051 1.733 0.316 -0.348
ac -0.0795 1.4296 1.2773 0.4994 0.8809 1.1221 0.4873
theta 6.058 -3.136 2.804 1.359 1.478 0.461 -0.452
ac -0.0404 -0.0721 -0.3706 -0.3859 0.0887 0.1223 -0.0297
theta: 6.058 -3.136 2.804 1.359 1.478 0.461 -0.452
Start phase 3
more than one dependent variable
return value is therefore not of class 'sienaRI'
but a list of objects of class 'sienaRI'.
[[1]]
Expected relative importance of effects for dependent variable 'mynet2' at observation moments:
wave 1 wave 2
1. eval outdegree (density) 0.4940 0.4638
2. eval reciprocity 0.2960 0.3412
3. eval transitive ties 0.2100 0.1950
Expected importance of effects for this dependent variable:
1. eval outdegree (density) 1.0860 1.0152
2. eval reciprocity 0.7581 0.8662
3. eval transitive ties 0.5713 0.5264
R_H ('degree of certainty') 0.4650 0.4215
[[2]]
Expected relative importance of effects for dependent variable 'mybeh' at observation moments:
wave 1 wave 2
1. eval mybeh linear shape 0.3866 0.3825
2. eval mybeh quadratic shape 0.6134 0.6175
Expected importance of effects for this dependent variable:
1. eval mybeh linear shape 0.2003 0.1953
2. eval mybeh quadratic shape 0.4107 0.4123
R_H ('degree of certainty') 0.1494 0.1547
RSiena documentation built on Sept. 24, 2020, 3 p.m.
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Description Usage Arguments Details Value Author(s) References Examples
Description
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.
Usage
1 2 3 4 5 6 7 8 9 | 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,...)
|
Arguments
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. |
Details
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).
Value
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
printmethod forsienaRIobjects.- -
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.
Author(s)
Natalie Indlekofer, some additions by Tom Snijders
References
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.
Examples
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 | 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)
|
Example output
Warning message:
no DISPLAY variable so Tk is not available
siena07 will create an output file /work/tmp/tmp/RtmpH1ueQx/Siena3698a74769b390.txt .
This is a temporary file for this R session.
effectName include fix test initialValue parm
1 outdegree (density) TRUE FALSE FALSE -0.56039 0
2 reciprocity TRUE FALSE FALSE 0.00000 0
3 transitive ties TRUE FALSE FALSE 0.00000 0
4 number of actors at dist 2 TRUE FALSE FALSE 0.00000 0
Start phase 0
theta: -0.56 0.00 0.00 0.00
Start phase 1
Phase 1 Iteration 1 Progress: 0%
Phase 1 Iteration 2 Progress: 0%
Phase 1 Iteration 3 Progress: 1%
Phase 1 Iteration 4 Progress: 1%
Phase 1 Iteration 5 Progress: 1%
Phase 1 Iteration 10 Progress: 2%
Phase 1 Iteration 15 Progress: 3%
Phase 1 Iteration 20 Progress: 4%
Phase 1 Iteration 25 Progress: 5%
Phase 1 Iteration 30 Progress: 6%
Phase 1 Iteration 35 Progress: 7%
Phase 1 Iteration 40 Progress: 8%
Phase 1 Iteration 45 Progress: 9%
Phase 1 Iteration 50 Progress: 9%
theta: -0.7531 0.5537 0.2647 -0.0822
Start phase 2.1
Phase 2 Subphase 1 Iteration 1 Progress: 48%
Phase 2 Subphase 1 Iteration 2 Progress: 48%
theta -0.804 0.855 0.390 -0.131
ac -0.276 1.366 3.693 0.157
Phase 2 Subphase 1 Iteration 3 Progress: 48%
Phase 2 Subphase 1 Iteration 4 Progress: 48%
theta -1.061 1.820 0.752 -0.303
ac 0.139 1.124 1.323 0.203
Phase 2 Subphase 1 Iteration 5 Progress: 48%
Phase 2 Subphase 1 Iteration 6 Progress: 49%
theta -1.445 2.425 1.053 -0.371
ac 0.881 0.836 1.371 0.361
Phase 2 Subphase 1 Iteration 7 Progress: 49%
Phase 2 Subphase 1 Iteration 8 Progress: 49%
theta -1.730 2.515 1.170 -0.358
ac 0.866 0.740 1.083 0.362
Phase 2 Subphase 1 Iteration 9 Progress: 49%
Phase 2 Subphase 1 Iteration 10 Progress: 49%
theta -2.050 2.679 1.304 -0.316
ac 0.861 0.465 1.097 0.327
theta -2.204 2.179 1.474 -0.264
ac 0.0302 -0.2485 -0.1302 -0.2562
theta: -2.204 2.179 1.474 -0.264
Start phase 3
Expected relative importance of effects for dependent variable 'mynet1' at observation moments:
wave 1 wave 2
1. eval outdegree (density) 0.3490 0.3096
2. eval reciprocity 0.2228 0.2206
3. eval transitive ties 0.1771 0.2259
4. eval number of actors at dist 2 0.2511 0.2439
Expected importance of effects for this dependent variable:
1. eval outdegree (density) 0.8611 0.7320
2. eval reciprocity 0.5441 0.5303
3. eval transitive ties 0.5184 0.6259
4. eval number of actors at dist 2 0.5891 0.5081
R_H ('degree of certainty') 0.2726 0.2483
siena07 will create an output file /work/tmp/tmp/RtmpH1ueQx/Siena3698a72b52b6c1.txt .
This is a temporary file for this R session.
effectName include fix test initialValue parm
1 outdegree (density) TRUE FALSE FALSE -1.44421 0
2 reciprocity TRUE FALSE FALSE 0.00000 0
3 transitive ties TRUE FALSE FALSE 0.00000 0
Start phase 0
theta: 4.329 -1.444 0.000 0.000 0.849 0.316 0.000
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: 1%
Phase 1 Iteration 5 Progress: 1%
Phase 1 Iteration 10 Progress: 1%
Phase 1 Iteration 15 Progress: 2%
Phase 1 Iteration 20 Progress: 3%
Phase 1 Iteration 25 Progress: 4%
Phase 1 Iteration 30 Progress: 4%
Phase 1 Iteration 35 Progress: 5%
Phase 1 Iteration 40 Progress: 6%
Phase 1 Iteration 45 Progress: 7%
Phase 1 Iteration 50 Progress: 7%
theta: 4.5425 -1.7103 0.7776 0.3807 0.9140 0.1854 -0.0819
Start phase 2.1
Phase 2 Subphase 1 Iteration 1 Progress: 59%
Phase 2 Subphase 1 Iteration 2 Progress: 59%
theta 4.607 -1.875 1.354 0.663 1.057 0.132 -0.141
ac 1.791 0.488 1.705 2.282 1.257 1.106 0.647
Phase 2 Subphase 1 Iteration 3 Progress: 59%
Phase 2 Subphase 1 Iteration 4 Progress: 59%
theta 4.6518 -2.1916 2.0351 0.7737 1.4830 0.0431 -0.3163
ac 0.270 1.488 1.411 0.620 1.369 1.255 0.684
Phase 2 Subphase 1 Iteration 5 Progress: 59%
Phase 2 Subphase 1 Iteration 6 Progress: 59%
theta 4.80607 -2.46159 2.49309 0.99108 1.79058 0.00464 -0.39247
ac -0.0717 1.3668 1.2873 0.5206 0.9508 1.0438 0.6731
Phase 2 Subphase 1 Iteration 7 Progress: 59%
Phase 2 Subphase 1 Iteration 8 Progress: 60%
theta 4.904 -2.608 2.514 0.973 1.747 0.201 -0.429
ac -0.045 1.433 1.325 0.657 0.979 1.121 0.484
Phase 2 Subphase 1 Iteration 9 Progress: 60%
Phase 2 Subphase 1 Iteration 10 Progress: 60%
theta 4.890 -2.722 2.531 1.051 1.733 0.316 -0.348
ac -0.0795 1.4296 1.2773 0.4994 0.8809 1.1221 0.4873
theta 6.058 -3.136 2.804 1.359 1.478 0.461 -0.452
ac -0.0404 -0.0721 -0.3706 -0.3859 0.0887 0.1223 -0.0297
theta: 6.058 -3.136 2.804 1.359 1.478 0.461 -0.452
Start phase 3
more than one dependent variable
return value is therefore not of class 'sienaRI'
but a list of objects of class 'sienaRI'.
[[1]]
Expected relative importance of effects for dependent variable 'mynet2' at observation moments:
wave 1 wave 2
1. eval outdegree (density) 0.4940 0.4638
2. eval reciprocity 0.2960 0.3412
3. eval transitive ties 0.2100 0.1950
Expected importance of effects for this dependent variable:
1. eval outdegree (density) 1.0860 1.0152
2. eval reciprocity 0.7581 0.8662
3. eval transitive ties 0.5713 0.5264
R_H ('degree of certainty') 0.4650 0.4215
[[2]]
Expected relative importance of effects for dependent variable 'mybeh' at observation moments:
wave 1 wave 2
1. eval mybeh linear shape 0.3866 0.3825
2. eval mybeh quadratic shape 0.6134 0.6175
Expected importance of effects for this dependent variable:
1. eval mybeh linear shape 0.2003 0.1953
2. eval mybeh quadratic shape 0.4107 0.4123
R_H ('degree of certainty') 0.1494 0.1547
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