

-----------------------------------
New Analysis started.
Date and time: 28/10/2025 14:34:43
New results follow.
-----------------------------------

RSiena version 1.5.6 (28 okt 25)


@1
Estimation by stochastic approximation algorithm.
=================================================

Random initialization of random number stream.
Current random number seed is 209429.
Effects object used: mymodel 
Model Type:
 Standard actor-oriented model 
Estimation method: unconditional moment estimation
.

Time duration for simulations is 1.0.
Standard errors are estimated with the likelihood ratio method.
Dolby method (regression on scores) is used.
Initial value of gain parameter is  0.2000000.
Reduction factor for gain parameter is  0.5000000.
Number of subphases in Phase 2 is 2.

Initial parameter values are 
  1. rate:  basic rate parameter mynet1                     4.8094
  2. eval:  outdegree (density)                            -0.5604
  3. eval:  reciprocity                                     0.0000
  4. eval:  transitive ties                                 0.0000
  5. eval:  number of actors at dist 2                      0.0000


Values of target statistics are
  1. Amount of network change                                            51.0000
  2. Number of ties                                                     129.0000
  3. Number of reciprocated ties                                         58.0000
  4. Number of ties with transitive closure                             107.0000
  5. Number of directed dists equal to 2                                199.0000
These were calculated from the data.

 5 parameters, 5 statistics

Estimation of derivatives by the LR method (type 1).


@2
End of stochastic approximation algorithm, phase 3.
---------------------------------------------------

Total of 534 iterations.
Parameter estimates based on 484 iterations,
convergence diagnostics, covariance and derivative matrices based on 50 iterations.

Information for convergence diagnosis.
Averages, standard deviations, and t-ratios for deviations from targets:
  1.  -0.7000   6.5691  -0.1066 
  2.  -0.5000   9.6622  -0.0517 
  3.   0.0800   6.6757   0.0120 
  4.   0.2800  11.3624   0.0246 
  5.   0.3400  28.6247   0.0119 

Good convergence is indicated by the t-ratios being close to zero.

Overall maximum convergence ratio =  0.2418 .
(Since the diagnostic checks now are based only on 50 iterations,
they are not reliable.)



@2
Estimation Results.
-------------------

Regular end of estimation algorithm.
Total of 534 iteration steps.


@3
Estimates and standard errors
                             
 1. rate:  basic rate parameter mynet1                             3.2504  (   0.6727)
 2. eval:  outdegree (density)                                    -2.2872  (   0.7246)
 3. eval:  reciprocity                                             2.1006  (   0.4807)
 4. eval:  transitive ties                                         1.5779  (   0.6422)
 5. eval:  number of actors at dist 2                             -0.2623  (   0.0887)


@3
Covariance matrices
                   
Covariance matrix of estimates (correlations below diagonal):
     0.453     -0.261      0.112      0.214     -0.041
    -0.535      0.525     -0.146     -0.455      0.021
     0.347     -0.419      0.231      0.100     -0.018
     0.496     -0.977      0.323      0.412     -0.018
    -0.683      0.324     -0.415     -0.319      0.008

Derivative matrix of expected statistics X by parameters and
covariance/correlation matrix of X can be found using
summary(ans) within R, or by using the 'verbose' option in Siena07.
 
Total computation time 0.68 seconds.


-----------------------------------
New Analysis started.
Date and time: 28/10/2025 14:34:44
New results follow.
-----------------------------------

RSiena version 1.5.6 (28 okt 25)


@1
Estimation by stochastic approximation algorithm.
=================================================

Random initialization of random number stream.
Current random number seed is 119015.
Effects object used: mymodel 
Model Type:
 Standard actor-oriented model 
Estimation method: conditional moment estimation.
Conditioning variable is the total number of observed changes ("distance") 
in the network variable.
Distance for simulations is   51 .
Standard errors are estimated with the likelihood ratio method.
Dolby method (regression on scores) is used.
Initial value of gain parameter is  0.2000000.
Reduction factor for gain parameter is  0.5000000.
Number of subphases in Phase 2 is 2.

Initial parameter values are 
  0. Rate parameter                           4.8094
  1. eval:  outdegree (density)                            -0.5604
  2. eval:  reciprocity                                     0.0000
  3. eval:  transitive ties                                 0.0000
  4. eval:  number of actors at dist 2                      0.0000


Values of target statistics are
  1. Number of ties                                                     129.0000
  2. Number of reciprocated ties                                         58.0000
  3. Number of ties with transitive closure                             107.0000
  4. Number of directed dists equal to 2                                199.0000
These were calculated from the data.

 4 parameters, 4 statistics

Estimation of derivatives by the LR method (type 1).


@2
End of stochastic approximation algorithm, phase 3.
---------------------------------------------------

Total of 374 iterations.
Parameter estimates based on 324 iterations,
basic rate parameter as well as 
convergence diagnostics, covariance and derivative matrices based on 50 iterations.

Information for convergence diagnosis.
Averages, standard deviations, and t-ratios for deviations from targets:
  1.  -0.8000   8.1014  -0.0987 
  2.   0.0400   5.1188   0.0078 
  3.  -0.0400   9.0372  -0.0044 
  4.  -0.7000  27.3468  -0.0256 

Good convergence is indicated by the t-ratios being close to zero.

Overall maximum convergence ratio =  0.2806 .
(Since the diagnostic checks now are based only on 50 iterations,
they are not reliable.)



@2
Estimation Results.
-------------------

Regular end of estimation algorithm.
Total of 374 iteration steps.


@3
Estimates and standard errors
                             
Rate parameters:
 0. Rate parameter                              3.26  (   0.4945)

Other parameters:
 1. eval:  outdegree (density)                                    -2.3679  (   0.8678)
 2. eval:  reciprocity                                             2.1944  (   0.7786)
 3. eval:  transitive ties                                         1.6337  (   1.0967)
 4. eval:  number of actors at dist 2                             -0.2673  (   0.1343)


@3
Covariance matrices
                   
Covariance matrix of estimates (correlations below diagonal):
     0.753      0.246     -0.872     -0.008
     0.365      0.606     -0.551      0.048
    -0.916     -0.645      1.203     -0.038
    -0.070      0.460     -0.255      0.018

Derivative matrix of expected statistics X by parameters and
covariance/correlation matrix of X can be found using
summary(ans) within R, or by using the 'verbose' option in Siena07.
 
Total computation time 0.34 seconds.


-----------------------------------
New Analysis started.
Date and time: 28/10/2025 14:34:45
New results follow.
-----------------------------------

RSiena version 1.5.6 (28 okt 25)


@1
Estimation by stochastic approximation algorithm.
=================================================

Random initialization of random number stream.
Current random number seed is 999543.
Effects object used: effects 
Model Type:
 Standard actor-oriented model 
Estimation method: conditional moment estimation.
Conditioning variable is the total number of observed changes ("distance") 
in the network variable.
Distance for simulations is   51 .
Standard errors are estimated with the likelihood ratio method.
Dolby method (regression on scores) is used.
Initial value of gain parameter is  0.2000000.
Reduction factor for gain parameter is  0.5000000.
Number of subphases in Phase 2 is 0.

Initial parameter values are 
  0. Rate parameter                           3.2600
  1. eval:  outdegree (density)                            -2.3679
  2. eval:  reciprocity                                     2.1944
  3. eval:  transitive ties                                 1.6337
  4. eval:  number of actors at dist 2                     -0.2673


Values of target statistics are
  1. Number of ties                                                     129.0000
  2. Number of reciprocated ties                                         58.0000
  3. Number of ties with transitive closure                             107.0000
  4. Number of directed dists equal to 2                                199.0000
These were calculated from the data.

 4 parameters, 4 statistics

Number of subphases is specified as 0.
Therefore the estimation phase is skipped
 and the program passes on immediately to phase 3
 for checking the current parameter values and calculating standard errors.
Estimation of derivatives by the LR method (type 1).


@2
End of stochastic approximation algorithm, phase 3.
---------------------------------------------------

Total of 50 iterations.
Parameter estimates based on 0 iterations,
basic rate parameter as well as 
convergence diagnostics, covariance and derivative matrices based on 50 iterations.

Information for convergence diagnosis.
Averages, standard deviations, and t-ratios for deviations from targets:
  1.   0.0400   7.7353   0.0052 
  2.   0.0000   6.7612   0.0000 
  3.   1.0200   9.8571   0.1035 
  4.  -0.6800  28.2831  -0.0240 

Good convergence is indicated by the t-ratios being close to zero.

Overall maximum convergence ratio =  0.2956 .
(Since the diagnostic checks now are based only on 50 iterations,
they are not reliable.)



@2
Estimation Results.
-------------------

Regular end of estimation algorithm.
Total of 50 iteration steps.


@3
Estimates and standard errors
                             
Rate parameters:
 0. Rate parameter                            3.3389  (   0.5185)

Other parameters:
 1. eval:  outdegree (density)                                    -2.3679  (   0.6727)
 2. eval:  reciprocity                                             2.1944  (   0.5188)
 3. eval:  transitive ties                                         1.6337  (   0.7113)
 4. eval:  number of actors at dist 2                             -0.2673  (   0.0739)


@3
Covariance matrices
                   
Covariance matrix of estimates (correlations below diagonal):
     0.453      0.032     -0.464     -0.013
     0.091      0.269     -0.044     -0.023
    -0.970     -0.119      0.506      0.010
    -0.265     -0.599      0.183      0.005

Derivative matrix of expected statistics X by parameters and
covariance/correlation matrix of X can be found using
summary(ans) within R, or by using the 'verbose' option in Siena07.
 
Total computation time 0.31 seconds.


-----------------------------------
New Analysis started.
Date and time: 28/10/2025 14:34:46
New results follow.
-----------------------------------

RSiena version 1.5.6 (28 okt 25)


@1
Estimation by stochastic approximation algorithm.
=================================================

Random initialization of random number stream.
Current random number seed is 403840.
Effects object used: effects 
Model Type:
 Standard actor-oriented model 
Estimation method: conditional moment estimation.
Conditioning variable is the total number of observed changes ("distance") 
in the network variable.
Distance for simulations is   51 .
Standard errors are estimated with the likelihood ratio method.
Dolby method (regression on scores) is used.
Initial value of gain parameter is  0.2000000.
Reduction factor for gain parameter is  0.5000000.
Number of subphases in Phase 2 is 0.

Initial parameter values are 
  0. Rate parameter                           3.2600
  1. eval:  outdegree (density)                            -2.3679
  2. eval:  reciprocity                                     2.1944
  3. eval:  transitive ties                                 1.6337
  4. eval:  number of actors at dist 2                     -0.2673


Values of target statistics are
  1. Number of ties                                                     129.0000
  2. Number of reciprocated ties                                         58.0000
  3. Number of ties with transitive closure                             107.0000
  4. Number of directed dists equal to 2                                199.0000
These were calculated from the data.

 4 parameters, 4 statistics

Number of subphases is specified as 0.
Therefore the estimation phase is skipped
 and the program passes on immediately to phase 3
 for checking the current parameter values and calculating standard errors.
Estimation of derivatives by the LR method (type 1).


@2
End of stochastic approximation algorithm, phase 3.
---------------------------------------------------

Total of 50 iterations.
Parameter estimates based on 0 iterations,
basic rate parameter as well as 
convergence diagnostics, covariance and derivative matrices based on 50 iterations.

Information for convergence diagnosis.
Averages, standard deviations, and t-ratios for deviations from targets:
  1.  -1.0800   8.6869  -0.1243 
  2.  -0.3200   6.8614  -0.0466 
  3.  -0.6800  10.4928  -0.0648 
  4.  -2.3600  26.1024  -0.0904 

Good convergence is indicated by the t-ratios being close to zero.

Overall maximum convergence ratio =  0.1788 .
(Since the diagnostic checks now are based only on 50 iterations,
they are not reliable.)



@2
Estimation Results.
-------------------

Regular end of estimation algorithm.
Total of 50 iteration steps.


@3
Estimates and standard errors
                             
Rate parameters:
 0. Rate parameter                             3.319  (   0.5098)

Other parameters:
 1. eval:  outdegree (density)                                    -2.3679  (   0.4827)
 2. eval:  reciprocity                                             2.1944  (   0.6516)
 3. eval:  transitive ties                                         1.6337  (   0.4519)
 4. eval:  number of actors at dist 2                             -0.2673  (   0.1017)


@3
Covariance matrices
                   
Covariance matrix of estimates (correlations below diagonal):
     0.233     -0.084     -0.206     -0.004
    -0.268      0.425      0.033     -0.040
    -0.944      0.111      0.204      0.005
    -0.084     -0.604      0.115      0.010

Derivative matrix of expected statistics X by parameters and
covariance/correlation matrix of X can be found using
summary(ans) within R, or by using the 'verbose' option in Siena07.
 
Total computation time 0.22 seconds.


-----------------------------------
New Analysis started.
Date and time: 28/10/2025 14:34:47
New results follow.
-----------------------------------

RSiena version 1.5.6 (28 okt 25)


@1
Estimation by stochastic approximation algorithm.
=================================================

Random initialization of random number stream.
Current random number seed is 253655.
Effects object used: mymodel2 
Model Type:
 Standard actor-oriented model 
Behavioral Model Type:
 Standard behavior actor-oriented model ('restrict')
Estimation method: unconditional moment estimation
.

Time duration for simulations in each period is 1.0.
Standard errors are estimated with the likelihood ratio method.
Dolby method (regression on scores) is used.
Initial value of gain parameter is  0.2000000.
Reduction factor for gain parameter is  0.5000000.
Number of subphases in Phase 2 is 2.

Initial parameter values are 
  1. rate:  constant mynet2 rate (period 1)                 4.6960
  2. rate:  constant mynet2 rate (period 2)                 4.3288
  3. eval:  outdegree (density)                            -1.4677
  4. eval:  reciprocity                                     0.0000
  5. eval:  transitive triplets                             0.0000
  6. eval:  transitive ties                                 0.0000
  7. eval:  number of actors at dist 2                      0.0000
  8. rate:  rate mybeh (period 1)                           0.7057
  9. rate:  rate mybeh (period 2)                           0.8494
 10. eval:  mybeh linear shape                              0.3224
 11. eval:  mybeh quadratic shape                           0.0000


Values of target statistics are
  1. Amount of network change in period 1                               115.0000
  2. Amount of network change in period 2                               106.0000
  3. Number of ties                                                     238.0000
  4. Number of reciprocated ties                                        160.0000
  5. Number of transitive triplets                                      225.0000
  6. Number of ties with transitive closure                             154.0000
  7. Number of directed dists equal to 2                                239.0000
  8. Amount of behavioral change in period 1 on mybeh                    27.0000
  9. Amount of behavioral change in period 2 on mybeh                    33.0000
 10. mybeh centered sum                                                  11.6667
 11. mybeh sum of cent. squares                                         121.0711
These were calculated from the data.

 11 parameters, 11 statistics

Estimation of derivatives by the LR method (type 1).


@2
End of stochastic approximation algorithm, phase 3.
---------------------------------------------------

Total of 658 iterations.
Parameter estimates based on 608 iterations,
convergence diagnostics, covariance and derivative matrices based on 50 iterations.

Information for convergence diagnosis.
Averages, standard deviations, and t-ratios for deviations from targets:
  1.  -1.6400   9.8494  -0.1665 
  2.  -1.8600   8.7435  -0.2127 
  3.   0.1800  16.4549   0.0109 
  4.   1.2000  15.3862   0.0780 
  5.   1.9200  42.6317   0.0450 
  6.   1.4800  20.4523   0.0724 
  7.  -1.2600  34.6126  -0.0364 
  8.   1.1000   5.2382   0.2100 
  9.  -0.0800   4.5349  -0.0176 
 10.   1.9000   8.0970   0.2347 
 11.  -1.7707  12.5682  -0.1409 

Good convergence is indicated by the t-ratios being close to zero.

Overall maximum convergence ratio =  0.5132 .
(Since the diagnostic checks now are based only on 50 iterations,
they are not reliable.)



@2
Estimation Results.
-------------------

Regular end of estimation algorithm.
Total of 658 iteration steps.


@3
Estimates and standard errors
                             
Network Dynamics
 1. rate:  constant mynet2 rate (period 1)                         7.3880  (   2.2720)
 2. rate:  constant mynet2 rate (period 2)                         5.5204  (   0.7687)
 3. eval:  outdegree (density)                                    -2.0080  (   0.2559)
 4. eval:  reciprocity                                             2.3983  (   0.4471)
 5. eval:  transitive triplets                                     0.1148  (   0.1813)
 6. eval:  transitive ties                                         0.5508  (   0.3179)
 7. eval:  number of actors at dist 2                             -0.5836  (   0.1384)

Behavior Dynamics
 8. rate:  rate mybeh (period 1)                                   1.1901  (   0.4869)
 9. rate:  rate mybeh (period 2)                                   1.5763  (   0.4392)
10. eval:  mybeh linear shape                                      0.3771  (   0.1804)
11. eval:  mybeh quadratic shape                                  -0.2080  (   0.1819)


@3
Covariance matrices
                   
Covariance matrix of estimates (correlations below diagonal):
     5.162     -1.008      0.415     -0.738      0.351     -0.601      0.079     -0.516     -0.055      0.028     -0.163
    -0.577      0.591     -0.091      0.172     -0.061      0.139     -0.040      0.171     -0.051      0.008      0.064
     0.714     -0.464      0.065     -0.100      0.021     -0.047     -0.001     -0.071      0.007     -0.010     -0.024
    -0.727      0.501     -0.870      0.200     -0.038      0.079     -0.019      0.155     -0.005      0.003      0.058
     0.851     -0.434      0.453     -0.467      0.033     -0.052      0.006     -0.024     -0.020      0.006     -0.003
    -0.832      0.569     -0.579      0.556     -0.895      0.101     -0.017      0.069      0.034     -0.011      0.013
     0.253     -0.374     -0.030     -0.306      0.253     -0.394      0.019     -0.029     -0.007      0.008     -0.010
    -0.467      0.458     -0.569      0.711     -0.267      0.448     -0.436      0.237      0.037     -0.026      0.061
    -0.055     -0.150      0.067     -0.026     -0.254      0.244     -0.108      0.172      0.193     -0.040     -0.003
     0.068      0.060     -0.221      0.040      0.183     -0.183      0.337     -0.292     -0.511      0.033     -0.001
    -0.395      0.458     -0.526      0.709     -0.081      0.220     -0.415      0.689     -0.035     -0.042      0.033

Derivative matrix of expected statistics X by parameters and
covariance/correlation matrix of X can be found using
summary(ans) within R, or by using the 'verbose' option in Siena07.
 
Total computation time 3.34 seconds.


-----------------------------------
New Analysis started.
Date and time: 28/10/2025 14:34:50
New results follow.
-----------------------------------

RSiena version 1.5.6 (28 okt 25)


@1
Estimation by stochastic approximation algorithm.
=================================================

Random initialization of random number stream.
Current random number seed is 895521.
Effects object used: effects 
Model Type:
 Standard actor-oriented model 
Behavioral Model Type:
 Standard behavior actor-oriented model ('restrict')
Estimation method: unconditional moment estimation
.

Time duration for simulations in each period is 1.0.
Standard errors are estimated with the likelihood ratio method.
Dolby method (regression on scores) is used.
Initial value of gain parameter is  0.2000000.
Reduction factor for gain parameter is  0.5000000.
Number of subphases in Phase 2 is 0.

Initial parameter values are 
  1. rate:  constant mynet2 rate (period 1)                 7.3880
  2. rate:  constant mynet2 rate (period 2)                 5.5204
  3. eval:  outdegree (density)                            -2.0080
  4. eval:  reciprocity                                     2.3983
  5. eval:  transitive triplets                             0.1148
  6. eval:  transitive ties                                 0.5508
  7. eval:  number of actors at dist 2                     -0.5836
  8. rate:  rate mybeh (period 1)                           1.1901
  9. rate:  rate mybeh (period 2)                           1.5763
 10. eval:  mybeh linear shape                              0.3771
 11. eval:  mybeh quadratic shape                          -0.2080


Values of target statistics are
  1. Amount of network change in period 1                               115.0000
  2. Amount of network change in period 2                               106.0000
  3. Number of ties                                                     238.0000
  4. Number of reciprocated ties                                        160.0000
  5. Number of transitive triplets                                      225.0000
  6. Number of ties with transitive closure                             154.0000
  7. Number of directed dists equal to 2                                239.0000
  8. Amount of behavioral change in period 1 on mybeh                    27.0000
  9. Amount of behavioral change in period 2 on mybeh                    33.0000
 10. mybeh centered sum                                                  11.6667
 11. mybeh sum of cent. squares                                         121.0711
These were calculated from the data.

 11 parameters, 11 statistics

Number of subphases is specified as 0.
Therefore the estimation phase is skipped
 and the program passes on immediately to phase 3
 for checking the current parameter values and calculating standard errors.
Estimation of derivatives by the LR method (type 1).


@2
End of stochastic approximation algorithm, phase 3.
---------------------------------------------------

Total of 50 iterations.
Parameter estimates based on 0 iterations,
convergence diagnostics, covariance and derivative matrices based on 50 iterations.

Information for convergence diagnosis.
Averages, standard deviations, and t-ratios for deviations from targets:
  1.  -0.5400   8.7089  -0.0620 
  2.  -0.3800   8.1788  -0.0465 
  3.   0.7200  19.0306   0.0378 
  4.   1.6400  20.3548   0.0806 
  5.   3.1600  51.2283   0.0617 
  6.   1.9400  24.8928   0.0779 
  7.   0.5800  35.3969   0.0164 
  8.   0.2200   4.1517   0.0530 
  9.  -0.9400   4.1078  -0.2288 
 10.  -0.5600   7.2960  -0.0768 
 11.  -2.5931   9.8184  -0.2641 

Good convergence is indicated by the t-ratios being close to zero.

Overall maximum convergence ratio =  0.4082 .
(Since the diagnostic checks now are based only on 50 iterations,
they are not reliable.)



@2
Estimation Results.
-------------------

Regular end of estimation algorithm.
Total of 50 iteration steps.


@3
Estimates and standard errors
                             
Network Dynamics
 1. rate:  constant mynet2 rate (period 1)                         7.3880  (   4.8665)
 2. rate:  constant mynet2 rate (period 2)                         5.5204  (   1.1865)
 3. eval:  outdegree (density)                                    -2.0080  (   0.2839)
 4. eval:  reciprocity                                             2.3983  (   1.2337)
 5. eval:  transitive triplets                                     0.1148  (   0.1431)
 6. eval:  transitive ties                                         0.5508  (   0.2654)
 7. eval:  number of actors at dist 2                             -0.5836  (   0.1983)

Behavior Dynamics
 8. rate:  rate mybeh (period 1)                                   1.1901  (   0.9845)
 9. rate:  rate mybeh (period 2)                                   1.5763  (   0.9702)
10. eval:  mybeh linear shape                                      0.3771  (   0.2673)
11. eval:  mybeh quadratic shape                                  -0.2080  (   0.2193)


@3
Covariance matrices
                   
Covariance matrix of estimates (correlations below diagonal):
    23.682      2.981      1.119     -5.329      0.153      0.344      0.702      4.242      4.123     -0.491     -0.919
     0.516      1.408      0.202     -1.006      0.047      0.096      0.165      0.794      0.725      0.029     -0.141
     0.810      0.601      0.081     -0.300      0.011      0.011      0.029      0.227      0.208     -0.008     -0.046
    -0.888     -0.687     -0.856      1.522     -0.070     -0.149     -0.203     -1.134     -0.983      0.032      0.241
     0.220      0.277      0.271     -0.395      0.020     -0.010      0.009      0.032      0.017      0.017     -0.011
     0.266      0.306      0.151     -0.454     -0.275      0.070      0.030      0.120      0.087     -0.003     -0.025
     0.728      0.702      0.514     -0.831      0.318      0.561      0.039      0.156      0.140     -0.006     -0.033
     0.885      0.680      0.813     -0.934      0.228      0.460      0.801      0.969      0.801     -0.058     -0.183
     0.873      0.630      0.755     -0.821      0.125      0.338      0.727      0.839      0.941     -0.059     -0.174
    -0.377      0.091     -0.111      0.098      0.444     -0.049     -0.107     -0.222     -0.229      0.071      0.001
    -0.861     -0.541     -0.731      0.891     -0.349     -0.423     -0.768     -0.847     -0.816      0.023      0.048

Derivative matrix of expected statistics X by parameters and
covariance/correlation matrix of X can be found using
summary(ans) within R, or by using the 'verbose' option in Siena07.
 
Total computation time 0.74 seconds.


-----------------------------------
New Analysis started.
Date and time: 28/10/2025 14:34:52
New results follow.
-----------------------------------

RSiena version 1.5.6 (28 okt 25)


@1
Estimation by stochastic approximation algorithm.
=================================================

Random initialization of random number stream.
Current random number seed is 223401.
Effects object used: effects 
Model Type:
 Standard actor-oriented model 
Behavioral Model Type:
 Standard behavior actor-oriented model ('restrict')
Estimation method: unconditional moment estimation
.

Time duration for simulations in each period is 1.0.
Standard errors are estimated with the likelihood ratio method.
Dolby method (regression on scores) is used.
Initial value of gain parameter is  0.2000000.
Reduction factor for gain parameter is  0.5000000.
Number of subphases in Phase 2 is 0.

Initial parameter values are 
  1. rate:  constant mynet2 rate (period 1)                 7.3880
  2. rate:  constant mynet2 rate (period 2)                 5.5204
  3. eval:  outdegree (density)                            -2.0080
  4. eval:  reciprocity                                     2.3983
  5. eval:  transitive triplets                             0.1148
  6. eval:  transitive ties                                 0.5508
  7. eval:  number of actors at dist 2                     -0.5836
  8. rate:  rate mybeh (period 1)                           1.1901
  9. rate:  rate mybeh (period 2)                           1.5763
 10. eval:  mybeh linear shape                              0.3771
 11. eval:  mybeh quadratic shape                          -0.2080


Values of target statistics are
  1. Amount of network change in period 1                               115.0000
  2. Amount of network change in period 2                               106.0000
  3. Number of ties                                                     238.0000
  4. Number of reciprocated ties                                        160.0000
  5. Number of transitive triplets                                      225.0000
  6. Number of ties with transitive closure                             154.0000
  7. Number of directed dists equal to 2                                239.0000
  8. Amount of behavioral change in period 1 on mybeh                    27.0000
  9. Amount of behavioral change in period 2 on mybeh                    33.0000
 10. mybeh centered sum                                                  11.6667
 11. mybeh sum of cent. squares                                         121.0711
These were calculated from the data.

 11 parameters, 11 statistics

Number of subphases is specified as 0.
Therefore the estimation phase is skipped
 and the program passes on immediately to phase 3
 for checking the current parameter values and calculating standard errors.
Estimation of derivatives by the LR method (type 1).


@2
End of stochastic approximation algorithm, phase 3.
---------------------------------------------------

Total of 100 iterations.
Parameter estimates based on 0 iterations,
convergence diagnostics, covariance and derivative matrices based on 100 iterations.

Information for convergence diagnosis.
Averages, standard deviations, and t-ratios for deviations from targets:
  1.  -2.0800   9.2656  -0.2245 
  2.   0.0700   8.7412   0.0080 
  3.  -2.4100  15.3472  -0.1570 
  4.  -0.9800  14.6763  -0.0668 
  5.  -2.8600  39.9755  -0.0715 
  6.  -1.6700  20.5269  -0.0814 
  7.  -4.4200  30.9621  -0.1428 
  8.   0.2300   5.0749   0.0453 
  9.  -0.9100   4.9850  -0.1825 
 10.  -0.5000   8.7519  -0.0571 
 11.  -0.7067  12.4568  -0.0567 

Good convergence is indicated by the t-ratios being close to zero.

Overall maximum convergence ratio =  0.4153 .



@2
Estimation Results.
-------------------

Regular end of estimation algorithm.
Total of 100 iteration steps.


@3
Estimates and standard errors
                             
Network Dynamics
 1. rate:  constant mynet2 rate (period 1)                         7.3880  (   1.5566)
 2. rate:  constant mynet2 rate (period 2)                         5.5204  (   0.9153)
 3. eval:  outdegree (density)                                    -2.0080  (   0.1426)
 4. eval:  reciprocity                                             2.3983  (   0.2700)
 5. eval:  transitive triplets                                     0.1148  (   0.1195)
 6. eval:  transitive ties                                         0.5508  (   0.2024)
 7. eval:  number of actors at dist 2                             -0.5836  (   0.1297)

Behavior Dynamics
 8. rate:  rate mybeh (period 1)                                   1.1901  (   0.2679)
 9. rate:  rate mybeh (period 2)                                   1.5763  (   0.4413)
10. eval:  mybeh linear shape                                      0.3771  (   0.1427)
11. eval:  mybeh quadratic shape                                  -0.2080  (   0.1106)


@3
Covariance matrices
                   
Covariance matrix of estimates (correlations below diagonal):
     2.423     -0.202     -0.035      0.275      0.058     -0.104     -0.003     -0.229     -0.090      0.032     -0.013
    -0.142      0.838     -0.006     -0.046     -0.039      0.072      0.013      0.033     -0.102     -0.034     -0.018
    -0.159     -0.045      0.020     -0.011     -0.006      0.000     -0.011      0.003      0.008      0.004      0.005
     0.654     -0.185     -0.280      0.073      0.009     -0.024     -0.007     -0.014     -0.001     -0.003      0.003
     0.309     -0.353     -0.379      0.266      0.014     -0.018      0.004     -0.007      0.005      0.000     -0.001
    -0.330      0.391      0.007     -0.436     -0.755      0.041      0.001      0.014     -0.004     -0.001     -0.002
    -0.016      0.114     -0.588     -0.212      0.278      0.026      0.017     -0.005     -0.013     -0.003     -0.007
    -0.549      0.133      0.068     -0.187     -0.233      0.260     -0.158      0.072      0.024     -0.012      0.007
    -0.130     -0.252      0.134     -0.005      0.099     -0.048     -0.229      0.199      0.195      0.009      0.026
     0.142     -0.262      0.183     -0.081     -0.005     -0.038     -0.172     -0.309      0.148      0.020      0.001
    -0.078     -0.178      0.311      0.105     -0.044     -0.099     -0.482      0.239      0.540      0.049      0.012

Derivative matrix of expected statistics X by parameters and
covariance/correlation matrix of X can be found using
summary(ans) within R, or by using the 'verbose' option in Siena07.
 
Total computation time 1.3 seconds.
