Running 'parallel.R' [205s]
Running the tests in 'tests/parallel.R' failed.
Complete output:
  > library(RSiena)
  > 
  > 
  > ##test3
  > mynet1 <- sienaDependent(array(c(tmp3, tmp4),dim=c(32, 32, 2)))
  > mydata <- sienaDataCreate(mynet1)
  > myeff<- getEffects(mydata)
  > mymodel<- model.create(findiff=TRUE, fn = simstats0c,
  +                        cond=FALSE, nsub=2, n3=50, seed=3)
  If you use this algorithm object, siena07 will create/use an output file Siena.txt .
  > print('test3')
  [1] "test3"
  > ans<- siena07(mymodel, data=mydata, effects=myeff,
  +      batch=TRUE, parallelTesting=TRUE, silent=TRUE)
  > #,dll='../siena/src/RSiena.dll')
  > ans
  Estimates, standard errors and convergence t-ratios
  
                                        Estimate   Standard   Convergence 
                                                     Error      t-ratio   
    1. rate basic rate parameter mynet1  3.0186  ( 0.5581   )    0.0134   
    2. eval outdegree (density)         -1.1374  ( 0.2723   )    0.1044   
    3. eval reciprocity                  1.7459  ( 0.2854   )   -0.1847   
  
  Overall maximum convergence ratio:    0.3838 
  
  
  Total of 407 iteration steps.
  
  > (myeff <- includeEffects(myeff, transTrip, cycle4))
    effectName          include fix   test  initialValue parm
  1 transitive triplets TRUE    FALSE FALSE          0   0   
  2 4-cycles (#)        TRUE    FALSE FALSE          0   1   
    effectName                  include fix   test  initialValue parm
  1 basic rate parameter mynet1 TRUE    FALSE FALSE    4.80941   0   
  2 outdegree (density)         TRUE    FALSE FALSE   -0.56039   0   
  3 reciprocity                 TRUE    FALSE FALSE    0.00000   0   
  4 transitive triplets         TRUE    FALSE FALSE    0.00000   0   
  5 4-cycles (#)                TRUE    FALSE FALSE    0.00000   1   
  > (myeff <- includeEffects(myeff, cycle4, include=FALSE))
  [1] effectName   include      fix          test         initialValue
  [6] parm        
  <0 rows> (or 0-length row.names)
    effectName                  include fix   test  initialValue parm
  1 basic rate parameter mynet1 TRUE    FALSE FALSE    4.80941   0   
  2 outdegree (density)         TRUE    FALSE FALSE   -0.56039   0   
  3 reciprocity                 TRUE    FALSE FALSE    0.00000   0   
  4 transitive triplets         TRUE    FALSE FALSE    0.00000   0   
  > ##test4
  > mymodel$cconditional <- TRUE
  > mymodel$condvarno<- 1
  > print('test4')
  [1] "test4"
  > ans<- siena07(mymodel, data=mydata, effects=myeff,  batch=TRUE,
  +               parallelTesting=TRUE, silent=TRUE)
  > ##, verbose=TRUE)#,dll='../siena/src/RSiena.dll')
  > ans
  Estimates, standard errors and convergence t-ratios
  
                                Estimate   Standard   Convergence 
                                             Error      t-ratio   
  
  Rate parameters: 
    0       Rate parameter       3.2640  ( 0.5554   )             
  
  Other parameters: 
    1. eval outdegree (density) -1.7460  ( 0.4314   )   0.1523    
    2. eval reciprocity          1.4356  ( 0.4395   )   0.2525    
    3. eval transitive triplets  0.3126  ( 0.1201   )   0.0971    
  
  Overall maximum convergence ratio:    0.2753 
  
  
  Total of 231 iteration steps.
  
  > ##test5
  > mynet1 <- sienaDependent(array(c(tmp3,tmp4),dim=c(32,32,2)))
  > mydata <- sienaDataCreate(mynet1)
  > myeff<- getEffects(mydata)
  > mymodel<- model.create(fn = simstats0c,  nsub=2, n3=50,
  +                        cond=FALSE, seed=5)
  If you use this algorithm object, siena07 will create/use an output file Siena.txt .
  > print('test5')
  [1] "test5"
  > ans<- siena07(mymodel, data=mydata, effects=myeff,  batch=TRUE,
  +               parallelTesting=TRUE, silent=TRUE)
  > ans
  Estimates, standard errors and convergence t-ratios
  
                                        Estimate   Standard   Convergence 
                                                     Error      t-ratio   
    1. rate basic rate parameter mynet1  3.0451  ( 0.5078   )    0.0103   
    2. eval outdegree (density)         -1.1448  ( 0.1690   )   -0.2193   
    3. eval reciprocity                  1.7883  ( 0.3820   )   -0.0597   
  
  Overall maximum convergence ratio:    0.2334 
  
  
  Total of 588 iteration steps.
  
  > (myeff <- includeEffects(myeff, recip, inPop))
    effectName            include fix   test  initialValue parm
  1 reciprocity           TRUE    FALSE FALSE          0   0   
  2 indegree - popularity TRUE    FALSE FALSE          0   0   
    effectName                  include fix   test  initialValue parm
  1 basic rate parameter mynet1 TRUE    FALSE FALSE    4.80941   0   
  2 outdegree (density)         TRUE    FALSE FALSE   -0.56039   0   
  3 reciprocity                 TRUE    FALSE FALSE    0.00000   0   
  4 indegree - popularity       TRUE    FALSE FALSE    0.00000   0   
  > (myeff <- includeEffects(myeff, outAct, fix=TRUE, test=TRUE))
    effectName           include fix  test initialValue parm
  1 outdegree - activity TRUE    TRUE TRUE          0   0   
    effectName                  include fix   test  initialValue parm
  1 basic rate parameter mynet1 TRUE    FALSE FALSE    4.80941   0   
  2 outdegree (density)         TRUE    FALSE FALSE   -0.56039   0   
  3 reciprocity                 TRUE    FALSE FALSE    0.00000   0   
  4 indegree - popularity       TRUE    FALSE FALSE    0.00000   0   
  5 outdegree - activity        TRUE    TRUE  TRUE     0.00000   0   
  > (myeff <- includeInteraction(myeff, recip, inPop, fix=TRUE, test=TRUE))
    effectName                          include fix  test initialValue parm
  1 reciprocity x indegree - popularity TRUE    TRUE TRUE          0   0   
    effect1 effect2
  1 14      77     
    effectName                          include fix   test  initialValue parm
  1 basic rate parameter mynet1         TRUE    FALSE FALSE    4.80941   0   
  2 outdegree (density)                 TRUE    FALSE FALSE   -0.56039   0   
  3 reciprocity                         TRUE    FALSE FALSE    0.00000   0   
  4 indegree - popularity               TRUE    FALSE FALSE    0.00000   0   
  5 outdegree - activity                TRUE    TRUE  TRUE     0.00000   0   
  6 reciprocity x indegree - popularity TRUE    TRUE  TRUE     0.00000   0   
    effect1 effect2
  1  0       0     
  2  0       0     
  3  0       0     
  4  0       0     
  5  0       0     
  6 14      77     
  > ans<- siena07(mymodel, data=mydata, effects=myeff,  batch=TRUE,
  +               parallelTesting=TRUE, silent=TRUE)
  > ans
  Estimates, standard errors and convergence t-ratios
  
                                                Estimate   Standard   Convergence 
                                                             Error      t-ratio   
    1. rate basic rate parameter mynet1          3.0375  ( 0.6487   )   -0.1316   
    2. eval outdegree (density)                 -1.4598  ( 0.4672   )   -0.0938   
    3. eval reciprocity                          1.8008  ( 0.5119   )    0.0402   
    4. eval indegree - popularity                0.0541  ( 0.0655   )   -0.1327   
    5. eval outdegree - activity                 0.0000  (     NA   )   -1.1184   
    6. eval reciprocity x indegree - popularity  0.0000  (     NA   )    0.3944   
  
  Overall maximum convergence ratio:    0.2102 
  
  
  Score test for 2 parameters:
  chi-squared = 11.43, p = 0.0033.
  
  Total of 612 iteration steps.
  
  > score.Test(ans)
  Tested effects:
   outdegree - activity eval 
   reciprocity x indegree - popularity eval 
  chi-squared = 11.43, d.f. = 2;  p = 0.003. 
  > ##test6
  > mynet1 <- sienaDependent(array(c(tmp3,tmp4),dim=c(32,32,2)))
  > mydata <- sienaDataCreate(mynet1)
  > myeff<- getEffects(mydata)
  > mymodel<- model.create(fn = simstats0c,  nsub=2, n3=50,
  +                        cond=FALSE, doubleAveraging=0,seed=5)
  If you use this algorithm object, siena07 will create/use an output file Siena.txt .
  > print('test6')
  [1] "test6"
  > ans<- siena07(mymodel, data=mydata, effects=myeff,  batch=TRUE,
  +               parallelTesting=TRUE, silent=TRUE)
  > ans
  Estimates, standard errors and convergence t-ratios
  
                                        Estimate   Standard   Convergence 
                                                     Error      t-ratio   
    1. rate basic rate parameter mynet1  3.0451  ( 0.5078   )    0.0103   
    2. eval outdegree (density)         -1.1448  ( 0.1690   )   -0.2193   
    3. eval reciprocity                  1.7883  ( 0.3820   )   -0.0597   
  
  Overall maximum convergence ratio:    0.2334 
  
  
  Total of 588 iteration steps.
  
  > myeff <- includeEffects(myeff, recip, include=FALSE)
  [1] effectName   include      fix          test         initialValue
  [6] parm        
  <0 rows> (or 0-length row.names)
  > myeff <- includeEffects(myeff, recip, type='endow')
    effectName  include fix   test  initialValue parm type 
  1 reciprocity TRUE    FALSE FALSE          0   0    endow
  > myeff <- includeEffects(myeff, recip, type='creation')
    effectName  include fix   test  initialValue parm type    
  1 reciprocity TRUE    FALSE FALSE          0   0    creation
  > ans<- siena07(mymodel, data=mydata, effects=myeff,  batch=TRUE,
  +               parallelTesting=TRUE, silent=TRUE)
  > ans
  Estimates, standard errors and convergence t-ratios
  
                                         Estimate   Standard   Convergence 
                                                      Error      t-ratio   
    1. rate  basic rate parameter mynet1  3.1508  ( 0.5674   )   -0.0293   
    2. eval  outdegree (density)         -1.0976  ( 0.1952   )   -0.0648   
    3. endow reciprocity                  2.3709  ( 0.9090   )    0.0125   
    4. creat reciprocity                  1.0175  ( 0.7842   )   -0.0142   
  
  Overall maximum convergence ratio:    0.0827 
  
  
  Total of 595 iteration steps.
  
  > testSame.RSiena(ans, 3, 4)
  Tested effects:
   reciprocity endow  ==  reciprocity creation 
  chi-squared = 0.91, d.f. = 1; one-sided Z = 0.95; two-sided p = 0.341. 
  > ##test7
  > mynet1 <- sienaDependent(array(c(tmp3,tmp4),dim=c(32,32,2)))
  > mydata <- sienaDataCreate(mynet1)
  > myeff<- getEffects(mydata)
  > mymodel<- model.create(fn = simstats0c,  nsub=2, n3=50,
  +                        cond=FALSE,  diagonalize=0.5, seed=5)
  If you use this algorithm object, siena07 will create/use an output file Siena.txt .
  > print('test7')
  [1] "test7"
  > ans<- siena07(mymodel, data=mydata, effects=myeff,  batch=TRUE,
  +               parallelTesting=TRUE, silent=TRUE)
  > ##, verbose=TRUE)#,dll='../siena/src/RSiena.dll')
  > ans
  Estimates, standard errors and convergence t-ratios
  
                                        Estimate   Standard   Convergence 
                                                     Error      t-ratio   
    1. rate basic rate parameter mynet1  3.0258  ( 0.5063   )   -0.1656   
    2. eval outdegree (density)         -1.1558  ( 0.1821   )    0.0801   
    3. eval reciprocity                  1.8464  ( 0.3633   )    0.2701   
  
  Overall maximum convergence ratio:    0.3630 
  
  
  Total of 545 iteration steps.
  
  > ##test8
  > mymodel<- model.create(fn = simstats0c,  nsub=1, n3=50,
  +                        cond=TRUE, condvarno=1, seed=5)
  If you use this algorithm object, siena07 will create/use an output file Siena.txt .
  > print('test8')
  [1] "test8"
  > ans <- siena07(mymodel, data=mydata, effects=myeff,  batch=TRUE,
  +               parallelTesting=TRUE, silent=TRUE)
  > ##, verbose=TRUE)#,dll='../siena/src/RSiena.dll')
  > ans
  Estimates, standard errors and convergence t-ratios
  
                                Estimate   Standard   Convergence 
                                             Error      t-ratio   
  
  Rate parameters: 
    0       Rate parameter       3.0998  ( 0.5110   )             
  
  Other parameters: 
    1. eval outdegree (density) -1.1509  ( 0.2244   )   -0.1080   
    2. eval reciprocity          1.7982  ( 0.4363   )    0.0185   
  
  Overall maximum convergence ratio:    0.1564 
  
  
  Total of 240 iteration steps.
  
  > ##test9
  > mynet1 <- sienaDependent(array(c(s501, s502, s503), dim=c(50, 50, 3)))
  > mynet2 <- sienaDependent(s50a,type='behavior')
  > mydata <- sienaDataCreate(mynet1, mynet2)
  > myeff <- getEffects(mydata)
  > myeff <- setEffect(myeff, linear, initialValue=0.34699930338, name="mynet2")
    effectName          include fix   test  initialValue parm
  1 mynet2 linear shape TRUE    FALSE FALSE      0.347   0   
  > myeff <- setEffect(myeff, avAlt, name="mynet2", interaction1="mynet1")
    effectName           include fix   test  initialValue parm
  1 mynet2 average alter TRUE    FALSE FALSE          0   0   
  > ##myeff$initialValue[98] <- 0.34699930338 ## siena3 starting values differ
  > ##test10
  > print('test10')
  [1] "test10"
  > mymodel$cconditional <- TRUE
  > mymodel$condvarno<- 1
  > ans <- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE,
  +                parallelTesting=TRUE, silent=TRUE)
  > ##, verbose=TRUE)
  > ans
  Estimates, standard errors and convergence t-ratios
  
                                                    Estimate   Standard   Convergence 
                                                                 Error      t-ratio   
  
  Rate parameters: 
    0.1      Rate parameter cond. variable period 1  6.0629  ( 1.2683   )             
    0.2      Rate parameter cond. variable period 2  4.4421  ( 0.6116   )             
  
  Other parameters: 
  Network Dynamics 
    1.  eval outdegree (density)                    -2.4172  ( 0.1896   )   -0.0951   
    2.  eval reciprocity                             2.9130  ( 0.3222   )    0.0078   
  
  Behavior Dynamics
    3.  rate rate mynet2 (period 1)                  1.3101  ( 0.4665   )    0.0888   
    4.  rate rate mynet2 (period 2)                  1.6253  ( 0.5829   )   -0.0653   
    5.  eval mynet2 linear shape                     0.4125  ( 0.2154   )   -0.0459   
    6.  eval mynet2 quadratic shape                 -0.5443  ( 0.4127   )   -0.0480   
    7.  eval mynet2 average alter                    1.2691  ( 1.1516   )   -0.0736   
  
  Overall maximum convergence ratio:    0.2577 
  
  
  Total of 335 iteration steps.
  
  > ##test11
  > print('test11')
  [1] "test11"
  > mymodel<- model.create(fn = simstats0c,  nsub=1, n3=50,
  +                        behModelType=c(mynet2=2), seed=6)
  If you use this algorithm object, siena07 will create/use an output file Siena.txt .
  > (ans <- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE,
  +                parallelTesting=TRUE, silent=TRUE))
  Estimates, standard errors and convergence t-ratios
  
                                            Estimate   Standard   Convergence 
                                                         Error      t-ratio   
  Network Dynamics 
    1. rate constant mynet1 rate (period 1)  5.7911  ( 1.8273   )   -0.2276   
    2. rate constant mynet1 rate (period 2)  4.5005  ( 0.8629   )   -0.1708   
    3. eval outdegree (density)             -2.3618  ( 0.1201   )   -0.2746   
    4. eval reciprocity                      2.8187  ( 0.1675   )   -0.2688   
  
  Behavior Dynamics
    5. rate rate mynet2 (period 1)           1.3221  ( 0.4155   )    0.0399   
    6. rate rate mynet2 (period 2)           1.7863  ( 0.5585   )   -0.0260   
    7. eval mynet2 linear shape              0.3724  ( 0.2906   )   -0.0873   
    8. eval mynet2 quadratic shape          -0.5723  ( 0.3959   )   -0.1168   
    9. eval mynet2 average alter             1.2012  ( 0.7552   )    0.0420   
  
  Overall maximum convergence ratio:    0.5329 
  
  
   Behavioral Model Type:
  mynet2 : Boundary-absorbing behavior model 
  
  
  Total of 340 iteration steps.
  
  > ##test12
  > print('test12')
  [1] "test12"
  > use<- 1:30
  > mynet1 <- sienaDependent(array(c(s501[use,], s502[use,], s503[use,]),
  +                          dim=c(length(use), 50,3)), type='bipartite',
  +                          nodeSet=c('Senders','receivers'))
  > receivers <- sienaNodeSet(50,'receivers')
  > senders <- sienaNodeSet(30,'Senders')
  > myvar1 <- coCovar(s50a[1:30,2], nodeSet='Senders')
  > mydata <- sienaDataCreate(mynet1, myvar1, nodeSets=list(senders, receivers))
  > myeff <- getEffects(mydata)
  > myeff <- includeEffects(myeff, inPop)
    effectName            include fix   test  initialValue parm
  1 indegree - popularity TRUE    FALSE FALSE          0   0   
  > myeff <- setEffect(myeff, altInDist2, interaction1="myvar1", parameter=1)
    effectName             include fix   test  initialValue parm
  1 myvar1 in-alter dist 2 TRUE    FALSE FALSE          0   1   
  > ans <- siena07(sienaModelCreate(n3=50, nsub=2, seed=1),
  +                data=mydata, effects=myeff, batch=TRUE, silent=TRUE)
  If you use this algorithm object, siena07 will create/use an output file Siena.txt .
  > ans
  Estimates, standard errors and convergence t-ratios
  
                                     Estimate   Standard   Convergence 
                                                  Error      t-ratio   
  
  Rate parameters: 
    0.1      Rate parameter period 1  3.6549  (  0.5402  )             
    0.2      Rate parameter period 2  2.7936  (  0.4046  )             
  
  Other parameters: 
    1.  eval outdegree (density)      2.6894  (  5.1633  )    0.7569   
    2.  eval indegree - popularity   -3.4982  (  3.1025  )    0.8662   
    3.  eval myvar1 in-alter dist 2  33.4085  ( 10.8879  )   -2.1192   
  
  Overall maximum convergence ratio:    7.2880 
  
  
  Total of 588 iteration steps.
  
  > tt <- sienaTimeTest(ans)
  > summary(tt)
  Joint significance test of time heterogeneity:
  chi-squared =   4.24, d.f. = 3, p= 0.2365, 
  where H0: The following parameters are zero:
   (1) (*)Dummy2:outdegree (density)
   (2) (*)Dummy2:indegree - popularity
   (3) (*)Dummy2:myvar1 in-alter dist 2
  
  Individual significance tests and one-step estimators:
                                   Initial Est. One Step Est. p-Value  
  outdegree (density)                 2.6894      15.2890        0.6020
  indegree - popularity              -3.4982     -16.1684        0.2600
  myvar1 in-alter dist 2             33.4085     142.5361        0.0020
  (*)Dummy2:outdegree (density)       0.0000     -17.8685        0.5020
  (*)Dummy2:indegree - popularity     0.0000      22.3587        0.1800
  (*)Dummy2:myvar1 in-alter dist 2    0.0000    -214.6286        0.0520
  
  Effect-wise joint significance tests
  (i.e. each effect across all dummies):
                         chi-sq. df p-value
  outdegree (density)       0.45  1   0.502
  indegree - popularity     1.80  1   0.180
  myvar1 in-alter dist 2    3.77  1   0.052
  
  Period-wise joint significance tests
  (i.e. each period across all parameters):
           chi-sq. df p-value
  Period 1    4.24  3   0.236
  Period 2    4.24  3   0.236
  
  Use the following indices for plotting:
    (1) outdegree (density)
    (2) indegree - popularity
    (3) myvar1 in-alter dist 2
  
  If you would like to fit time dummies to your model,
  use the includeTimeDummy function.
  Type "?sienaTimeTest" for more information on this output.
  > ##test13
  > print('test13')
  [1] "test13"
  > use<- 1:30
  > mynet1 <- sienaDependent(array(c(s502[,use], s503[,use]),
  +                          dim=c(50, length(use), 2)), type='bipartite',
  +                          nodeSet=c('Senders','receivers'))
  > receivers <- sienaNodeSet(30,'receivers')
  > senders <- sienaNodeSet(50,'Senders')
  > myvar1 <- coCovar(s50a[1:50,2], nodeSet='Senders')
  > mydata <- sienaDataCreate(mynet1, myvar1, nodeSets=list(senders, receivers))
  > myeff <- getEffects(mydata)
  > myeff <- setEffect(myeff, altInDist2, interaction1="myvar1", parameter=1)
    effectName             include fix   test  initialValue parm
  1 myvar1 in-alter dist 2 TRUE    FALSE FALSE          0   1   
  > myeff <- setEffect(myeff, egoX, interaction1="myvar1")
    effectName include fix   test  initialValue parm
  1 myvar1 ego TRUE    FALSE FALSE          0   0   
  > (ans <- siena07(sienaModelCreate(n3=50, nsub=2, seed=1),
  +                data=mydata, effects=myeff, batch=TRUE, silent=TRUE))
  If you use this algorithm object, siena07 will create/use an output file Siena.txt .
  Estimates, standard errors and convergence t-ratios
  
                                   Estimate   Standard   Convergence 
                                                Error      t-ratio   
  
  Rate parameters: 
    0       Rate parameter          1.8747  ( 0.3077   )             
  
  Other parameters: 
    1. eval outdegree (density)    -1.8071  ( 0.1798   )    0.0536   
    2. eval myvar1 ego              0.3003  ( 0.1138   )   -0.0517   
    3. eval myvar1 in-alter dist 2 -0.0569  ( 0.1678   )   -0.0475   
  
  Overall maximum convergence ratio:    0.0766 
  
  
  Total of 588 iteration steps.
  
  > ##test14
  > print('test14')
  [1] "test14"
  > net <- sienaDependent(array(c(tmp3, tmp4), dim=c(32, 32, 2)))
  > dataset <- sienaDataCreate(net)
  > myeff <- getEffects(dataset)
  > myeff <- includeEffects(myeff, inPop)
    effectName            include fix   test  initialValue parm
  1 indegree - popularity TRUE    FALSE FALSE          0   0   
  > algo <- sienaAlgorithmCreate(nsub=1, n3=20, maxlike=TRUE, seed=15, mult=1, prML=1)
  If you use this algorithm object, siena07 will create/use an output file Siena.txt .
  > (ans <- siena07(algo, data=dataset, effects=myeff, batch=TRUE, silent=TRUE))
  Estimated by Maximum Likelihood
  
  Estimates, standard errors and convergence t-ratios
  
                                     Estimate   Standard   Convergence 
                                                  Error      t-ratio   
    1. rate basic rate parameter net  3.7816  ( 0.4178   )   0.6167    
    2. eval outdegree (density)      -1.5840  ( 0.3353   )   2.9602    
    3. eval reciprocity               1.8606  ( 0.2314   )   1.4786    
    4. eval indegree - popularity     0.1082  ( 0.0440   )   2.5318    
  
  Overall maximum convergence ratio:    4.7198 
  
  
  Total of 266 iteration steps.
  
  > ##test 15
  > print('test15')
  [1] "test15"
  > mynet1 <- sienaDependent(array(c(s501, s502, s503), dim=c(50, 50, 3)))
  > mynet2 <- sienaDependent(s50a,type='behavior')
  > mydata <- sienaDataCreate(mynet1, mynet2)
  > myeff <- getEffects(mydata)
  > (myeff <- includeEffects(myeff, transTrip))
    effectName          include fix   test  initialValue parm
  1 transitive triplets TRUE    FALSE FALSE          0   0   
    name   effectName                      include fix   test  initialValue parm
  1 mynet1 constant mynet1 rate (period 1) TRUE    FALSE FALSE    4.69604   0   
  2 mynet1 constant mynet1 rate (period 2) TRUE    FALSE FALSE    4.32885   0   
  3 mynet1 outdegree (density)             TRUE    FALSE FALSE   -1.46770   0   
  4 mynet1 reciprocity                     TRUE    FALSE FALSE    0.00000   0   
  5 mynet1 transitive triplets             TRUE    FALSE FALSE    0.00000   0   
  6 mynet2 rate mynet2 (period 1)          TRUE    FALSE FALSE    0.70571   0   
  7 mynet2 rate mynet2 (period 2)          TRUE    FALSE FALSE    0.84939   0   
  8 mynet2 mynet2 linear shape             TRUE    FALSE FALSE    0.32237   0   
  9 mynet2 mynet2 quadratic shape          TRUE    FALSE FALSE    0.00000   0   
  > (myeff <- includeEffects(myeff, egoX, simX, interaction1="mynet2"))
    effectName        include fix   test  initialValue parm
  1 mynet2 ego        TRUE    FALSE FALSE          0   0   
  2 mynet2 similarity TRUE    FALSE FALSE          0   0   
     name   effectName                      include fix   test  initialValue parm
  1  mynet1 constant mynet1 rate (period 1) TRUE    FALSE FALSE    4.69604   0   
  2  mynet1 constant mynet1 rate (period 2) TRUE    FALSE FALSE    4.32885   0   
  3  mynet1 outdegree (density)             TRUE    FALSE FALSE   -1.46770   0   
  4  mynet1 reciprocity                     TRUE    FALSE FALSE    0.00000   0   
  5  mynet1 transitive triplets             TRUE    FALSE FALSE    0.00000   0   
  6  mynet1 mynet2 ego                      TRUE    FALSE FALSE    0.00000   0   
  7  mynet1 mynet2 similarity               TRUE    FALSE FALSE    0.00000   0   
  8  mynet2 rate mynet2 (period 1)          TRUE    FALSE FALSE    0.70571   0   
  9  mynet2 rate mynet2 (period 2)          TRUE    FALSE FALSE    0.84939   0   
  10 mynet2 mynet2 linear shape             TRUE    FALSE FALSE    0.32237   0   
  11 mynet2 mynet2 quadratic shape          TRUE    FALSE FALSE    0.00000   0   
  > (myeff <- includeEffects(myeff, avSim, name="mynet2", interaction1="mynet1"))
    effectName                include fix   test  initialValue parm
  1 mynet2 average similarity TRUE    FALSE FALSE          0   0   
     name   effectName                      include fix   test  initialValue parm
  1  mynet1 constant mynet1 rate (period 1) TRUE    FALSE FALSE    4.69604   0   
  2  mynet1 constant mynet1 rate (period 2) TRUE    FALSE FALSE    4.32885   0   
  3  mynet1 outdegree (density)             TRUE    FALSE FALSE   -1.46770   0   
  4  mynet1 reciprocity                     TRUE    FALSE FALSE    0.00000   0   
  5  mynet1 transitive triplets             TRUE    FALSE FALSE    0.00000   0   
  6  mynet1 mynet2 ego                      TRUE    FALSE FALSE    0.00000   0   
  7  mynet1 mynet2 similarity               TRUE    FALSE FALSE    0.00000   0   
  8  mynet2 rate mynet2 (period 1)          TRUE    FALSE FALSE    0.70571   0   
  9  mynet2 rate mynet2 (period 2)          TRUE    FALSE FALSE    0.84939   0   
  10 mynet2 mynet2 linear shape             TRUE    FALSE FALSE    0.32237   0   
  11 mynet2 mynet2 quadratic shape          TRUE    FALSE FALSE    0.00000   0   
  12 mynet2 mynet2 average similarity       TRUE    FALSE FALSE    0.00000   0   
  > (myeff <- includeGMoMStatistics(myeff, simX_gmm, interaction1="mynet2"))
      name shortName type include
  1 mynet1  simX_gmm  gmm    TRUE
  
   Effects and statistics for estimation by the Generalized Method of Moments
  
   Effects
     name   effectName                      include fix   test  initialValue parm
  1  mynet1 constant mynet1 rate (period 1) TRUE    FALSE FALSE    4.69604   0   
  2  mynet1 constant mynet1 rate (period 2) TRUE    FALSE FALSE    4.32885   0   
  3  mynet1 outdegree (density)             TRUE    FALSE FALSE   -1.46770   0   
  4  mynet1 reciprocity                     TRUE    FALSE FALSE    0.00000   0   
  5  mynet1 transitive triplets             TRUE    FALSE FALSE    0.00000   0   
  6  mynet1 mynet2 ego                      TRUE    FALSE FALSE    0.00000   0   
  7  mynet1 mynet2 similarity               TRUE    FALSE FALSE    0.00000   0   
  8  mynet2 rate mynet2 (period 1)          TRUE    FALSE FALSE    0.70571   0   
  9  mynet2 rate mynet2 (period 2)          TRUE    FALSE FALSE    0.84939   0   
  10 mynet2 mynet2 linear shape             TRUE    FALSE FALSE    0.32237   0   
  11 mynet2 mynet2 quadratic shape          TRUE    FALSE FALSE    0.00000   0   
  12 mynet2 mynet2 average similarity       TRUE    FALSE FALSE    0.00000   0   
     type
  1  rate
  2  rate
  3  eval
  4  eval
  5  eval
  6  eval
  7  eval
  8  rate
  9  rate
  10 eval
  11 eval
  12 eval
  
   Regular and GMoM statistics
     name   effectName                      Statistic
  1  mynet1 constant mynet1 rate (period 1) Regular  
  2  mynet1 constant mynet1 rate (period 2) Regular  
  3  mynet1 outdegree (density)             Regular  
  4  mynet1 reciprocity                     Regular  
  5  mynet1 transitive triplets             Regular  
  6  mynet1 mynet2 ego                      Regular  
  7  mynet1 mynet2 similarity               Regular  
  8  mynet2 rate mynet2 (period 1)          Regular  
  9  mynet2 rate mynet2 (period 2)          Regular  
  10 mynet2 mynet2 linear shape             Regular  
  11 mynet2 mynet2 quadratic shape          Regular  
  12 mynet2 mynet2 average similarity       Regular  
  13 mynet1 mynet2 similarity               GMoM     
  > algo <- sienaAlgorithmCreate(nsub=1, n3=100, gmm=TRUE, seed=6)
  If you use this algorithm object, siena07 will create/use an output file Siena.txt .
  > (ans <- siena07(algo, data=mydata, effects=myeff, batch=TRUE,
  +                 parallelTesting=TRUE, silent=TRUE))
  Estimated by Generalized Method of Moments
  
  Estimates, standard errors and convergence t-ratios
  
                                             Estimate   Standard   Convergence 
                                                          Error      t-ratio   
  Network Dynamics 
     1. rate constant mynet1 rate (period 1)  5.6736  ( 0.6951   )   -0.7532   
     2. rate constant mynet1 rate (period 2)  4.3793  ( 0.7939   )   -0.2401   
     3. eval outdegree (density)             -2.5281  ( 0.1238   )    3.0663   
     4. eval reciprocity                      2.7731  ( 0.2046   )    3.2600   
     5. eval transitive triplets              0.4332  ( 0.0824   )   -0.8205   
     6. eval mynet2 ego                       0.0220  ( 0.0772   )    1.7040   
     7. eval mynet2 similarity                0.8670  ( 0.3446   )    1.4465   
  
  Behavior Dynamics
     8. rate rate mynet2 (period 1)           1.2663  ( 0.3007   )    1.0844   
     9. rate rate mynet2 (period 2)           1.6833  ( 0.3537   )   -0.9834   
    10. eval mynet2 linear shape              0.3607  ( 0.2267   )    0.5830   
    11. eval mynet2 quadratic shape          -0.1885  ( 0.1240   )   -0.8490   
    12. eval mynet2 average similarity        1.4133  ( 1.6348   )   -0.1432   
  
  Overall maximum convergence ratio:    5.7962 
  
  
  Total of 757 iteration steps.
  
  > ##test16
  > print('test16')
  [1] "test16"
  > set.seed(123) # simulate behavior data according to dZ(t) = [-0.1 Z + 1] dt + 1 dW(t)
  > y1 <- rnorm(50, 0,3)
  > y2 <- exp(-0.1) * y1 + (1-exp(-0.1)) * 1/ -0.1 + rnorm(50, 0, (exp(-0.2)- 1) / -0.2 * 1^2)
  > friend <- sienaDependent(array(c(s501, s502), dim = c(50,50,2)))
  > behavior <- sienaDependent(matrix(c(y1,y2), 50,2), type = "continuous")
  > (mydata <- sienaDataCreate(friend, behavior))
  Dependent variables:  friend, behavior 
  Number of observations: 2 
  
  Nodeset                  Actors 
  Number of nodes              50 
  
  Dependent variable friend     
  Type               oneMode    
  Observations       2          
  Nodeset            Actors     
  Densities          0.046 0.047
  
  Dependent variable behavior      
  Type               continuous    
  Observations       2             
  Nodeset            Actors        
  Range              -6.242 - 6.507
  
  > (myeff <- getEffects(mydata, onePeriodSde = TRUE))
  SDE init parameters:  -0.1117176 -0.864023 0.8576913 
  SDE par stand errors: 0.04669358 0.1223113 0.08791327 
    name     effectName                  include fix   test  initialValue parm
  1 friend   basic rate parameter friend TRUE    FALSE FALSE    4.69604   0   
  2 friend   outdegree (density)         TRUE    FALSE FALSE   -1.48852   0   
  3 friend   reciprocity                 TRUE    FALSE FALSE    0.00000   0   
  4 sde      scale parameter period 1    TRUE    TRUE  FALSE    1.00000   0   
  5 behavior wiener (behavior.behavior)  TRUE    FALSE FALSE    0.85769   0   
  6 behavior feedback from behavior      TRUE    FALSE FALSE   -0.11172   0   
  7 behavior intercept                   TRUE    FALSE FALSE   -0.86402   0   
  > algorithmMoM <- sienaAlgorithmCreate(nsub=1, n3=20, seed=321)
  If you use this algorithm object, siena07 will create/use an output file Siena.txt .
  > (ans <- siena07(algorithmMoM, data = mydata, effects = myeff, batch=TRUE,
  +                                                 silent=TRUE))
  SDE init parameters:  -0.1117176 -0.864023 0.8576913 
  SDE par stand errors: 0.04669358 0.1223113 0.08791327 
  Estimates, standard errors and convergence t-ratios
  
                                        Estimate   Standard   Convergence 
                                                     Error      t-ratio   
  Network Dynamics 
    1. rate basic rate parameter friend  5.5123  (  1.2624  )    0.0490   
    2. eval outdegree (density)         -2.2836  (  0.1444  )   -0.5160   
    3. eval reciprocity                  2.5132  (  0.2420  )   -0.2630   
  
  Continuous Behavior Dynamics
    4. eval wiener (behavior.behavior)   0.8457  ( 10.9158  )   -0.3587   
    5. eval feedback from behavior      -0.1111  (  0.0684  )    0.2224   
    6. eval intercept                   -0.8643  (  0.2726  )    0.1532   
    7. rate scale parameter period 1     1.0000  (      NA  )   -0.3587   
  
  Overall maximum convergence ratio:    0.7099 
  
  
  Total of 305 iteration steps.
  
  > ##test17
  > print('test17')
  [1] "test17"
  > mynet <- sienaNet(array(c(s501, s502), dim=c(50, 50, 2)))
  > sm1 <- 1*(s50s[,2] >= 2)
  > sm2 <- 1*(s50s[,3] >= 2)
  > sm2 <- pmax(sm1,sm2)
  > sm2[c(33,28,29,44)] <- 1
  > mybeh <- sienaDependent(cbind(sm1,sm2), type="behavior")
  > (mydata <- sienaDataCreate(mynet, mybeh))
  For dependent variable mybeh, in some periods,
  there are only increases, or only decreases.
  This will be respected in the simulations. 
  If this is not desired, use allowOnly=FALSE when creating the dependent variable.
  Dependent variables:  mynet, mybeh 
  Number of observations: 2 
  
  Nodeset                  Actors 
  Number of nodes              50 
  
  Dependent variable mynet      
  Type               oneMode    
  Observations       2          
  Nodeset            Actors     
  Densities          0.046 0.047
  
  Dependent variable mybeh   
  Type               behavior
  Observations       2       
  Nodeset            Actors  
  Range              0 - 1   
  
  "uponly":  mybeh:    all periods
  > mymodel <- sienaModelCreate(projname=NULL, seed=1234, firstg=0.001, nsub=1, n3=10)
  If you use this algorithm object, siena07 will create/use an output file Siena.txt .
  > myeff <- getEffects(mydata)
  > (myeff <- setEffect(myeff,avExposure,type='rate',parameter=2,
  +                             name='mybeh',interaction1='mynet'))
    effectName                            include fix   test  initialValue parm
  1 average exposure effect on rate mybeh TRUE    FALSE FALSE          0   2   
    name  effectName                            include fix   test  initialValue
  1 mynet basic rate parameter mynet            TRUE    FALSE FALSE    4.69604  
  2 mynet outdegree (density)                   TRUE    FALSE FALSE   -1.48852  
  3 mynet reciprocity                           TRUE    FALSE FALSE    0.00000  
  4 mybeh rate mybeh period 1                   TRUE    FALSE FALSE    0.26000  
  5 mybeh average exposure effect on rate mybeh TRUE    FALSE FALSE    0.00000  
    parm
  1 0   
  2 0   
  3 0   
  4 0   
  5 2   
  > (ans <- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE, silent=TRUE))
  Estimates, standard errors and convergence t-ratios
  
                                                  Estimate   Standard   Convergence 
                                                               Error      t-ratio   
  Network Dynamics 
    1. rate basic rate parameter mynet             5.0309  ( 1.8002   )    3.1556   
    2. eval outdegree (density)                   -1.5767  ( 0.1300   )    1.7004   
    3. eval reciprocity                            0.4639  ( 0.3817   )   -6.0068   
  
  Behavior Dynamics
    4. rate rate mybeh period 1                    0.3731  ( 0.2364   )   -1.2577   
    5. rate average exposure effect on rate mybeh  0.4511  ( 1.4274   )   -3.5793   
  
  Overall maximum convergence ratio:   13.9256 
  
  
  Total of 290 iteration steps.
  
  > ##test18
  > print('test18')
  [1] "test18"
  > myalgorithm <- sienaAlgorithmCreate(nsub=1, n3=10, seed=1293)
  If you use this algorithm object, siena07 will create/use an output file Siena.txt .
  > mynet1 <- sienaDependent(array(c(tmp3, tmp4), dim=c(32, 32, 2)))
  > cova <- coCovar(1:32)
  > cova2 <- coCovar(rep(0,32), warn=FALSE)
  > mydata <- sienaDataCreate(mynet1, cova)
  > mydata2 <- sienaDataCreate(mynet1, cova=cova2)
  > mygroup <- sienaGroupCreate(list(mydata,mydata2))
  > myeff <- getEffects(mygroup)
  > myeff <- setEffect(myeff, simX, interaction1='cova')
    effectName      include fix   test  initialValue parm
  1 cova similarity TRUE    FALSE FALSE          0   0   
  > (ans <- siena07(myalgorithm, data=mygroup, effects=myeff, batch=TRUE,
  +                               silent=TRUE))
  Estimates, standard errors and convergence t-ratios
  
                                     Estimate   Standard   Convergence 
                                                  Error      t-ratio   
  
  Rate parameters: 
    0.1      Rate parameter period 1  3.1050  ( 0.4096   )             
    0.2      Rate parameter period 2  3.4061  ( 0.5843   )             
  
  Other parameters: 
    1.  eval outdegree (density)     -1.1485  ( 1.1638   )   -0.4229   
    2.  eval reciprocity              1.8147  ( 0.9422   )   -0.1142   
    3.  eval cova similarity          0.0027  ( 1.9694   )   -0.4641   
  
  Overall maximum convergence ratio:    0.5163 
  
  
  Total of 285 iteration steps.
  
  > ## delete output file
  > if (file.exists('Siena.txt')){unlink('Siena.txt')}
  > 
  > proc.time()
     user  system elapsed 
    33.26    0.51   33.71