Running ‘parallel.R’ [67s/102s]
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)
  siena07 will create 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)
  siena07 will create 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, 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 outdegree - activity        TRUE    TRUE  TRUE     0.00000   0   
  > 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  3.0512  ( 0.5362   )    0.0177   
    2. eval outdegree (density)         -1.1688  ( 0.1862   )   -0.1783   
    3. eval reciprocity                  1.8243  ( 0.4225   )   -0.1683   
    4. eval outdegree - activity         0.0000  (     NA   )   -1.0807   
  
  Overall maximum convergence ratio:    0.1935 
  
  
  Score test for 1 parameter:
  chi-squared = 5.52, p = 0.0188.
  
  Total of 395 iteration steps.
  
  > score.Test(ans, 4)
  Tested effects:
   mynet1: outdegree - activity eval 
  chi-squared = 5.52, d.f. = 1; one-sided Z = 2.35;  p = 0.019. 
  > ##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)
  siena07 will create 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.
  
  > ##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)
  siena07 will create 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)
  siena07 will create 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)
  siena07 will create 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)
  siena07 will create 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   0.237
  Period 2    4.24  3   0.237
  
  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))
  siena07 will create 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)
  siena07 will create 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  3.7505  ( 0.3841   )   0.6758    
    2. eval outdegree (density)      -1.6225  ( 0.3358   )   1.3177    
    3. eval reciprocity               1.8051  ( 0.2445   )   1.5288    
    4. eval indegree - popularity     0.1246  ( 0.0449   )   1.6988    
  
  Overall maximum convergence ratio:    2.9012 
  
  
  Total of 266 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)
  siena07 will create an output file Siena.txt .
  > (ans <- siena07(algorithmMoM, data = mydata, effects = myeff, batch=TRUE))
  SDE init parameters:  -0.1519645 -1.175127 0.7355777 
  SDE par stand errors: 0.06428706 0.2889378 0.1507865 
  
  Start phase 0 
  theta:  4.696 -1.489  0.000  0.858 -0.112 -0.864  1.000 
  
  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: 2%
  Phase 1 Iteration 15 Progress: 2%
  Phase 1 Iteration 20 Progress: 3%
  Phase 1 Iteration 25 Progress: 4%
  Phase 1 Iteration 30 Progress: 5%
  Phase 1 Iteration 35 Progress: 5%
  Phase 1 Iteration 40 Progress: 6%
  Phase 1 Iteration 45 Progress: 7%
  Phase 1 Iteration 50 Progress: 8%
  Error in if ((var(scores[, i]) > 0) && (var(z$sf[, i]) > 0)) { : 
    missing value where TRUE/FALSE needed
  Calls: siena07 -> robmon -> phase1.2
  Execution halted