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Type 'q()' to quit R. > 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 37.25 0.39 37.62