[Rsiena-commits] r38 - in pkg/RSiena: . man tests
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Tue Jan 12 19:28:21 CET 2010
Author: ripleyrm
Date: 2010-01-12 19:28:21 +0100 (Tue, 12 Jan 2010)
New Revision: 38
Modified:
pkg/RSiena/changeLog
pkg/RSiena/man/RSiena-package.Rd
pkg/RSiena/man/siena07.Rd
pkg/RSiena/man/sienaFit.Rd
pkg/RSiena/man/simstats0c.Rd
pkg/RSiena/tests/parallel.R
pkg/RSiena/tests/parallel.Rout.save
Log:
Reduce time taken to run tests and examples.
Modified: pkg/RSiena/changeLog
===================================================================
--- pkg/RSiena/changeLog 2010-01-12 16:45:29 UTC (rev 37)
+++ pkg/RSiena/changeLog 2010-01-12 18:28:21 UTC (rev 38)
@@ -1,3 +1,9 @@
+2010-01-12 R-forge revision 35
+
+ * man/RSiena-package.Rd, man/siena07.Rd, man/sienaFit.Rd,
+ man/simstats0c.Rd: reduce time for examples
+ * tests/parallel.R, tests/patallel.Rout.save: reduce tests
+
2010-01-02 R-forge revision 34
* R/sienaprint.r: corrected layout of sienaFitThetaTable for
Modified: pkg/RSiena/man/RSiena-package.Rd
===================================================================
--- pkg/RSiena/man/RSiena-package.Rd 2010-01-12 16:45:29 UTC (rev 37)
+++ pkg/RSiena/man/RSiena-package.Rd 2010-01-12 18:28:21 UTC (rev 38)
@@ -45,9 +45,9 @@
\references{See \url{http://www.stats.ox.ac.uk/~snijders/siena/}}
\keyword{ package }
\examples{
-mynet1 <- sienaNet(array(c(s501, s502, s503), dim=c(50, 50, 3)))
+mynet1 <- sienaNet(array(c(tmp3, tmp4), dim=c(32, 32, 2)))
mydata <- sienaDataCreate(mynet1)
myeff <- getEffects(mydata)
-mymodel <- sienaModelCreate(findiff=FALSE, fn=simstats0c)
+mymodel <- sienaModelCreate(findiff=FALSE, fn=simstats0c, nsub=2, n3=100)
ans <- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE)
}
Modified: pkg/RSiena/man/siena07.Rd
===================================================================
--- pkg/RSiena/man/siena07.Rd 2010-01-12 16:45:29 UTC (rev 37)
+++ pkg/RSiena/man/siena07.Rd 2010-01-12 18:28:21 UTC (rev 38)
@@ -62,15 +62,16 @@
objects
\code{\link{sienaModelCreate}}, \code{\link{print.sienaFit}}}
\examples{
-mymodel <- sienaModelCreate(fn=simstats0c)
-mynet1 <- sienaNet(array(c(s501, s502, s503), dim=c(50, 50, 3)))
+mymodel <- sienaModelCreate(fn=simstats0c, nsub=2, n3=100)
+mynet1 <- sienaNet(array(c(tmp3, tmp4), dim=c(32, 32, 2)))
mydata <- sienaDataCreate(mynet1)
myeff <- getEffects(mydata)
ans <- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE)
#or for conditional estimation
+\dontrun{
mymodel$condname <- 'mynet1'
mymodel$cconditional <- TRUE
-ans <- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE)
+ans <- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE)}
}
\keyword{models}
Modified: pkg/RSiena/man/sienaFit.Rd
===================================================================
--- pkg/RSiena/man/sienaFit.Rd 2010-01-12 16:45:29 UTC (rev 37)
+++ pkg/RSiena/man/sienaFit.Rd 2010-01-12 18:28:21 UTC (rev 38)
@@ -51,7 +51,7 @@
derivative matrix of expected statistics \code{X} by parameters, and the
covariance matrix of the expected statistics \code{X}.
- The function \code{xtable.sientFit} creates an object of class
+ The function \code{xtable.sienaFit} creates an object of class
\code{xtable.sienaFit} which inherits from class \code{xtable} and
passes an extra arguments to the \code{print.xtable}.
}
@@ -62,8 +62,8 @@
\seealso{\code{\link{xtable}}, \code{\link{print.xtable}},
\code{\link{siena07}}}
\examples{
-mymodel <- sienaModelCreate(fn=simstats0c)
-mynet1 <- sienaNet(array(c(s501, s502, s503), dim=c(50, 50, 3)))
+mymodel <- sienaModelCreate(fn=simstats0c, nsub=2, n3=100)
+mynet1 <- sienaNet(array(c(tmp3, tmp4), dim=c(32, 32, 2)))
mydata <- sienaDataCreate(mynet1)
myeff <- getEffects(mydata)
ans <- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE)
Modified: pkg/RSiena/man/simstats0c.Rd
===================================================================
--- pkg/RSiena/man/simstats0c.Rd 2010-01-12 16:45:29 UTC (rev 37)
+++ pkg/RSiena/man/simstats0c.Rd 2010-01-12 18:28:21 UTC (rev 38)
@@ -58,12 +58,12 @@
\seealso{\code{\link{siena07}} }
\examples{
-mynet1 <- sienaNet(array(c(tmp3,tmp4),dim=c(32,32,2)))
+mynet1 <- sienaNet(array(c(tmp3, tmp4), dim=c(32, 32, 2)))
mydata <- sienaDataCreate(mynet1)
myeff<- getEffects(mydata)
myeff[myeff$effectName=='transitive triplets'&
myeff$type=='eval','include']<- TRUE
-mymodel<- sienaModelCreate(findiff=TRUE, fn = simstats0c)
+mymodel<- sienaModelCreate(fn = simstats0c, nsub=2, n3=100)
ans<- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE)
}
\keyword{models}
Modified: pkg/RSiena/tests/parallel.R
===================================================================
--- pkg/RSiena/tests/parallel.R 2010-01-12 16:45:29 UTC (rev 37)
+++ pkg/RSiena/tests/parallel.R 2010-01-12 18:28:21 UTC (rev 38)
@@ -1,95 +1,53 @@
-library(RSiena)
-print(packageDescription("RSiena",fields="Repository/R-Forge/Revision"))
+library(RSienaTest)
+print(packageDescription("RSienaTest",fields="Repository/R-Forge/Revision"))
-##test1
-print('test1')
-mynet1 <- sienaNet(array(c(s501, s502, s503), dim=c(50, 50, 3)))
-mydata <- sienaDataCreate(mynet1)
-myeff <- getEffects(mydata)
-mymodel<- model.create(findiff=TRUE, fn=simstats0c, projname='test1',
- cond=FALSE)
-ans <- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE,parallelTesting=TRUE, verbose=TRUE)#,dll='../siena/src/RSiena.dll')
-##test2
-print('test2')
-mymodel2 <- mymodel
-mymodel2$cconditional <- TRUE
-mymodel2$condvarno <- 1
-mymodel2$projname <- 'test2'
-ans <- siena07(mymodel2, data=mydata, effects=myeff, batch=TRUE,parallelTesting=TRUE, verbose=TRUE)#,dll='../siena/src/RSiena.dll')
##test3
-mynet1 <- sienaNet(array(c(tmp3,tmp4),dim=c(32,32,2)))
+mynet1 <- sienaNet(array(c(tmp3, tmp4),dim=c(32, 32, 2)))
mydata <- sienaDataCreate(mynet1)
myeff<- getEffects(mydata)
mymodel<- model.create(findiff=TRUE, fn = simstats0c, projname='test3',
- cond=FALSE)
+ cond=FALSE, nsub=2, n3=100)
print('test3')
-ans<- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE, parallelTesting=TRUE, verbose=TRUE)#,dll='../siena/src/RSiena.dll')
+system.time(ans<- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE, parallelTesting=TRUE, verbose=TRUE))#,dll='../siena/src/RSiena.dll')
##test4
mymodel$projname <- 'test4'
mymodel$cconditional <- TRUE
mymodel$condvarno<- 1
print('test4')
-ans<- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE, parallelTesting=TRUE, verbose=TRUE)#,dll='../siena/src/RSiena.dll')
-##test5
-mynet1 <- sienaNet(array(c(s501, s502, s503), dim=c(50, 50, 3)))
-mydata <- sienaDataCreate(mynet1)
-myeff <- getEffects(mydata)
-mymodel<- model.create(fn=simstats0c, projname='test5',
- cond=FALSE)
-print('test5')
-ans <- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE,parallelTesting=TRUE, verbose=TRUE)#,dll='../siena/src/RSiena.dll')
-##test6
-mymodel2 <- mymodel
-mymodel2$cconditional <- TRUE
-mymodel2$condvarno <- 1
-mymodel2$projname <- 'test6'
-print('test6')
-ans <- siena07(mymodel2, data=mydata, effects=myeff, batch=TRUE,parallelTesting=TRUE, verbose=TRUE)#,dll='../siena/src/RSiena.dll')
+system.time(ans<- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE, parallelTesting=TRUE, verbose=TRUE))#,dll='../siena/src/RSiena.dll')
##test7
mynet1 <- sienaNet(array(c(tmp3,tmp4),dim=c(32,32,2)))
mydata <- sienaDataCreate(mynet1)
myeff<- getEffects(mydata)
-mymodel<- model.create(fn = simstats0c, projname='test7',
+mymodel<- model.create(fn = simstats0c, projname='test7', nsub=2, n3=100,
cond=FALSE)
print('test7')
-ans<- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE, parallelTesting=TRUE, verbose=TRUE)#,dll='../siena/src/RSiena.dll')
+system.time(ans<- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE, parallelTesting=TRUE, verbose=TRUE))#,dll='../siena/src/RSiena.dll')
##test8
mymodel$projname <- 'test8'
mymodel$cconditional <- TRUE
mymodel$condvarno<- 1
print('test8')
-ans<- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE, parallelTesting=TRUE, verbose=TRUE)#,dll='../siena/src/RSiena.dll')
-##test9
+system.time(ans<- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE, parallelTesting=TRUE, verbose=TRUE))#,dll='../siena/src/RSiena.dll')
-print('test9')
mynet1 <- sienaNet(array(c(s501, s502, s503), dim=c(50, 50, 3)))
mynet2 <- sienaNet(s50a,type='behavior')
mydata <- sienaDataCreate(mynet1, mynet2)
myeff <- getEffects(mydata)
-myeff$initialValue[96] <- 0.34699930338 ## siena3 starting values differ
-mymodel<- model.create(findiff=FALSE, fn=simstats0c, projname='test9',
- cond=FALSE)
-ans<- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE, parallelTesting=TRUE, verbose=TRUE)
+myeff$initialValue[94] <- 0.34699930338 ## siena3 starting values differ
##test10
print('test10')
mymodel$projname <- 'test10'
mymodel$cconditional <- TRUE
mymodel$condvarno<- 1
-ans<- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE, parallelTesting=TRUE, verbose=TRUE)
+system.time(ans<- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE, parallelTesting=TRUE, verbose=TRUE))
##test11
print('test11')
-data501 <- sienaDataCreateFromSession("s50.csv", modelName="s50")
-data501e <- sienaDataCreateFromSession("s50e.csv", modelName="s50e")
-data501paj <- sienaDataCreateFromSession("s50paj.csv", modelName="s50paj")
+system.time(data501 <- sienaDataCreateFromSession("s50.csv", modelName="s50"))
+system.time(data501e <- sienaDataCreateFromSession("s50e.csv", modelName="s50e"))
+system.time(data501paj <- sienaDataCreateFromSession("s50paj.csv", modelName="s50paj"))
-model501 <- model.create( projname="s50", cond=FALSE)
-model501e <- model.create( projname="s50e", cond=FALSE )
-model501paj <- model.create(projname="s50paj", cond=FALSE )
-ans501 <- siena07(model501, data=data501$mydata, effects=data501$myeff,
- parallelTesting=TRUE, batch=TRUE, verbose=TRUE)
-ans501e <- siena07(model501e, data=data501e$mydata, effects=data501e$myeff,
- parallelTesting=TRUE, batch=TRUE, verbose=TRUE)
-ans501paj <- siena07(model501paj, data=data501paj$mydata,
- effects=data501paj$myeff,
- parallelTesting=TRUE, batch=TRUE, verbose=TRUE)
+model501e <- model.create( projname="s50e", cond=FALSE, nsub=2, n3=100 )
+system.time(ans501e <- siena07(model501e, data=data501e$mydata, effects=data501e$myeff,
+ parallelTesting=TRUE, batch=TRUE, verbose=TRUE))
## compare with outputs in parallelchecked/
Modified: pkg/RSiena/tests/parallel.Rout.save
===================================================================
--- pkg/RSiena/tests/parallel.Rout.save 2010-01-12 16:45:29 UTC (rev 37)
+++ pkg/RSiena/tests/parallel.Rout.save 2010-01-12 18:28:21 UTC (rev 38)
@@ -1,6 +1,6 @@
-R version 2.9.2 Patched (2009-09-16 r49745)
-Copyright (C) 2009 The R Foundation for Statistical Computing
+R version 2.10.1 Patched (2010-01-11 r50955)
+Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
@@ -15,545 +15,59 @@
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
-> library(RSiena)
-> print(packageDescription("RSiena",fields="Repository/R-Forge/Revision"))
+> library(RSienaTest)
+Loading required package: xtable
+> print(packageDescription("RSienaTest",fields="Repository/R-Forge/Revision"))
[1] NA
>
-> ##test1
-> print('test1')
-[1] "test1"
-> mynet1 <- sienaNet(array(c(s501, s502, s503), dim=c(50, 50, 3)))
+> ##test3
+> mynet1 <- sienaNet(array(c(tmp3, tmp4),dim=c(32, 32, 2)))
> mydata <- sienaDataCreate(mynet1)
-> myeff <- getEffects(mydata)
-> mymodel<- model.create(findiff=TRUE, fn=simstats0c, projname='test1')
-> ans <- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE,parallelTesting=TRUE, verbose=TRUE)#,dll='../siena/src/RSiena.dll')
+> myeff<- getEffects(mydata)
+> mymodel<- model.create(findiff=TRUE, fn = simstats0c, projname='test3',
++ cond=FALSE, nsub=2, n3=100)
+> print('test3')
+[1] "test3"
+> system.time(ans<- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE, parallelTesting=TRUE, verbose=TRUE))#,dll='../siena/src/RSiena.dll')
Stochastic approximation algorithm.
Initial value for gain parameter = 0.2.
Start of the algorithm.
Target function values are
- 1. 115.0000 2. 106.0000 3. 238.0000 4. 160.0000
+ 1. 51.0000 2. 129.0000 3. 58.0000
Start phase 0
-theta: 4.70 4.33 -1.47 0.00
+theta: 4.81 -0.56 0.00
Current parameter values:
- 4.696042 4.328845 -1.467705 0.000000
+ 4.8094118 -0.5603907 0.0000000
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: 0%
-Phase 1 Iteration 5 Progress: 0%
-Phase 1 Iteration 6 Progress: 0%
-Phase 1 Iteration 7 Progress: 1%
-Phase 1 Iteration 8 Progress: 1%
-Phase 1 Iteration 9 Progress: 1%
-Phase 1 Iteration 10 Progress: 1%
-Phase 1 Iteration 11 Progress: 1%
-Phase 1 Iteration 12 Progress: 1%
-Phase 1 Iteration 13 Progress: 1%
-Phase 1 Iteration 14 Progress: 1%
-Phase 1 Iteration 15 Progress: 1%
-Phase 1 Iteration 16 Progress: 1%
-Phase 1 Iteration 17 Progress: 1%
-Phase 1 Iteration 18 Progress: 1%
-Phase 1 Iteration 19 Progress: 1%
-Time per iteration in phase 1 = 0.07556
+Phase 1 Iteration 2 Progress: 1%
+Phase 1 Iteration 3 Progress: 1%
+Phase 1 Iteration 4 Progress: 2%
+Phase 1 Iteration 5 Progress: 2%
+Phase 1 Iteration 6 Progress: 3%
+Phase 1 Iteration 7 Progress: 3%
+Phase 1 Iteration 8 Progress: 3%
+Phase 1 Iteration 9 Progress: 4%
+Phase 1 Iteration 10 Progress: 4%
+Phase 1 Iteration 11 Progress: 5%
+Phase 1 Iteration 12 Progress: 5%
+Phase 1 Iteration 13 Progress: 5%
+Phase 1 Iteration 14 Progress: 6%
+Phase 1 Iteration 15 Progress: 6%
+Phase 1 Iteration 16 Progress: 7%
+Time per iteration in phase 1 = 0.0200
Average deviations NR generated statistics and targets
after phase 1:
- 28.105263
- 30.421053
- 23.368421
- -123.684211
+ 32.437500
+ 8.687500
+ -25.875000
Diagonal values of derivative matrix :
- 17.1477 16.5353 224.2105 60.0000
+ 18.1935 90.6250 31.2500
dfra :
- 17.147698 0.000000 29.473684 -9.473684
- 0.000000 16.535344 31.578947 -9.473684
- -3.026064 -2.431668 224.210526 30.526316
- -5.155517 -5.836004 66.315789 60.000000
-
-inverse of dfra :
- 0.0610880666 0.0040772223 -0.0137112006 0.0172651301
- 0.0028583933 0.0647268885 -0.0148890663 0.0182464993
- 0.0001212103 -0.0001740369 0.0052631476 -0.0026860828
- 0.0053930667 0.0068384658 -0.0084435127 0.0228937835
-
-Full Quasi-Newton-Raphson step after phase 1:
-1. 0.614904
-2. 0.555342
-3. -0.453330
-4. 2.669304
-This step is multiplied by the factor 0.10000.
-Phase 1 achieved after 19 iterations.
-theta: 4.758 4.384 -1.513 0.267
-Current parameter values:
- 4.7575328 4.3843796 -1.5130376 0.2669304
-
-Phase 2 has 4 subphases.
-Each subphase can be repeated up to 4 times
-
-Start phase 2.1
-Phase 2 Subphase 1 Iteration 1 Progress: 1%
-Phase 2 Subphase 1 Iteration 2 Progress: 1%
-theta 4.412 3.979 -1.528 0.599
-ac 3.17 3.07 0.75 1.52
-Phase 2 Subphase 1 Iteration 3 Progress: 1%
-Phase 2 Subphase 1 Iteration 4 Progress: 2%
-theta 4.17 3.69 -1.59 1.12
-ac 2.625 3.005 0.854 1.502
-Phase 2 Subphase 1 Iteration 5 Progress: 2%
-Phase 2 Subphase 1 Iteration 6 Progress: 2%
-theta 4.09 3.60 -1.67 1.46
-ac 2.568 2.634 0.908 1.495
-Phase 2 Subphase 1 Iteration 7 Progress: 2%
-Phase 2 Subphase 1 Iteration 8 Progress: 2%
-theta 4.14 3.46 -1.76 1.67
-ac 1.81 2.40 0.86 1.48
-Phase 2 Subphase 1 Iteration 9 Progress: 2%
-Phase 2 Subphase 1 Iteration 10 Progress: 2%
-theta 4.58 3.86 -1.83 1.81
-ac 1.809 2.397 0.848 1.458
-Phase 2 Subphase 1 Iteration 200 Progress: 5%
-theta 5.74 4.62 -2.38 2.81
-ac -0.024 -0.116 0.201 0.300
-Intervention 2.1.1: changes truncated, iterations: 1
-Warning: an autocorrelation is positive at the end of this subphase.
-Autocorrelations:
--0.08107265
--0.06697238
- 0.19914922
- 0.29846032
-
-Time per iteration in phase 2.1 = 0.01555
-theta 5.63 4.41 -2.29 2.67
-ac -0.081 -0.067 0.199 0.298
-Phase 2.1 ended after 227 iterations.
-Warning. Autocorrelation criterion not satisfied.
-theta: 5.63 4.41 -2.29 2.67
-Current parameter values:
- 5.634409 4.408576 -2.291103 2.672469
-
-Start phase 2.2
-Phase 2 Subphase 2 Iteration 1 Progress: 5%
-Phase 2 Subphase 2 Iteration 2 Progress: 5%
-Phase 2 Subphase 2 Iteration 3 Progress: 5%
-Phase 2 Subphase 2 Iteration 4 Progress: 5%
-Phase 2 Subphase 2 Iteration 5 Progress: 5%
-Phase 2 Subphase 2 Iteration 6 Progress: 5%
-Phase 2 Subphase 2 Iteration 7 Progress: 5%
-Phase 2 Subphase 2 Iteration 8 Progress: 5%
-Phase 2 Subphase 2 Iteration 9 Progress: 5%
-Phase 2 Subphase 2 Iteration 10 Progress: 5%
-Time per iteration in phase 2.2 = 0.01629
-theta 5.88 4.52 -2.34 2.80
-ac -0.17139 -0.04297 -0.00407 -0.12345
-Phase 2.2 ended after 140 iterations.
-theta: 5.88 4.52 -2.34 2.80
-Current parameter values:
- 5.884014 4.516232 -2.337510 2.797008
-
-Start phase 2.3
-Phase 2 Subphase 3 Iteration 1 Progress: 9%
-Phase 2 Subphase 3 Iteration 2 Progress: 9%
-Phase 2 Subphase 3 Iteration 3 Progress: 9%
-Phase 2 Subphase 3 Iteration 4 Progress: 9%
-Phase 2 Subphase 3 Iteration 5 Progress: 9%
-Phase 2 Subphase 3 Iteration 6 Progress: 9%
-Phase 2 Subphase 3 Iteration 7 Progress: 9%
-Phase 2 Subphase 3 Iteration 8 Progress: 9%
-Phase 2 Subphase 3 Iteration 9 Progress: 9%
-Phase 2 Subphase 3 Iteration 10 Progress: 9%
-Phase 2 Subphase 3 Iteration 200 Progress: 12%
-theta 5.88 4.50 -2.36 2.87
-ac -0.0113 0.0289 -0.0913 -0.2268
-Time per iteration in phase 2.3 = 0.0162
-theta 5.88 4.56 -2.36 2.82
-ac 0.0267 0.0707 -0.0909 -0.1898
-Phase 2.3 ended after 371 iterations.
-theta: 5.88 4.56 -2.36 2.82
-Current parameter values:
- 5.876732 4.557273 -2.359675 2.820789
-
-Start phase 2.4
-Phase 2 Subphase 4 Iteration 1 Progress: 15%
-Phase 2 Subphase 4 Iteration 2 Progress: 15%
-Phase 2 Subphase 4 Iteration 3 Progress: 15%
-Phase 2 Subphase 4 Iteration 4 Progress: 15%
-Phase 2 Subphase 4 Iteration 5 Progress: 15%
-Phase 2 Subphase 4 Iteration 6 Progress: 15%
-Phase 2 Subphase 4 Iteration 7 Progress: 15%
-Phase 2 Subphase 4 Iteration 8 Progress: 15%
-Phase 2 Subphase 4 Iteration 9 Progress: 15%
-Phase 2 Subphase 4 Iteration 10 Progress: 15%
-Phase 2 Subphase 4 Iteration 200 Progress: 18%
-theta 5.91 4.56 -2.36 2.81
-ac 0.0513 0.1045 0.0121 -0.0514
-Phase 2 Subphase 4 Iteration 400 Progress: 21%
-theta 5.99 4.58 -2.35 2.82
-ac 0.01805 0.05127 0.04037 -0.00067
-Phase 2 Subphase 4 Iteration 600 Progress: 24%
-theta 5.88 4.56 -2.35 2.83
-ac 0.0408 0.0264 0.0433 0.0345
-Warning: an autocorrelation is positive at the end of this subphase.
-Autocorrelations:
-0.03663243
-0.02358606
-0.03208522
-0.05905858
-
-Time per iteration in phase 2.4 = 0.01498
-theta 5.89 4.51 -2.36 2.82
-ac 0.0366 0.0236 0.0321 0.0591
-Phase 2.4 ended after 630 iterations.
-Warning. Autocorrelation criterion not satisfied.
-theta: 5.89 4.51 -2.36 2.82
-Current parameter values:
- 5.891445 4.509223 -2.356034 2.819908
-
-Start phase 3
-Simulated values, phase 3.
-Phase 3 Iteration 50 Progress 28%
-Phase 3 Iteration 100 Progress 32%
-Phase 3 Iteration 150 Progress 36%
-Phase 3 Iteration 200 Progress 39%
-Phase 3 Iteration 250 Progress 43%
-Phase 3 Iteration 300 Progress 47%
-Phase 3 Iteration 350 Progress 51%
-Phase 3 Iteration 400 Progress 54%
-Phase 3 Iteration 450 Progress 58%
-Phase 3 Iteration 500 Progress 62%
-Phase 3 Iteration 550 Progress 66%
-Phase 3 Iteration 600 Progress 70%
-Phase 3 Iteration 650 Progress 73%
-Phase 3 Iteration 700 Progress 77%
-Phase 3 Iteration 750 Progress 81%
-Phase 3 Iteration 800 Progress 85%
-Phase 3 Iteration 850 Progress 89%
-Phase 3 Iteration 900 Progress 92%
-Phase 3 Iteration 950 Progress 96%
-Phase 3 Iteration 1000 Progress 100%
-Time per iteration in phase 3 = 0.08661
-dfrac :
- 9.7571520 0.0000000 25.1200000 -1.6600000
- 0.0000000 12.3589538 24.3000000 4.5500000
- 0.4003371 1.0164373 221.7000000 93.3500000
- -0.0809192 1.4969350 169.8200000 114.4000000
-
-inverse of dfra :
- 0.1041458815 -0.0007574245 -0.0343988346 0.0296106613
- 0.0008034366 0.0810137195 -0.0173662475 0.0109603189
--0.0005823038 0.0002040916 0.0121798651 -0.0099552941
- 0.0009275484 -0.0013635699 -0.0178773783 0.0233968290
-
-A full Quasi-Newton-Raphson step after phase 3
-would add the following numbers to the parameters, yielding the following results:
- change new value
- 1. -0.041395 5.850050
- 2. 0.029326 4.538549
- 3. -0.002098 -2.358132
- 4. 0.003855 2.823763
-
-unconditional moment estimation.
-Information for convergence diagnosis.
-Averages, standard deviations, and t-ratios for deviations from targets:
- 1. 0.4630 9.4253 0.0491
- 2. -0.3290 8.6415 -0.0381
- 3. 0.0920 15.3955 0.0060
- 4. -0.1320 14.2377 -0.0093
-
-Total of 2387 iteration steps.
-
- at 3
-Estimates and standard errors
-
- 1. rate: constant mynet1 rate (period 1) 5.8914 ( 0.9350)
- 2. rate: constant mynet1 rate (period 2) 4.5092 ( 0.6868)
- 3. eval: outdegree (density) -2.3560 ( 0.0985)
- 4. eval: reciprocity 2.8199 ( 0.1744)
-
-Derivative matrix of expected statistics X by parameters:
-
- 9.7571520 0.0000000 25.1200000 -1.6600000
- 0.0000000 12.3589538 24.3000000 4.5500000
- 0.4003371 1.0164373 221.7000000 93.3500000
- -0.0809192 1.4969350 169.8200000 114.4000000
-
-Covariance matrix of X (correlations below the diagonal):
- 88.835 20.485 29.754 7.931
- 0.252 74.675 21.098 8.005
- 0.205 0.159 237.023 186.897
- 0.059 0.065 0.853 202.713
-
-
-> ##test2
-> print('test2')
-[1] "test2"
-> mymodel2 <- mymodel
-> mymodel2$cconditional <- TRUE
-> mymodel2$condvarno <- 1
-> mymodel2$projname <- 'test2'
-> ans <- siena07(mymodel2, data=mydata, effects=myeff, batch=TRUE,parallelTesting=TRUE, verbose=TRUE)#,dll='../siena/src/RSiena.dll')
-
-Stochastic approximation algorithm.
-Initial value for gain parameter = 0.2.
-Start of the algorithm.
-Target function values are
- 1. 238.0000 2. 160.0000
-
-Start phase 0
-theta: -1.47 0.00
-Current parameter values:
--1.467705 0.000000
-
-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: 0%
-Phase 1 Iteration 5 Progress: 0%
-Phase 1 Iteration 6 Progress: 0%
-Phase 1 Iteration 7 Progress: 0%
-Phase 1 Iteration 8 Progress: 1%
-Phase 1 Iteration 9 Progress: 1%
-Phase 1 Iteration 10 Progress: 1%
-Phase 1 Iteration 11 Progress: 1%
-Phase 1 Iteration 12 Progress: 1%
-Phase 1 Iteration 13 Progress: 1%
-Time per iteration in phase 1 = 0.0325
-Average deviations NR generated statistics and targets
-after phase 1:
- 15.692308
- -107.692308
-
-Diagonal values of derivative matrix :
-210.7692 55.3846
-dfra :
-210.76923 60.00000
- 84.61538 55.38462
-
-inverse of dfra :
- 0.008396125 -0.009095802
--0.012827413 0.031951920
-
-Full Quasi-Newton-Raphson step after phase 1:
-1. -1.111302
-2. 3.642268
-This step is multiplied by the factor 0.10000.
-Phase 1 achieved after 13 iterations.
-theta: -1.579 0.364
-Current parameter values:
--1.5788348 0.3642268
-
-Phase 2 has 4 subphases.
-Each subphase can be repeated up to 4 times
-
-Start phase 2.1
-Phase 2 Subphase 1 Iteration 1 Progress: 1%
-Phase 2 Subphase 1 Iteration 2 Progress: 1%
-theta -1.579 0.711
-ac 0.00 1.45
-Phase 2 Subphase 1 Iteration 3 Progress: 1%
-Phase 2 Subphase 1 Iteration 4 Progress: 1%
-theta -1.64 1.18
-ac 0.00 1.30
-Phase 2 Subphase 1 Iteration 5 Progress: 1%
-Phase 2 Subphase 1 Iteration 6 Progress: 1%
-theta -1.68 1.52
-ac 0.378 1.111
-Phase 2 Subphase 1 Iteration 7 Progress: 1%
-Phase 2 Subphase 1 Iteration 8 Progress: 1%
-theta -1.75 1.77
-ac 0.625 1.070
-Phase 2 Subphase 1 Iteration 9 Progress: 1%
-Phase 2 Subphase 1 Iteration 10 Progress: 1%
-theta -1.83 1.89
-ac 0.736 1.041
-Phase 2 Subphase 1 Iteration 200 Progress: 6%
-theta -2.39 2.87
-ac 0.00234 0.09035
-Time per iteration in phase 2.1 = 0.01473
-theta -2.31 2.72
-ac -0.0383 0.0489
-Phase 2.1 ended after 222 iterations.
-theta: -2.31 2.72
-Current parameter values:
--2.314309 2.723011
-
-Start phase 2.2
-Phase 2 Subphase 2 Iteration 1 Progress: 6%
-Phase 2 Subphase 2 Iteration 2 Progress: 6%
-Phase 2 Subphase 2 Iteration 3 Progress: 6%
-Phase 2 Subphase 2 Iteration 4 Progress: 6%
-Phase 2 Subphase 2 Iteration 5 Progress: 6%
-Phase 2 Subphase 2 Iteration 6 Progress: 6%
-Phase 2 Subphase 2 Iteration 7 Progress: 6%
-Phase 2 Subphase 2 Iteration 8 Progress: 6%
-Phase 2 Subphase 2 Iteration 9 Progress: 6%
-Phase 2 Subphase 2 Iteration 10 Progress: 6%
-Phase 2 Subphase 2 Iteration 200 Progress: 11%
-theta -2.39 2.88
-ac 0.1031 0.0382
-Warning: an autocorrelation is positive at the end of this subphase.
-Autocorrelations:
-0.106880509
-0.001288962
-
-Time per iteration in phase 2.2 = 0.01447
-theta -2.38 2.85
-ac 0.10688 0.00129
-Phase 2.2 ended after 255 iterations.
-Warning. Autocorrelation criterion not satisfied.
-theta: -2.38 2.85
-Current parameter values:
--2.375661 2.845126
-
-Start phase 2.3
-Phase 2 Subphase 3 Iteration 1 Progress: 12%
-Phase 2 Subphase 3 Iteration 2 Progress: 12%
-Phase 2 Subphase 3 Iteration 3 Progress: 12%
-Phase 2 Subphase 3 Iteration 4 Progress: 12%
-Phase 2 Subphase 3 Iteration 5 Progress: 12%
-Phase 2 Subphase 3 Iteration 6 Progress: 12%
-Phase 2 Subphase 3 Iteration 7 Progress: 12%
-Phase 2 Subphase 3 Iteration 8 Progress: 12%
-Phase 2 Subphase 3 Iteration 9 Progress: 12%
-Phase 2 Subphase 3 Iteration 10 Progress: 12%
-Time per iteration in phase 2.3 = 0.01471
-theta -2.38 2.89
-ac -0.256 -0.227
-Phase 2.3 ended after 138 iterations.
-theta: -2.38 2.89
-Current parameter values:
--2.381897 2.886809
-
-Start phase 2.4
-Phase 2 Subphase 4 Iteration 1 Progress: 19%
-Phase 2 Subphase 4 Iteration 2 Progress: 19%
-Phase 2 Subphase 4 Iteration 3 Progress: 19%
-Phase 2 Subphase 4 Iteration 4 Progress: 19%
-Phase 2 Subphase 4 Iteration 5 Progress: 20%
-Phase 2 Subphase 4 Iteration 6 Progress: 20%
-Phase 2 Subphase 4 Iteration 7 Progress: 20%
-Phase 2 Subphase 4 Iteration 8 Progress: 20%
-Phase 2 Subphase 4 Iteration 9 Progress: 20%
-Phase 2 Subphase 4 Iteration 10 Progress: 20%
-Phase 2 Subphase 4 Iteration 200 Progress: 24%
-theta -2.39 2.85
-ac -0.1250 -0.0958
-Time per iteration in phase 2.4 = 0.01441
-theta -2.38 2.87
-ac -0.1492 -0.0243
-Phase 2.4 ended after 347 iterations.
-theta: -2.38 2.87
-Current parameter values:
--2.384249 2.868362
-
-Start phase 3
-Simulated values, phase 3.
-Phase 3 Iteration 100 Progress 39%
-Phase 3 Iteration 200 Progress 45%
-Phase 3 Iteration 300 Progress 52%
-Phase 3 Iteration 400 Progress 59%
-Phase 3 Iteration 500 Progress 66%
-Phase 3 Iteration 600 Progress 73%
-Phase 3 Iteration 700 Progress 80%
-Phase 3 Iteration 800 Progress 86%
-Phase 3 Iteration 900 Progress 93%
-Phase 3 Iteration 1000 Progress 100%
-Time per iteration in phase 3 = 0.04675
-dfrac :
-206.42 98.96
-157.82 116.70
-
-inverse of dfra :
- 0.01377585 -0.01168173
--0.01862986 0.02436685
-
-A full Quasi-Newton-Raphson step after phase 3
-would add the following numbers to the parameters, yielding the following results:
- change new value
- 1. 0.002063 -2.382186
- 2. -0.000887 2.867475
-
-conditional moment estimation.
-Information for convergence diagnosis.
-Averages, standard deviations, and t-ratios for deviations from targets:
- 1. -0.3380 15.3236 -0.0221
- 2. -0.2220 14.3792 -0.0154
-
-Total of 1975 iteration steps.
-
- at 3
-Estimates and standard errors
-
-Rate parameters:
- 0.1 Rate parameter period 1 5.7636 ( 0.9461)
- 0.2 Rate parameter period 2 4.4706 ( 0.6605)
-
-Other parameters:
- 1. eval: outdegree (density) -2.3842 ( 0.1123)
- 2. eval: reciprocity 2.8684 ( 0.1859)
-
-Derivative matrix of expected statistics X by parameters:
-
- 206.42 98.96
-157.82 116.70
-
-Covariance matrix of X (correlations below the diagonal):
- 234.813 186.908
- 0.848 206.761
-
-
-> ##test3
-> mynet1 <- sienaNet(array(c(tmp3,tmp4),dim=c(32,32,2)))
-> mydata <- sienaDataCreate(mynet1)
-> myeff<- getEffects(mydata)
-> mymodel<- model.create(findiff=TRUE, fn = simstats0c, projname='test3')
-> print('test3')
-[1] "test3"
-> ans<- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE, parallelTesting=TRUE, verbose=TRUE)#,dll='../siena/src/RSiena.dll')
-
-Stochastic approximation algorithm.
-Initial value for gain parameter = 0.2.
-Start of the algorithm.
-Target function values are
- 1. 51.0000 2. 129.0000 3. 58.0000
-
-Start phase 0
-theta: 4.81 -0.56 0.00
-Current parameter values:
- 4.8094118 -0.5603907 0.0000000
-
-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: 0%
-Phase 1 Iteration 5 Progress: 0%
-Phase 1 Iteration 6 Progress: 0%
-Phase 1 Iteration 7 Progress: 1%
-Phase 1 Iteration 8 Progress: 1%
-Phase 1 Iteration 9 Progress: 1%
-Phase 1 Iteration 10 Progress: 1%
-Phase 1 Iteration 11 Progress: 1%
-Phase 1 Iteration 12 Progress: 1%
-Phase 1 Iteration 13 Progress: 1%
-Phase 1 Iteration 14 Progress: 1%
-Phase 1 Iteration 15 Progress: 1%
-Phase 1 Iteration 16 Progress: 1%
-Time per iteration in phase 1 = 0.02467
-Average deviations NR generated statistics and targets
-after phase 1:
- 32.437500
- 8.687500
- -25.875000
-
-Diagonal values of derivative matrix :
- 18.1935 90.6250 31.2500
-dfra :
18.193493 4.375000 1.875000
4.938234 90.625000 34.375000
-3.638699 50.000000 31.250000
@@ -573,28 +87,28 @@
Current parameter values:
4.6292124 -0.6194468 0.1563076
-Phase 2 has 4 subphases.
+Phase 2 has 2 subphases.
Each subphase can be repeated up to 4 times
Start phase 2.1
-Phase 2 Subphase 1 Iteration 1 Progress: 1%
-Phase 2 Subphase 1 Iteration 2 Progress: 1%
+Phase 2 Subphase 1 Iteration 1 Progress: 7%
+Phase 2 Subphase 1 Iteration 2 Progress: 7%
theta 4.244 -0.648 0.310
ac 1.46 1.30 1.00
-Phase 2 Subphase 1 Iteration 3 Progress: 1%
-Phase 2 Subphase 1 Iteration 4 Progress: 1%
+Phase 2 Subphase 1 Iteration 3 Progress: 7%
+Phase 2 Subphase 1 Iteration 4 Progress: 7%
theta 3.772 -0.694 0.540
ac 1.459 1.480 0.836
-Phase 2 Subphase 1 Iteration 5 Progress: 1%
-Phase 2 Subphase 1 Iteration 6 Progress: 1%
+Phase 2 Subphase 1 Iteration 5 Progress: 7%
+Phase 2 Subphase 1 Iteration 6 Progress: 7%
theta 3.42 -0.76 0.72
ac 1.25 1.94 0.65
-Phase 2 Subphase 1 Iteration 7 Progress: 1%
-Phase 2 Subphase 1 Iteration 8 Progress: 1%
+Phase 2 Subphase 1 Iteration 7 Progress: 7%
+Phase 2 Subphase 1 Iteration 8 Progress: 8%
theta 3.244 -0.783 0.912
ac 0.799 0.794 0.653
-Phase 2 Subphase 1 Iteration 9 Progress: 1%
-Phase 2 Subphase 1 Iteration 10 Progress: 1%
+Phase 2 Subphase 1 Iteration 9 Progress: 8%
+Phase 2 Subphase 1 Iteration 10 Progress: 8%
theta 2.969 -0.838 1.040
ac 0.792 0.780 0.653
Warning: an autocorrelation is positive at the end of this subphase.
@@ -603,7 +117,7 @@
0.1117977
0.1982289
-Time per iteration in phase 2.1 = 0.004667
+Time per iteration in phase 2.1 = 0.004089
theta 3.10 -1.09 1.67
ac 0.121 0.112 0.198
Phase 2.1 ended after 225 iterations.
@@ -613,17 +127,17 @@
3.102896 -1.092006 1.668237
Start phase 2.2
-Phase 2 Subphase 2 Iteration 1 Progress: 5%
-Phase 2 Subphase 2 Iteration 2 Progress: 5%
-Phase 2 Subphase 2 Iteration 3 Progress: 5%
-Phase 2 Subphase 2 Iteration 4 Progress: 5%
-Phase 2 Subphase 2 Iteration 5 Progress: 5%
-Phase 2 Subphase 2 Iteration 6 Progress: 5%
-Phase 2 Subphase 2 Iteration 7 Progress: 5%
-Phase 2 Subphase 2 Iteration 8 Progress: 5%
-Phase 2 Subphase 2 Iteration 9 Progress: 5%
-Phase 2 Subphase 2 Iteration 10 Progress: 5%
-Time per iteration in phase 2.2 = 0.004449
+Phase 2 Subphase 2 Iteration 1 Progress: 30%
+Phase 2 Subphase 2 Iteration 2 Progress: 31%
+Phase 2 Subphase 2 Iteration 3 Progress: 31%
+Phase 2 Subphase 2 Iteration 4 Progress: 31%
+Phase 2 Subphase 2 Iteration 5 Progress: 31%
+Phase 2 Subphase 2 Iteration 6 Progress: 31%
+Phase 2 Subphase 2 Iteration 7 Progress: 31%
+Phase 2 Subphase 2 Iteration 8 Progress: 31%
+Phase 2 Subphase 2 Iteration 9 Progress: 31%
+Phase 2 Subphase 2 Iteration 10 Progress: 31%
+Time per iteration in phase 2.2 = 0.004030
theta 3.03 -1.13 1.79
ac 0.0471 -0.0488 0.0117
Phase 2.2 ended after 263 iterations.
@@ -631,107 +145,63 @@
Current parameter values:
3.026403 -1.134321 1.792116
-Start phase 2.3
-Phase 2 Subphase 3 Iteration 1 Progress: 10%
-Phase 2 Subphase 3 Iteration 2 Progress: 10%
-Phase 2 Subphase 3 Iteration 3 Progress: 10%
-Phase 2 Subphase 3 Iteration 4 Progress: 10%
-Phase 2 Subphase 3 Iteration 5 Progress: 10%
-Phase 2 Subphase 3 Iteration 6 Progress: 10%
-Phase 2 Subphase 3 Iteration 7 Progress: 10%
-Phase 2 Subphase 3 Iteration 8 Progress: 10%
-Phase 2 Subphase 3 Iteration 9 Progress: 10%
-Phase 2 Subphase 3 Iteration 10 Progress: 10%
-Time per iteration in phase 2.3 = 0.004881
-theta 3.08 -1.15 1.78
-ac -0.0061 -0.1404 -0.1409
-Phase 2.3 ended after 168 iterations.
-theta: 3.08 -1.15 1.78
-Current parameter values:
- 3.077319 -1.153246 1.782989
-
-Start phase 2.4
-Phase 2 Subphase 4 Iteration 1 Progress: 17%
-Phase 2 Subphase 4 Iteration 2 Progress: 17%
-Phase 2 Subphase 4 Iteration 3 Progress: 17%
[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/rsiena -r 38
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