[Rsiena-commits] r40 - in pkg/RSiena: . R src src/win32 tests
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Fri Jan 15 16:45:34 CET 2010
Author: ripleyrm
Date: 2010-01-15 16:45:33 +0100 (Fri, 15 Jan 2010)
New Revision: 40
Modified:
pkg/RSiena/R/print07Report.r
pkg/RSiena/R/sienaprint.r
pkg/RSiena/changeLog
pkg/RSiena/src/Makefile.win
pkg/RSiena/src/win32/Makefile
pkg/RSiena/tests/parallel.R
pkg/RSiena/tests/parallel.Rout.save
Log:
Reverting a bug fix in revision 32. (sqrt of standard error of rate for conditioning variable.) Changes to Makefiles for 64 bit Windows.
Modified: pkg/RSiena/R/print07Report.r
===================================================================
--- pkg/RSiena/R/print07Report.r 2010-01-12 18:56:12 UTC (rev 39)
+++ pkg/RSiena/R/print07Report.r 2010-01-15 15:45:33 UTC (rev 40)
@@ -44,9 +44,9 @@
}
Report(format(round(z$rate[1], digits = 4), width = 9), outf)
Report(format(round(z$rate[1], digits = 4), width = 9), bof)
- Report(c(' (', format(round(sqrt(z$vrate[1]), digits = 4),
+ Report(c(' (', format(round(z$vrate[1], digits = 4),
width = 9), ')\n'), sep = '', outf)
- Report(c(' (', format(round(sqrt(z$vrate[1]), digits = 4),
+ Report(c(' (', format(round(z$vrate[1], digits = 4),
width = 9), ')\n'), sep = '', bof)
}
else ## observations > 2
@@ -58,14 +58,14 @@
tmp <- paste(' 0.', nnstr, ' Rate parameter period ',
1:nn, ' ',
format(round(z$rate,4),width=9),
- ' (',format(round(sqrt(z$vrate),4),width=9),
+ ' (',format(round(z$vrate,4),width=9),
')\n', sep = '')
} else{
tmp <- paste(' 0.', nnstr,
'Rate parameter cond. variable period ',
1:nn, ' ',
format(round(z$rate,4),width=9),
- ' (',format(round(sqrt(z$vrate),4),width=9),
+ ' (',format(round(z$vrate,4),width=9),
')\n', sep='')
}
Report(tmp, outf, sep='')
Modified: pkg/RSiena/R/sienaprint.r
===================================================================
--- pkg/RSiena/R/sienaprint.r 2010-01-12 18:56:12 UTC (rev 39)
+++ pkg/RSiena/R/sienaprint.r 2010-01-15 15:45:33 UTC (rev 40)
@@ -210,7 +210,7 @@
mydf[1, 'text'] <- 'Rate parameter of conditioning variable'
}
mydf[1, 'value'] <- x$rate[1]
- mydf[1, 'se'] <- sqrt(x$vrate[1])
+ mydf[1, 'se'] <- x$vrate[1]
}
else ## observations > 2
{
@@ -303,14 +303,36 @@
tmp <- sienaFitThetaTable(x)
mydf <- tmp$mydf
addtorow <- tmp$addtorow
+ ## find out whether the type is html or latex
+ dots <- substitute(list(...))[-1] ##first entry is the word 'list'
+ if (!is.null(dots[["type"]]))
+ {
+ type <- dots[["type"]]
+ }
+ else
+ {
+ type <- "latex"
+ }
if (!is.null(addtorow$command))
{
+ if (type =="latex")
+ {
use <- addtorow$command != 'Network Dynamics'
addtorow$command <- paste('\\multicolumn{4}{l}{', addtorow$command,
'} \\\\ \n')
use[1] <- FALSE
addtorow$command[use] <- paste('\\\\ ', addtorow$command[use])
}
+ else ##html
+ {
+ # use <- addtorow$command != 'Network Dynamics'
+ addtorow$command <- paste("<TR> <TD colspan=9 align=left>",
+ addtorow$command,
+ "</TD> </TR> <TR> </TR> \n")
+ # use[1] <- FALSE
+ # addtorow$command[use] <- paste('\\\\ ', addtorow$command[use])
+ }
+ }
else
{
addtorow <- NULL
@@ -320,7 +342,7 @@
mydf[mydf[,'row'] >= 1, 'row'] <- paste(format(mydf[mydf$row >= 1,
'row']), '.', sep='')
tmp <- list(xtable(mydf, caption=caption, label=label, align=align,
- digits=digits, display=display), addtorow=addtorow,
+ digits=digits, display=display), add.to.row=addtorow,
include.colnames=FALSE, include.rownames=FALSE, ...)
class(tmp) <- c("xtable.sienaFit", "xtable")
tmp
@@ -328,9 +350,10 @@
##@print.xtable.sienaFit Methods
print.xtable.sienaFit <- function(x, ...)
{
- addtorow <- x[["addtorow"]]
+ addtorow <- x[["add.to.row"]]
if (!is.null(addtorow))
{
+ x$add.to.row$pos <- lapply(x$add.to.row$pos, function(x)x-2)
do.call("print", x)
}
else
Modified: pkg/RSiena/changeLog
===================================================================
--- pkg/RSiena/changeLog 2010-01-12 18:56:12 UTC (rev 39)
+++ pkg/RSiena/changeLog 2010-01-15 15:45:33 UTC (rev 40)
@@ -1,3 +1,16 @@
+2010-01-15 R-forge revision 40
+
+ * R/print01report.r, R/sienaprint.r: remove extra sqrt roots in
+ standard error of rates for conditional estimation (see revision 32)
+ * src/Makefile.win, src/win32/Makefile: Changes to makefile for 64 bit
+ windows
+ * tests/parallel.R, tests/parallel.R.save: remove timers from
+ tests to reduce differences with output.
+
+2010-01-12 R-forge revision 39
+
+ * tests/parallel.r: fix bug: use library RSiena not RSienaTest
+
2010-01-12 R-forge revision 38
* man/RSiena-package.Rd, man/siena07.Rd, man/sienaFit.Rd,
@@ -2,3 +15,3 @@
man/simstats0c.Rd: reduce time for examples
- * tests/parallel.R, tests/patallel.Rout.save: reduce tests
+ * tests/parallel.R, tests/parallel.Rout.save: reduce tests
Modified: pkg/RSiena/src/Makefile.win
===================================================================
--- pkg/RSiena/src/Makefile.win 2010-01-12 18:56:12 UTC (rev 39)
+++ pkg/RSiena/src/Makefile.win 2010-01-15 15:45:33 UTC (rev 40)
@@ -1,6 +1,8 @@
#-*- Makefile -*-
#
+include $(R_HOME)/etc/Makeconf
+
#RHOME = d:/R/R-2.9.0/
#RHOME = c:/progra~1/R/R-2.9.0/
#RHOME = c:/R/R-2.9.0/
@@ -39,12 +41,12 @@
CXXFLAGS = -O2 -Wall -pedantic
DLLLIBS = $(PKG_LIBS) -L$(R_HOME)/bin -lR
-.cpp.o:
- $(CXX) $(CPPFLAGS) $(CXXFLAGS) -c $< -o $@
+#.cpp.o:
+# $(CXX) $(CPPFLAGS) $(CXXFLAGS) -c $< -o $@
ECHO = echo
-NM = nm
-DLL = g++
+#NM = nm
+DLL = $(CXX)
SED = sed
%.dll:
Modified: pkg/RSiena/src/win32/Makefile
===================================================================
--- pkg/RSiena/src/win32/Makefile 2010-01-12 18:56:12 UTC (rev 39)
+++ pkg/RSiena/src/win32/Makefile 2010-01-15 15:45:33 UTC (rev 40)
@@ -1,5 +1,7 @@
+include $(R_HOME)/etc/Makeconf
+
%.o: %.rc
- windres -i $< -o $@
+ $(RESCOMP) -i $< -o $@
all: siena.exe sienaC.exe
@@ -7,10 +9,10 @@
cp siena.exe sienaC.exe ../../inst
siena.exe: siena.o siena_rc.o
- gcc -s -mwindows -o $@ siena.o siena_rc.o
+ $(CC) -s -mwindows -o $@ siena.o siena_rc.o
sienaC.exe: siena.o siena_rc.o
- gcc -s -o $@ siena.o siena_rc.o
+ $(CC) -s -o $@ siena.o siena_rc.o
clean:
@rm -f *.exe *.o ../../inst/*.exe
Modified: pkg/RSiena/tests/parallel.R
===================================================================
--- pkg/RSiena/tests/parallel.R 2010-01-12 18:56:12 UTC (rev 39)
+++ pkg/RSiena/tests/parallel.R 2010-01-15 15:45:33 UTC (rev 40)
@@ -8,13 +8,13 @@
mymodel<- model.create(findiff=TRUE, fn = simstats0c, projname='test3',
cond=FALSE, nsub=2, n3=100)
print('test3')
-system.time(ans<- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE, parallelTesting=TRUE, verbose=TRUE))#,dll='../siena/src/RSiena.dll')
+ans<- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE, parallelTesting=TRUE, verbose=TRUE)#,dll='../siena/src/RSiena.dll')
##test4
mymodel$projname <- 'test4'
mymodel$cconditional <- TRUE
mymodel$condvarno<- 1
print('test4')
-system.time(ans<- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE, parallelTesting=TRUE, verbose=TRUE))#,dll='../siena/src/RSiena.dll')
+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)
@@ -22,32 +22,33 @@
mymodel<- model.create(fn = simstats0c, projname='test7', nsub=2, n3=100,
cond=FALSE)
print('test7')
-system.time(ans<- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE, parallelTesting=TRUE, verbose=TRUE))#,dll='../siena/src/RSiena.dll')
+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$condvarno<- 1
print('test8')
-system.time(ans<- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE, parallelTesting=TRUE, verbose=TRUE))#,dll='../siena/src/RSiena.dll')
+ans<- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE, parallelTesting=TRUE, verbose=TRUE)#,dll='../siena/src/RSiena.dll')
+##test9
mynet1 <- sienaNet(array(c(s501, s502, s503), dim=c(50, 50, 3)))
mynet2 <- sienaNet(s50a,type='behavior')
mydata <- sienaDataCreate(mynet1, mynet2)
myeff <- getEffects(mydata)
-myeff$initialValue[94] <- 0.34699930338 ## siena3 starting values differ
+myeff$initialValue[96] <- 0.34699930338 ## siena3 starting values differ
##test10
print('test10')
mymodel$projname <- 'test10'
mymodel$cconditional <- TRUE
mymodel$condvarno<- 1
-system.time(ans<- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE, parallelTesting=TRUE, verbose=TRUE))
+ans<- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE, parallelTesting=TRUE, verbose=TRUE)
##test11
print('test11')
-system.time(data501 <- sienaDataCreateFromSession("s50.csv", modelName="s50"))
-system.time(data501e <- sienaDataCreateFromSession("s50e.csv", modelName="s50e"))
-system.time(data501paj <- sienaDataCreateFromSession("s50paj.csv", modelName="s50paj"))
+data501 <- sienaDataCreateFromSession("s50.csv", modelName="s50")
+data501e <- sienaDataCreateFromSession("s50e.csv", modelName="s50e")
+data501paj <- sienaDataCreateFromSession("s50paj.csv", modelName="s50paj")
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))
+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 18:56:12 UTC (rev 39)
+++ pkg/RSiena/tests/parallel.Rout.save 2010-01-15 15:45:33 UTC (rev 40)
@@ -15,9 +15,9 @@
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
-> library(RSienaTest)
+> library(RSiena)
Loading required package: xtable
-> print(packageDescription("RSienaTest",fields="Repository/R-Forge/Revision"))
+> print(packageDescription("RSiena",fields="Repository/R-Forge/Revision"))
[1] NA
>
> ##test3
@@ -28,7 +28,7 @@
+ 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')
+> 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.
@@ -58,7 +58,7 @@
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
+Time per iteration in phase 1 = 0.01933
Average deviations NR generated statistics and targets
after phase 1:
32.437500
@@ -117,7 +117,7 @@
0.1117977
0.1982289
-Time per iteration in phase 2.1 = 0.004089
+Time per iteration in phase 2.1 = 0.004533
theta 3.10 -1.09 1.67
ac 0.121 0.112 0.198
Phase 2.1 ended after 225 iterations.
@@ -137,7 +137,7 @@
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
+Time per iteration in phase 2.2 = 0.003802
theta 3.03 -1.13 1.79
ac 0.0471 -0.0488 0.0117
Phase 2.2 ended after 263 iterations.
@@ -147,7 +147,7 @@
Start phase 3
Simulated values, phase 3.
-Time per iteration in phase 3 = 0.0152
+Time per iteration in phase 3 = 0.0147
dfrac :
12.246820 11.600000 5.800000
3.555528 44.000000 10.800000
@@ -193,15 +193,13 @@
0.229 0.574 31.507
- user system elapsed
- 3.96 0.02 4.00
> ##test4
> mymodel$projname <- 'test4'
> mymodel$cconditional <- TRUE
> mymodel$condvarno<- 1
> print('test4')
[1] "test4"
-> system.time(ans<- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE, parallelTesting=TRUE, verbose=TRUE))#,dll='../siena/src/RSiena.dll')
+> 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.
@@ -228,7 +226,7 @@
Phase 1 Iteration 11 Progress: 4%
Phase 1 Iteration 12 Progress: 4%
Phase 1 Iteration 13 Progress: 5%
-Time per iteration in phase 1 = 0.01167
+Time per iteration in phase 1 = 0.01083
Average deviations NR generated statistics and targets
after phase 1:
2.461538
@@ -312,7 +310,7 @@
Start phase 3
Simulated values, phase 3.
-Time per iteration in phase 3 = 0.0114
+Time per iteration in phase 3 = 0.0109
dfrac :
41.0 14.0
22.2 22.8
@@ -339,7 +337,7 @@
Estimates and standard errors
Rate parameters:
- 0. Rate parameter 3.0428 ( 0.7235)
+ 0. Rate parameter 3.0428 ( 0.5235)
1. eval: outdegree (density) -1.0952 ( 0.1923)
2. eval: reciprocity 1.7007 ( 0.3089)
@@ -353,8 +351,6 @@
0.571 32.750
- user system elapsed
- 2.53 0.00 2.55
> ##test7
> mynet1 <- sienaNet(array(c(tmp3,tmp4),dim=c(32,32,2)))
> mydata <- sienaDataCreate(mynet1)
@@ -363,7 +359,7 @@
+ cond=FALSE)
> print('test7')
[1] "test7"
-> system.time(ans<- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE, parallelTesting=TRUE, verbose=TRUE))#,dll='../siena/src/RSiena.dll')
+> 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.
@@ -384,7 +380,7 @@
Phase 1 Iteration 5 Progress: 1%
Phase 1 Iteration 10 Progress: 2%
Phase 1 Iteration 15 Progress: 2%
-Time per iteration in phase 1 = 0.005333
+Time per iteration in phase 1 = 0.006667
Average deviations NR generated statistics and targets
after phase 1:
32.437500
@@ -443,7 +439,7 @@
0.03195180
-0.00410509
-Time per iteration in phase 2.1 = 0.004044
+Time per iteration in phase 2.1 = 0.003867
theta 3.12 -1.11 1.73
ac 0.14682 0.03195 -0.00411
Phase 2.1 ended after 225 iterations.
@@ -463,7 +459,7 @@
Phase 2 Subphase 2 Iteration 8 Progress: 46%
Phase 2 Subphase 2 Iteration 9 Progress: 46%
Phase 2 Subphase 2 Iteration 10 Progress: 46%
-Time per iteration in phase 2.2 = 0.003968
+Time per iteration in phase 2.2 = 0.003651
theta 3.11 -1.13 1.75
ac -0.112 -0.219 -0.157
Phase 2.2 ended after 63 iterations.
@@ -473,7 +469,7 @@
Start phase 3
Simulated values, phase 3.
-Time per iteration in phase 3 = 0.0043
+Time per iteration in phase 3 = 0.0041
dfrac :
18.4056484 3.7142613 -0.3794248
2.1526407 49.1035790 18.8351756
@@ -519,15 +515,13 @@
0.101 0.660 35.489
- user system elapsed
- 1.81 0.01 1.83
> ##test8
> mymodel$projname <- 'test8'
> mymodel$cconditional <- TRUE
> mymodel$condvarno<- 1
> print('test8')
[1] "test8"
-> system.time(ans<- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE, parallelTesting=TRUE, verbose=TRUE))#,dll='../siena/src/RSiena.dll')
+> 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.
@@ -596,7 +590,7 @@
Phase 2 Subphase 1 Iteration 10 Progress: 8%
theta -0.922 1.368
ac 0.109 0.535
-Time per iteration in phase 2.1 = 0.004043
+Time per iteration in phase 2.1 = 0.00383
theta -1.11 1.66
ac -0.0414 0.0000
Phase 2.1 ended after 94 iterations.
@@ -625,7 +619,7 @@
Start phase 3
Simulated values, phase 3.
-Time per iteration in phase 3 = 0.0040
+Time per iteration in phase 3 = 0.0039
dfrac :
33.189028 8.787036
15.949957 19.294151
@@ -652,7 +646,7 @@
Estimates and standard errors
Rate parameters:
- 0. Rate parameter 3.1368 ( 0.6976)
+ 0. Rate parameter 3.1368 ( 0.4867)
1. eval: outdegree (density) -1.1224 ( 0.2040)
2. eval: reciprocity 1.7395 ( 0.2947)
@@ -666,21 +660,20 @@
0.586 30.010
- user system elapsed
- 1.24 0.00 1.25
+> ##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$initialValue[94] <- 0.34699930338 ## siena3 starting values differ
+> myeff$initialValue[96] <- 0.34699930338 ## siena3 starting values differ
> ##test10
> print('test10')
[1] "test10"
> mymodel$projname <- 'test10'
> mymodel$cconditional <- TRUE
> mymodel$condvarno<- 1
-> system.time(ans<- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE, parallelTesting=TRUE, verbose=TRUE))
+> ans<- siena07(mymodel, data=mydata, effects=myeff, batch=TRUE, parallelTesting=TRUE, verbose=TRUE)
Stochastic approximation algorithm.
Initial value for gain parameter = 0.2.
@@ -704,7 +697,7 @@
Phase 1 Iteration 15 Progress: 2%
Phase 1 Iteration 20 Progress: 3%
Phase 1 Iteration 25 Progress: 3%
-Time per iteration in phase 1 = 0.01083
+Time per iteration in phase 1 = 0.01125
Average deviations NR generated statistics and targets
after phase 1:
11.760000
@@ -769,7 +762,7 @@
Phase 2 Subphase 1 Iteration 10 Progress: 24%
theta -1.964 1.965 0.230 0.412 0.665 -0.217
ac 1.298 0.971 0.332 0.228 -0.775 0.825
-Time per iteration in phase 2.1 = 0.01440
+Time per iteration in phase 2.1 = 0.01434
theta -2.312 2.722 0.202 0.350 0.407 -0.212
ac -0.0102 -0.0819 -0.1969 -0.1784 -0.3442 -0.3264
Phase 2.1 ended after 166 iterations.
@@ -788,7 +781,7 @@
Phase 2 Subphase 2 Iteration 8 Progress: 53%
Phase 2 Subphase 2 Iteration 9 Progress: 53%
Phase 2 Subphase 2 Iteration 10 Progress: 53%
-Time per iteration in phase 2.2 = 0.01475
+Time per iteration in phase 2.2 = 0.01488
theta -2.355 2.815 0.200 0.367 0.356 -0.200
ac -0.1972 -0.3500 -0.0972 -0.3752 -0.3719 -0.1077
Phase 2.2 ended after 80 iterations.
@@ -799,7 +792,7 @@
Start phase 3
Simulated values, phase 3.
Phase 3 Iteration 100 Progress 100%
-Time per iteration in phase 3 = 0.0169
+Time per iteration in phase 3 = 0.0157
dfrac :
198.7730468 87.7900813 -28.0652717 -6.5802604 11.4180195 12.2382586
145.6055398 103.8763360 -0.4775313 -6.5941940 13.6390143 8.8744044
@@ -842,8 +835,8 @@
Estimates and standard errors
Rate parameters:
- 0.1 Rate parameter period 1 5.7677 ( 0.9338)
- 0.2 Rate parameter period 2 4.5201 ( 0.7804)
+ 0.1 Rate parameter period 1 5.7677 ( 0.8719)
+ 0.2 Rate parameter period 2 4.5201 ( 0.6091)
Other parameters:
1. eval: outdegree (density) -2.3554 ( 0.1091)
@@ -871,21 +864,15 @@
-0.006 0.035 -0.145 -0.062 0.155 257.690
- user system elapsed
- 5.95 0.00 5.97
> ##test11
> print('test11')
[1] "test11"
-> system.time(data501 <- sienaDataCreateFromSession("s50.csv", modelName="s50"))
- user system elapsed
- 0.54 0.00 0.56
-> system.time(data501e <- sienaDataCreateFromSession("s50e.csv", modelName="s50e"))
+> data501 <- sienaDataCreateFromSession("s50.csv", modelName="s50")
+> data501e <- sienaDataCreateFromSession("s50e.csv", modelName="s50e")
<sparse>[ <logic> ] : .M.sub.i.logical() maybe inefficient
<sparse>[ <logic> ] : .M.sub.i.logical() maybe inefficient
<sparse>[ <logic> ] : .M.sub.i.logical() maybe inefficient
- user system elapsed
- 0.81 0.02 0.83
-> system.time(data501paj <- sienaDataCreateFromSession("s50paj.csv", modelName="s50paj"))
+> data501paj <- sienaDataCreateFromSession("s50paj.csv", modelName="s50paj")
Loading required package: network
Classes for Relational Data
Version 1.4-1 created on July 26, 2008.
@@ -898,12 +885,10 @@
<sparse>[ <logic> ] : .M.sub.i.logical() maybe inefficient
<sparse>[ <logic> ] : .M.sub.i.logical() maybe inefficient
<sparse>[ <logic> ] : .M.sub.i.logical() maybe inefficient
- user system elapsed
- 0.72 0.00 0.73
>
> model501e <- model.create( projname="s50e", cond=FALSE, nsub=2, n3=100 )
-> system.time(ans501e <- siena07(model501e, data=data501e$mydata, effects=data501e$myeff,
-+ parallelTesting=TRUE, batch=TRUE, verbose=TRUE))
+> ans501e <- siena07(model501e, data=data501e$mydata, effects=data501e$myeff,
++ parallelTesting=TRUE, batch=TRUE, verbose=TRUE)
Stochastic approximation algorithm.
Initial value for gain parameter = 0.2.
@@ -992,7 +977,7 @@
0.1923869
0.2436313
-Time per iteration in phase 2.1 = 0.01326
+Time per iteration in phase 2.1 = 0.01282
theta 5.55 4.35 -2.27 2.65
ac 0.0352 0.1297 0.1924 0.2436
Phase 2.1 ended after 227 iterations.
@@ -1015,7 +1000,7 @@
Phase 2 Subphase 2 Iteration 200 Progress: 77%
theta 5.54 4.51 -2.36 2.69
ac -0.1015 0.0533 -0.1115 0.0278
-Time per iteration in phase 2.2 = 0.01366
+Time per iteration in phase 2.2 = 0.01299
theta 5.77 4.53 -2.33 2.75
ac -0.0641 0.0195 -0.1176 -0.0246
Phase 2.2 ended after 268 iterations.
@@ -1026,7 +1011,7 @@
Start phase 3
Simulated values, phase 3.
Phase 3 Iteration 100 Progress 100%
-Time per iteration in phase 3 = 0.0142
+Time per iteration in phase 3 = 0.0140
dfrac :
8.1662885 0.0000000 38.2604466 5.4507366
0.0000000 7.5602624 25.5047375 0.3465663
@@ -1079,7 +1064,5 @@
0.258 0.114 0.861 194.886
- user system elapsed
- 8.64 0.05 8.71
> ## compare with outputs in parallelchecked/
>
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