R : Copyright 2005, The R Foundation for Statistical Computing
Version 2.1.1  (2005-06-20), ISBN 3-900051-07-0

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> ### * <HEADER>
> ###
> attach(NULL, name = "CheckExEnv")
> assign(".CheckExEnv", as.environment(2), pos = length(search())) # base
> ## add some hooks to label plot pages for base and grid graphics
> setHook("plot.new", ".newplot.hook")
> setHook("persp", ".newplot.hook")
> setHook("grid.newpage", ".gridplot.hook")
> 
> assign("cleanEx",
+        function(env = .GlobalEnv) {
+ 	   rm(list = ls(envir = env, all.names = TRUE), envir = env)
+            RNGkind("default", "default")
+ 	   set.seed(1)
+    	   options(warn = 1)
+ 	   delayedAssign("T", stop("T used instead of TRUE"),
+ 		  assign.env = .CheckExEnv)
+ 	   delayedAssign("F", stop("F used instead of FALSE"),
+ 		  assign.env = .CheckExEnv)
+ 	   sch <- search()
+ 	   newitems <- sch[! sch %in% .oldSearch]
+ 	   for(item in rev(newitems))
+                eval(substitute(detach(item), list(item=item)))
+ 	   missitems <- .oldSearch[! .oldSearch %in% sch]
+ 	   if(length(missitems))
+ 	       warning("items ", paste(missitems, collapse=", "),
+ 		       " have been removed from the search path")
+        },
+        env = .CheckExEnv)
> assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now
> assign("ptime", proc.time(), env = .CheckExEnv)
> grDevices::postscript("deal-Examples.ps")
> assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv)
> options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly"))
> options(warn = 1)    
> library('deal')
Loading required package: dynamicGraph
Loading required package: tcltk
Loading Tcl/Tk interface ... done
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "autosearch"
> 
> ### * autosearch
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: autosearch
> ### Title: Greedy search
> ### Aliases: autosearch heuristic gettable
> ### Keywords: models
> 
> ### ** Examples
> 
> data(rats)
> fit       <- network(rats)
> fit.prior <- jointprior(fit,12)
Imaginary sample size: 12 
> fit       <- getnetwork(learn(fit,rats,fit.prior))
> fit       <- getnetwork(insert(fit,2,1,rats,fit.prior))
> fit       <- getnetwork(insert(fit,1,3,rats,fit.prior))
> hisc      <- autosearch(fit,rats,fit.prior,trace=FALSE)
[Autosearch (1) -170.0867 [Sex|Drug][Drug][W1|Sex:W2][W2]
(2) -166.3658 [Sex|Drug][Drug][W1|Sex:Drug:W2][W2]
(3) -163.5749 [Sex|Drug][Drug][W1|Drug:W2][W2]
(4) -161.4324 [Sex|Drug][Drug][W1|Drug][W2|W1]
(5) -160.2244 [Sex][Drug][W1|Drug][W2|W1]
Total 0.65 add 0.34 rem 0.14 turn 0.04 sort 0.02 choose 0.02 rest 0.09 ]
> hisc      <- autosearch(fit,rats,fit.prior,trace=FALSE,removecycles=TRUE) # slower
[Autosearch (1) -170.0867 [Sex|Drug][Drug][W1|Sex:W2][W2]
(2) -166.3658 [Sex|Drug][Drug][W1|Sex:Drug:W2][W2]
(3) -163.5749 [Sex|Drug][Drug][W1|Drug:W2][W2]
(4) -161.4324 [Sex|Drug][Drug][W1|Drug][W2|W1]
(5) -160.2244 [Sex][Drug][W1|Drug][W2|W1]
Total 0.78 add 0.33 rem 0.14 turn 0.05 sort 0.01 choose 0.13 rest 0.12 ]
> plot(getnetwork(hisc))
> 
> hisc2     <- heuristic(fit,rats,fit.prior,restart=10,trace=FALSE)
[Heuristic [Autosearch (1) -170.0867 [Sex|Drug][Drug][W1|Sex:W2][W2]
(2) -166.3658 [Sex|Drug][Drug][W1|Sex:Drug:W2][W2]
(3) -163.5749 [Sex|Drug][Drug][W1|Drug:W2][W2]
(4) -161.4324 [Sex|Drug][Drug][W1|Drug][W2|W1]
(5) -160.2244 [Sex][Drug][W1|Drug][W2|W1]
Total 0.66 add 0.31 rem 0.21 turn 0.05 sort 0 choose 0.01 rest 0.08 ]
[Perturb 0.03 ]
[Autosearch (1) -167.7038 [Sex][Drug|Sex][W1|Drug][W2]
(2) -161.4324 [Sex][Drug|Sex][W1|Drug][W2|W1]
(3) -160.2244 [Sex][Drug][W1|Drug][W2|W1]
Total 0.16 add 0.04 rem 0.01 turn 0.02 sort 0 choose 0.03 rest 0.06 ]
[Perturb 0.02 ]
[Perturb 0.01 ]
[Perturb 0.03 ]
[Perturb 0.01 ]
[Perturb 0.02 ]
[Perturb 0.03 ]
[Perturb 0.05 ]
[Perturb 0 ]
[Perturb 0.01 ]
[Autosearch (1) -167.1701 [Sex][Drug][W1|Drug][W2|Sex]
(2) -160.6097 [Sex][Drug][W1|Drug][W2|Sex:W1]
(3) -160.2244 [Sex][Drug][W1|Drug][W2|W1]
Total 0.25 add 0.16 rem 0 turn 0.01 sort 0.03 choose 0 rest 0.05 ]
Tried 63 out of approx. 144 networks
1.46 ]
Perturb: 0.24 ,Autosearch: 1.29 ,Unique: 0 
> plot(getnetwork(hisc2))
> print(modelstring(getnetwork(hisc2)))
[1] "[Sex][Drug][W1|Drug][W2|W1]"
> plot(makenw(gettable(hisc2),fit))
> 
> 
> 
> cleanEx(); ..nameEx <- "drawnetwork"
> 
> ### * drawnetwork
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: drawnetwork
> ### Title: Graphical interface for editing networks
> ### Aliases: drawnetwork
> ### Keywords: models
> 
> ### ** Examples
> 
> data(rats)
> rats.nw    <- network(rats)
> rats.prior <- jointprior(rats.nw,12)
Imaginary sample size: 12 
> rats.nw    <- getnetwork(learn(rats.nw,rats,rats.prior))
> 
> ## Not run: newrat  <- getnetwork(drawnetwork(rats.nw,rats,rats.prior))
> 
> 
> 
> cleanEx(); ..nameEx <- "genlatex"
> 
> ### * genlatex
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: genlatex
> ### Title: From a network family, generate LaTeX output
> ### Aliases: genlatex genpicfile
> ### Keywords: iplot
> 
> ### ** Examples
> 
> data(rats)
> allrats <- getnetwork(networkfamily(rats,network(rats)))
[networkfamily Imaginary sample size: 12 
Creating all (144 minus restrictions) networks with 2 discrete and 2 continuous nodes
Created 144 networks, 1.1 ]
> allrats <- nwfsort(allrats)
> 
> ## Not run: dir.create("c:/temp")
> ## Not run: genpicfile(allrats,outdir="c:/temp/pic/")
> ## Not run: genlatex(allrats,outdir="c:/temp/pic/",picdir="c:/temp/pic/")
> 
> ## LATEX FILE:
> #\documentclass{article}
> #\usepackage{array,pictex}
> #\begin{document}
> #\input{scoretable}
> #\input{picnice}
> #\end{document}
> 
> 
> 
> cleanEx(); ..nameEx <- "insert"
> 
> ### * insert
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: insert
> ### Title: Insert/remove an arrow in network
> ### Aliases: insert remover
> ### Keywords: iplot
> 
> ### ** Examples
> 
> data(rats)
> rats.nw    <- network(rats)
> rats.nw    <- getnetwork(insert(rats.nw,2,1,nocalc=TRUE))
> rats.prior <- jointprior(rats.nw,12)
Imaginary sample size: 12 
> 
> rats.nw2   <- network(rats)
> rats.nw2   <- getnetwork(learn(rats.nw2,rats,rats.prior))
> rats.nw2   <- getnetwork(insert(rats.nw2,1,2,rats,rats.prior))
> 
> rats.nw3   <- getnetwork(remover(rats.nw2,1,2,rats,rats.prior))
> 
> 
> 
> cleanEx(); ..nameEx <- "jointprior"
> 
> ### * jointprior
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: jointprior
> ### Title: Calculates the joint prior distribution
> ### Aliases: jointprior
> ### Keywords: models
> 
> ### ** Examples
> 
> data(rats)
> rats.nw    <- network(rats)
> rats.prior <- jointprior(rats.nw,12)
Imaginary sample size: 12 
> 
> ## Not run: savenet(rats.nw,file("rats.net"))
> ## Not run: rats.nw <- readnet(file("rats.net"))
> ## Not run: rats.nw <- prob(rats.nw,rats)
> ## Not run: rats.prior <- jointprior(rats.nw,12)
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "learn"
> 
> ### * learn
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: learn
> ### Title: Estimation of parameters in the local probability distributions
> ### Aliases: learn
> ### Keywords: iplot
> 
> ### ** Examples
> 
> data(rats)
> fit       <- network(rats)
> fit.prior <- jointprior(fit,12)
Imaginary sample size: 12 
> fit.learn <- learn(fit,rats,fit.prior,timetrace=TRUE)
[Learn.network 0.02 ]> fit.nw    <- getnetwork(fit.learn)
> fit.learn2<- learn(fit,rats,fit.prior,trylist=gettrylist(fit.learn),timetrace=TRUE)
[Learn.network 0 ]> 
> 
> 
> cleanEx(); ..nameEx <- "maketrylist"
> 
> ### * maketrylist
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: maketrylist
> ### Title: Creates the full trylist
> ### Aliases: maketrylist
> ### Keywords: models
> 
> ### ** Examples
> 
> data(rats)
> rats.nw <- network(rats)
> rats.pr <- jointprior(rats.nw,12)
Imaginary sample size: 12 
> rats.nw <- getnetwork(learn(rats.nw,rats,rats.pr))
> rats.tr <- maketrylist(rats.nw,rats,rats.pr)
Creating all (144 minus restrictions) networks with 2 discrete and 2 continuous nodes
Created 144 networks, > 
> rats.hi <- getnetwork(heuristic(rats.nw,rats,rats.pr,trylist=rats.tr))
[Heuristic [Autosearch (1) -166.4958 [Sex][Drug][W1|Drug][W2]
(2) -160.2244 [Sex][Drug][W1|Drug][W2|W1]
Total 0.13 add 0.04 rem 0.01 turn 0.01 sort 0.01 choose 0 rest 0.06 ]
[Perturb 0.02 ]
[Perturb 0.01 ]
[Perturb 0.02 ]
[Perturb 0.02 ]
[Perturb 0 ]
[Perturb 0 ]
[Perturb 0 ]
[Perturb 0.02 ]
[Perturb 0.04 ]
[Perturb 0.03 ]
Tried 25 out of approx. 144 networks
0.36 ]
Perturb: 0.19 ,Autosearch: 0.18 ,Unique: 0 
> 
> 
> 
> cleanEx(); ..nameEx <- "network"
> 
> ### * network
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: network
> ### Title: Bayesian network data structure
> ### Aliases: network plot.network print.network
> ### Keywords: models
> 
> ### ** Examples
> 
> A <- factor(rep(c("A1","A2"),50))
> B <- factor(rep(rep(c("B1","B2"),25),2))
> thisnet <- network( data.frame(A,B) )
> 
> set.seed(109)
> sex     <- gl(2,4,label=c("male","female"))
> age     <- gl(2,2,8)
> yield   <- rnorm(length(sex))
> weight  <- rnorm(length(sex))
> mydata  <- data.frame(sex,age,yield,weight)
> mynw    <- network(mydata)
> 
> # adjust prior probability distribution
> localprob(mynw,"sex")   <- c(0.4,0.6)
> localprob(mynw,"age")   <- c(0.6,0.4)
> localprob(mynw,"yield") <- c(2,0)
> localprob(mynw,"weight")<- c(1,0)
> 
> print(mynw)
##  4 ( 2 discrete+ 2 ) nodes;score=  ;relscore=  
1	sex	discrete(2) 
2	age	discrete(2) 
3	yield	continuous() 
4	weight	continuous() 
> plot(mynw)
> 
> prior <- jointprior(mynw)
Imaginary sample size: 8.333333 
> mynw  <- getnetwork(learn(mynw,mydata,prior))
> thebest <- getnetwork(autosearch(mynw,mydata,prior))
[Autosearch (1) -35.34196 [sex][age][yield][weight|age]
(2) -34.47202 [sex][age][yield][weight|age:yield]
(3) -34.19462 [sex][age][yield|sex][weight|age:yield]
Total 0.44 add 0.3 rem 0.01 turn 0.02 sort 0.04 choose 0 rest 0.07 ]
> 
> print(mynw,condposterior=TRUE)
##  4 ( 2 discrete+ 2 ) nodes;score= -35.8417032539780 ;relscore=  
1	sex	discrete(2) 
------------------------------------------------------------
Conditional Posterior: sex
[[1]]
[[1]]$alpha

  male female 
     8      8 
attr(,"class")
[1] "table"

[[1]]$nu
[1] NA

[[1]]$rho
[1] NA

[[1]]$mu
[1] NA

[[1]]$phi
[1] NA

[[1]]$tau
[1] NA


2	age	discrete(2) 
------------------------------------------------------------
Conditional Posterior: age
[[1]]
[[1]]$alpha

1 2 
8 8 
attr(,"class")
[1] "table"

[[1]]$nu
[1] NA

[[1]]$rho
[1] NA

[[1]]$mu
[1] NA

[[1]]$phi
[1] NA

[[1]]$tau
[1] NA


3	yield	continuous() 
------------------------------------------------------------
Conditional Posterior: yield
[[1]]
[[1]]$alpha
[1] NA

[[1]]$nu
[1] 8

[[1]]$rho
[1] 16

[[1]]$mu
[1] 0.0867827

[[1]]$phi
[1] 19.08751

[[1]]$tau
[1] 16


4	weight	continuous() 
------------------------------------------------------------
Conditional Posterior: weight
[[1]]
[[1]]$alpha
[1] NA

[[1]]$nu
[1] 8

[[1]]$rho
[1] 16

[[1]]$mu
[1] -0.06561016

[[1]]$phi
[1] 9.385794

[[1]]$tau
[1] 16


> 
> ## Not run: savenet(mynw,file("yield.net"))
> 
> 
> 
> cleanEx(); ..nameEx <- "networkfamily"
> 
> ### * networkfamily
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: networkfamily
> ### Title: Generates and learns all networks for a set of variables.
> ### Aliases: networkfamily print.networkfamily plot.networkfamily
> ### Keywords: iplot
> 
> ### ** Examples
> 
> data(rats)
> allrats <- getnetwork(networkfamily(rats))
[networkfamily Imaginary sample size: 12 
Creating all (144 minus restrictions) networks with 2 discrete and 2 continuous nodes
Created 144 networks, 1.07 ]
> plot(allrats)
> print(allrats)
Discrete:  Sex(2),Drug(3)
Continuous:W1,W2
  log(Score)	|Relscore	|Network
------------------------------------------------------------
1. -160.2244	1		[Sex][Drug][W1|Drug][W2|W1]
2. -160.6097	0.6802642	[Sex][Drug][W1|Drug][W2|SexW1]
3. -161.4324	0.2987972	[Sex|Drug][Drug][W1|Drug][W2|W1]
4. -161.4324	0.2987972	[Sex][Drug|Sex][W1|Drug][W2|W1]
5. -161.8177	0.2032610	[Sex|Drug][Drug][W1|Drug][W2|SexW1]
6. -161.8177	0.2032610	[Sex][Drug|Sex][W1|Drug][W2|SexW1]
7. -162.3077	0.1245122	[Sex][Drug][W1|Drug][W2|DrugW1]
8. -162.3077	0.1245122	[Sex][Drug][W1|DrugW2][W2|Drug]
9. -162.3669	0.1173555	[Sex][Drug][W1|DrugW2][W2]
10. -162.8150	0.07497692	[Sex][Drug][W1|SexDrug][W2|W1]
11. -163.0412	0.05979467	[Sex][Drug][W1|DrugW2][W2|Sex]
12. -163.2002	0.05100412	[Sex][Drug][W1|SexDrug][W2|SexW1]
13. -163.5157	0.03720389	[Sex|Drug][Drug][W1|Drug][W2|DrugW1]
14. -163.5157	0.03720389	[Sex][Drug|Sex][W1|Drug][W2|DrugW1]
15. -163.5157	0.03720389	[Sex|Drug][Drug][W1|DrugW2][W2|Drug]
16. -163.5157	0.03720389	[Sex][Drug|Sex][W1|DrugW2][W2|Drug]
17. -163.5749	0.0350655	[Sex|Drug][Drug][W1|DrugW2][W2]
18. -163.5749	0.0350655	[Sex][Drug|Sex][W1|DrugW2][W2]
19. -163.7908	0.02825631	[Sex][Drug][W1|DrugW2][W2|SexDrug]
20. -163.9911	0.02312842	[Sex][Drug][W1|Drug][W2|SexDrugW1]
21. -164.0230	0.02240289	[Sex|Drug][Drug][W1|SexDrug][W2|W1]
22. -164.0230	0.02240289	[Sex][Drug|Sex][W1|SexDrug][W2|W1]
23. -164.2492	0.01786648	[Sex|Drug][Drug][W1|DrugW2][W2|Sex]
24. -164.2492	0.01786648	[Sex][Drug|Sex][W1|DrugW2][W2|Sex]
25. -164.4082	0.01523989	[Sex|Drug][Drug][W1|SexDrug][W2|SexW1]
26. -164.4082	0.01523989	[Sex][Drug|Sex][W1|SexDrug][W2|SexW1]
27. -164.8983	0.009335542	[Sex][Drug][W1|SexDrug][W2|DrugW1]
28. -164.9988	0.008442904	[Sex|Drug][Drug][W1|DrugW2][W2|SexDrug]
29. -164.9988	0.008442904	[Sex][Drug|Sex][W1|DrugW2][W2|SexDrug]
30. -165.0986	0.00764135	[Sex][Drug][W1|SexDrugW2][W2|Drug]
31. -165.1578	0.007202143	[Sex][Drug][W1|SexDrugW2][W2]
32. -165.1991	0.006910706	[Sex|Drug][Drug][W1|Drug][W2|SexDrugW1]
33. -165.1991	0.006910706	[Sex][Drug|Sex][W1|Drug][W2|SexDrugW1]
34. -165.8321	0.003669616	[Sex][Drug][W1|SexDrugW2][W2|Sex]
35. -166.1063	0.002789433	[Sex|Drug][Drug][W1|SexDrug][W2|DrugW1]
36. -166.1063	0.002789433	[Sex][Drug|Sex][W1|SexDrug][W2|DrugW1]
37. -166.3066	0.002283214	[Sex|Drug][Drug][W1|SexDrugW2][W2|Drug]
38. -166.3066	0.002283214	[Sex][Drug|Sex][W1|SexDrugW2][W2|Drug]
39. -166.3658	0.002151980	[Sex|Drug][Drug][W1|SexDrugW2][W2]
40. -166.3658	0.002151980	[Sex][Drug|Sex][W1|SexDrugW2][W2]
41. -166.4366	0.002004747	[Sex][Drug][W1|Drug][W2|Drug]
42. -166.4958	0.001889518	[Sex][Drug][W1|Drug][W2]
43. -166.5817	0.001734098	[Sex][Drug][W1|SexDrug][W2|SexDrugW1]
44. -166.5817	0.001734098	[Sex][Drug][W1|SexDrugW2][W2|SexDrug]
45. -167.0401	0.001096471	[Sex|Drug][Drug][W1|SexDrugW2][W2|Sex]
46. -167.0401	0.001096471	[Sex][Drug|Sex][W1|SexDrugW2][W2|Sex]
47. -167.1701	0.0009627422	[Sex][Drug][W1|Drug][W2|Sex]
48. -167.6446	0.0005990126	[Sex|Drug][Drug][W1|Drug][W2|Drug]
49. -167.6446	0.0005990126	[Sex][Drug|Sex][W1|Drug][W2|Drug]
50. -167.7038	0.0005645827	[Sex|Drug][Drug][W1|Drug][W2]
51. -167.7038	0.0005645827	[Sex][Drug|Sex][W1|Drug][W2]
52. -167.7897	0.0005181435	[Sex|Drug][Drug][W1|SexDrug][W2|SexDrugW1]
53. -167.7897	0.0005181435	[Sex][Drug|Sex][W1|SexDrug][W2|SexDrugW1]
54. -167.7897	0.0005181435	[Sex|Drug][Drug][W1|SexDrugW2][W2|SexDrug]
55. -167.7897	0.0005181435	[Sex][Drug|Sex][W1|SexDrugW2][W2|SexDrug]
56. -167.9197	0.0004549493	[Sex][Drug][W1|Drug][W2|SexDrug]
57. -168.3781	0.0002876646	[Sex|Drug][Drug][W1|Drug][W2|Sex]
58. -168.3781	0.0002876646	[Sex][Drug|Sex][W1|Drug][W2|Sex]
59. -168.8195	0.0001850109	[Sex][Drug][W1|SexW2][W2|Drug]
60. -168.8787	0.0001743769	[Sex][Drug][W1|SexW2][W2]
61. -168.9201	0.0001672946	[Sex][Drug][W1|W2][W2|Drug]
62. -168.9793	0.0001576789	[Sex][Drug][W1][W2|W1]
63. -168.9793	0.0001576789	[Sex][Drug][W1|W2][W2]
64. -169.0272	0.0001503097	[Sex][Drug][W1|SexDrug][W2|Drug]
65. -169.0864	0.0001416703	[Sex][Drug][W1|SexDrug][W2]
66. -169.1277	0.0001359376	[Sex|Drug][Drug][W1|Drug][W2|SexDrug]
67. -169.1277	0.0001359376	[Sex][Drug|Sex][W1|Drug][W2|SexDrug]
68. -169.1677	0.0001306081	[Sex][Drug][W1|Sex][W2|W1]
69. -169.3646	0.0001072633	[Sex][Drug][W1][W2|SexW1]
70. -169.5530	8.884804e-05	[Sex][Drug][W1|Sex][W2|SexW1]
71. -169.5530	8.884804e-05	[Sex][Drug][W1|SexW2][W2|Sex]
72. -169.6536	8.03401e-05	[Sex][Drug][W1|W2][W2|Sex]
73. -169.7607	7.218345e-05	[Sex][Drug][W1|SexDrug][W2|Sex]
74. -170.0275	5.528073e-05	[Sex|Drug][Drug][W1|SexW2][W2|Drug]
75. -170.0275	5.528073e-05	[Sex][Drug|Sex][W1|SexW2][W2|Drug]
76. -170.0867	5.210332e-05	[Sex|Drug][Drug][W1|SexW2][W2]
77. -170.0867	5.210332e-05	[Sex][Drug|Sex][W1|SexW2][W2]
78. -170.1281	4.998714e-05	[Sex|Drug][Drug][W1|W2][W2|Drug]
79. -170.1281	4.998714e-05	[Sex][Drug|Sex][W1|W2][W2|Drug]
80. -170.1873	4.711399e-05	[Sex|Drug][Drug][W1][W2|W1]
81. -170.1873	4.711399e-05	[Sex][Drug|Sex][W1][W2|W1]
82. -170.1873	4.711399e-05	[Sex|Drug][Drug][W1|W2][W2]
83. -170.1873	4.711399e-05	[Sex][Drug|Sex][W1|W2][W2]
84. -170.2352	4.491212e-05	[Sex|Drug][Drug][W1|SexDrug][W2|Drug]
85. -170.2352	4.491212e-05	[Sex][Drug|Sex][W1|SexDrug][W2|Drug]
86. -170.2944	4.233068e-05	[Sex|Drug][Drug][W1|SexDrug][W2]
87. -170.2944	4.233068e-05	[Sex][Drug|Sex][W1|SexDrug][W2]
88. -170.3026	4.198564e-05	[Sex][Drug][W1|SexW2][W2|SexDrug]
89. -170.3757	3.902534e-05	[Sex|Drug][Drug][W1|Sex][W2|W1]
90. -170.3757	3.902534e-05	[Sex][Drug|Sex][W1|Sex][W2|W1]
91. -170.4032	3.796517e-05	[Sex][Drug][W1|W2][W2|SexDrug]
92. -170.5103	3.41107e-05	[Sex][Drug][W1|SexDrug][W2|SexDrug]
93. -170.5726	3.204997e-05	[Sex|Drug][Drug][W1][W2|SexW1]
94. -170.5726	3.204997e-05	[Sex][Drug|Sex][W1][W2|SexW1]
95. -170.7610	2.654754e-05	[Sex|Drug][Drug][W1|Sex][W2|SexW1]
96. -170.7610	2.654754e-05	[Sex][Drug|Sex][W1|Sex][W2|SexW1]
97. -170.7610	2.654754e-05	[Sex|Drug][Drug][W1|SexW2][W2|Sex]
98. -170.7610	2.654754e-05	[Sex][Drug|Sex][W1|SexW2][W2|Sex]
99. -170.8616	2.400539e-05	[Sex|Drug][Drug][W1|W2][W2|Sex]
100. -170.8616	2.400539e-05	[Sex][Drug|Sex][W1|W2][W2|Sex]
101. -170.9687	2.156821e-05	[Sex|Drug][Drug][W1|SexDrug][W2|Sex]
102. -170.9687	2.156821e-05	[Sex][Drug|Sex][W1|SexDrug][W2|Sex]
103. -171.0627	1.963294e-05	[Sex][Drug][W1][W2|DrugW1]
104. -171.2511	1.626230e-05	[Sex][Drug][W1|Sex][W2|DrugW1]
105. -171.5106	1.254519e-05	[Sex|Drug][Drug][W1|SexW2][W2|SexDrug]
106. -171.5106	1.254519e-05	[Sex][Drug|Sex][W1|SexW2][W2|SexDrug]
107. -171.6112	1.134388e-05	[Sex|Drug][Drug][W1|W2][W2|SexDrug]
108. -171.6112	1.134388e-05	[Sex][Drug|Sex][W1|W2][W2|SexDrug]
109. -171.7183	1.019218e-05	[Sex|Drug][Drug][W1|SexDrug][W2|SexDrug]
110. -171.7183	1.019218e-05	[Sex][Drug|Sex][W1|SexDrug][W2|SexDrug]
111. -172.2707	5.866267e-06	[Sex|Drug][Drug][W1][W2|DrugW1]
112. -172.2707	5.866267e-06	[Sex][Drug|Sex][W1][W2|DrugW1]
113. -172.4590	4.85913e-06	[Sex|Drug][Drug][W1|Sex][W2|DrugW1]
114. -172.4590	4.85913e-06	[Sex][Drug|Sex][W1|Sex][W2|DrugW1]
115. -172.7460	3.646863e-06	[Sex][Drug][W1][W2|SexDrugW1]
116. -172.9344	3.020759e-06	[Sex][Drug][W1|Sex][W2|SexDrugW1]
117. -173.9540	1.089672e-06	[Sex|Drug][Drug][W1][W2|SexDrugW1]
118. -173.9540	1.089672e-06	[Sex][Drug|Sex][W1][W2|SexDrugW1]
119. -174.1424	9.025943e-07	[Sex|Drug][Drug][W1|Sex][W2|SexDrugW1]
120. -174.1424	9.025943e-07	[Sex][Drug|Sex][W1|Sex][W2|SexDrugW1]
121. -175.1916	3.161061e-07	[Sex][Drug][W1][W2|Drug]
122. -175.2508	2.979371e-07	[Sex][Drug][W1][W2]
123. -175.3799	2.618362e-07	[Sex][Drug][W1|Sex][W2|Drug]
124. -175.4391	2.467865e-07	[Sex][Drug][W1|Sex][W2]
125. -175.9251	1.518041e-07	[Sex][Drug][W1][W2|Sex]
126. -176.1134	1.257420e-07	[Sex][Drug][W1|Sex][W2|Sex]
127. -176.3996	9.445162e-08	[Sex|Drug][Drug][W1][W2|Drug]
128. -176.3996	9.445162e-08	[Sex][Drug|Sex][W1][W2|Drug]
129. -176.4588	8.902276e-08	[Sex|Drug][Drug][W1][W2]
130. -176.4588	8.902276e-08	[Sex][Drug|Sex][W1][W2]
131. -176.5879	7.823591e-08	[Sex|Drug][Drug][W1|Sex][W2|Drug]
132. -176.5879	7.823591e-08	[Sex][Drug|Sex][W1|Sex][W2|Drug]
133. -176.6471	7.373909e-08	[Sex|Drug][Drug][W1|Sex][W2]
134. -176.6471	7.373909e-08	[Sex][Drug|Sex][W1|Sex][W2]
135. -176.6747	7.173588e-08	[Sex][Drug][W1][W2|SexDrug]
136. -176.8630	5.942007e-08	[Sex][Drug][W1|Sex][W2|SexDrug]
137. -177.1331	4.535863e-08	[Sex|Drug][Drug][W1][W2|Sex]
138. -177.1331	4.535863e-08	[Sex][Drug|Sex][W1][W2|Sex]
139. -177.3214	3.757134e-08	[Sex|Drug][Drug][W1|Sex][W2|Sex]
140. -177.3214	3.757134e-08	[Sex][Drug|Sex][W1|Sex][W2|Sex]
141. -177.8827	2.143448e-08	[Sex|Drug][Drug][W1][W2|SexDrug]
142. -177.8827	2.143448e-08	[Sex][Drug|Sex][W1][W2|SexDrug]
143. -178.071	1.775455e-08	[Sex|Drug][Drug][W1|Sex][W2|SexDrug]
144. -178.071	1.775455e-08	[Sex][Drug|Sex][W1|Sex][W2|SexDrug]
> 
> 
> 
> cleanEx(); ..nameEx <- "numbermixed"
> 
> ### * numbermixed
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: numbermixed
> ### Title: The number of possible networks
> ### Aliases: numbermixed
> ### Keywords: models
> 
> ### ** Examples
> 
> numbermixed(2,2)
[1] 144
> ## Not run: numbermixed(5,10)
> 
> 
> 
> cleanEx(); ..nameEx <- "nwfunique"
> 
> ### * nwfunique
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: unique.networkfamily
> ### Title: Makes a network family unique.
> ### Aliases: unique.networkfamily
> ### Keywords: iplot
> 
> ### ** Examples
> 
> data(rats)
> rats.nwf <- networkfamily(rats)
[networkfamily Imaginary sample size: 12 
Creating all (144 minus restrictions) networks with 2 discrete and 2 continuous nodes
Created 144 networks, 1.01 ]
> rats.nwf2<- unique(getnetwork(rats.nwf),equi=TRUE)
> 
> 
> 
> cleanEx(); ..nameEx <- "perturb"
> 
> ### * perturb
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: perturb
> ### Title: Perturbs a network
> ### Aliases: perturb
> ### Keywords: iplot
> 
> ### ** Examples
> 
> set.seed(200)
> data(rats)
> fit       <- network(rats)
> fit.prior <- jointprior(fit)
Imaginary sample size: 12 
> fit       <- getnetwork(learn(fit,rats,fit.prior))
> fit.new   <- getnetwork(perturb(fit,rats,fit.prior,degree=10))
[Perturb 0.08 ]
> 
> data(ksl)
> ksl.nw    <- network(ksl)
> ksl.rand  <- getnetwork(perturb(ksl.nw,nocalc=TRUE,degree=10))
[Perturb 0.27 ]
> plot(ksl.rand)
> 
> 
> 
> cleanEx(); ..nameEx <- "readnet"
> 
> ### * readnet
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: readnet
> ### Title: Reads/saves .net file
> ### Aliases: readnet savenet
> ### Keywords: iplot
> 
> ### ** Examples
> 
> data(rats)
> nw <- network(rats)
> ## Not run: savenet(nw,file("default.net"))
> ## Not run: nw2 <- readnet(file("default.net"))
> ## Not run: nw2 <- prob(nw2,rats)
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "rnetwork"
> 
> ### * rnetwork
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: rnetwork
> ### Title: Simulation of data sets with a given dependency structure
> ### Aliases: rnetwork
> ### Keywords: models
> 
> ### ** Examples
> 
> A  <- factor(NA,levels=paste("A",1:2,sep=""))
> B  <- factor(NA,levels=paste("B",1:3,sep=""))
> c1 <- NA
> c2 <- NA
> df <- data.frame(A,B,c1,c2)
> 
> nw <- network(df,doprob=FALSE) # doprob must be FALSE
> nw <- makesimprob(nw)          # create simprob properties
> 
> set.seed(944) 
> sim <- rnetwork(nw,n=100)    # create simulated data frame
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  19.65 0.21 19.95 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')