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("boolean-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('boolean')
Loading required package: rgenoud
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "boolean"
> 
> ### * boolean
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: boolean
> ### Title: Partial-Observability Logit or Probit Models for Testing Boolean
> ###   Hypotheses
> ### Aliases: boolean Boolean
> ### Keywords: logic optimize distribution models
> 
> ### ** Examples
> 
> library("boolean")
> set.seed(50)
> x1<-rnorm(1000)
> x2<-rnorm(1000)
> x3<-rnorm(1000)
> x4<-rnorm(1000)
> x5<-rnorm(1000)
> x6<-rnorm(1000)
> y<-1-(1-pnorm(-2+0.33*x1+0.66*x2+1*x3)*1-(pnorm(1+1.5*x4-0.25*x5)*pnorm(1+0.2*x6)))
> y <- y>runif(1000)
> answer <- boolean(y ~( ((cons+x1+x2+x3)|((cons+x4+x5)&(cons+x6))) ), method="probit")
> 
> ## Examine coefficients, standard errors, etc.
> summary(answer)
Model:  y~(((cons+x1+x2+x3)|((cons+x4+x5)&(cons+x6)))) 
   
     LogLik Iterations
1 -467.1378         32
   
  Vars    Coefs StdErrs Zscore  ProbZ   CI95Lo   CI95Hi
1 cons -1.73611 0.35400  4.904 0.0000 -2.42996 -1.04226
2   x1  0.35630 0.15680  2.272 0.0115  0.04896  0.66363
3   x2  0.69787 0.18042  3.868 0.0001  0.34425  1.05150
4   x3  1.21986 0.26612  4.584 0.0000  0.69826  1.74145
5 cons  0.90127 0.15563  5.791 0.0000  0.59624  1.20631
6   x4  1.58494 0.18725  8.464 0.0000  1.21793  1.95196
7   x5 -0.27452 0.07694  3.568 0.0002 -0.42533 -0.12372
8 cons  1.15816 0.12672  9.139 0.0000  0.90979  1.40653
9   x6  0.21024 0.09606  2.189 0.0143  0.02197  0.39852
> 
> ## Examine "summary" output plus Hessian, gradient, etc.
> show(answer)
Model:  y~(((cons+x1+x2+x3)|((cons+x4+x5)&(cons+x6)))) 
   
     LogLik Iterations
1 -467.1378         32
   
  Vars    Coefs StdErrs Zscore  ProbZ   CI95Lo   CI95Hi
1 cons -1.73611 0.35400  4.904 0.0000 -2.42996 -1.04226
2   x1  0.35630 0.15680  2.272 0.0115  0.04896  0.66363
3   x2  0.69787 0.18042  3.868 0.0001  0.34425  1.05150
4   x3  1.21986 0.26612  4.584 0.0000  0.69826  1.74145
5 cons  0.90127 0.15563  5.791 0.0000  0.59624  1.20631
6   x4  1.58494 0.18725  8.464 0.0000  1.21793  1.95196
7   x5 -0.27452 0.07694  3.568 0.0002 -0.42533 -0.12372
8 cons  1.15816 0.12672  9.139 0.0000  0.90979  1.40653
9   x6  0.21024 0.09606  2.189 0.0143  0.02197  0.39852
   
Hessian:
            [,1]       [,2]      [,3]      [,4]        [,5]       [,6]
 [1,]  48.227065 11.7733066 25.452749 41.261633  39.9504756 -22.207223
 [2,]  11.773307 50.4195611 -9.426805 13.292399   3.1173670  -1.352251
 [3,]  25.452749 -9.4268046 54.164292 13.438779  14.2507190  -7.310498
 [4,]  41.261633 13.2923988 13.438779 53.345202  20.6095187  -9.478340
 [5,]  39.950476  3.1173670 14.250719 20.609519 172.6610606 -84.535046
 [6,] -22.207223 -1.3522513 -7.310498 -9.478340 -84.5350461  85.902646
 [7,]   4.791269  3.5183632  3.002026 -2.221444  26.4654830   7.763311
 [8,]  24.765682  3.7521922 10.164038 15.173612  79.2356578  -6.780162
 [9,]  -6.651578  0.6850428 -2.836254 -5.127811   0.5952359   1.744116
            [,7]       [,8]        [,9]
 [1,]   4.791269  24.765682  -6.6515781
 [2,]   3.518363   3.752192   0.6850428
 [3,]   3.002026  10.164038  -2.8362535
 [4,]  -2.221444  15.173612  -5.1278106
 [5,]  26.465483  79.235658   0.5952359
 [6,]   7.763311  -6.780162   1.7441156
 [7,] 192.395498   4.766118   4.9517553
 [8,]   4.766118 138.275025 -38.4204932
 [9,]   4.951755 -38.420493 129.5816617
   
Gradient:
     [,1]           [,2]          [,3]          [,4]          [,5]          
[1,] "cons"         "x1"          "x2"          "x3"          "cons"        
[2,] "-0.000101205" "-0.00025608" "0.000134435" "-3.6626e-05" "-0.000146656"
     [,6]         [,7]        [,8]           [,9]          
[1,] "x4"         "x5"        "cons"         "x6"          
[2,] "6.6134e-05" "1.711e-05" "-0.000115978" "-0.000166438"
> 
> ## Plot first differences for model
> plot(answer)
> 
> ## Plot profiles
> plot(answer, panel="boolprof")
> 
> 
> 
> cleanEx(); ..nameEx <- "boolfirst"
> 
> ### * boolfirst
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: boolfirst
> ### Title: Graph First Differences after Boolean Logit/Probit
> ### Aliases: boolfirst
> ### Keywords: logic optimize distribution models
> 
> ### ** Examples
> 
> library("boolean")
> set.seed(50)
> x1<-rnorm(1000)
> x2<-rnorm(1000)
> x3<-rnorm(1000)
> x4<-rnorm(1000)
> x5<-rnorm(1000)
> x6<-rnorm(1000)
> e1<-rnorm(1000)/3
> e2<-rnorm(1000)/3
> e3<-rnorm(1000)/3
> y<-1-(1-pnorm(-2+0.33*x1+0.66*x2+1*x3+e1)*1-(pnorm(1+1.5*x4-0.25*x5+e2)*pnorm(1+0.2*x6+e3)))
> y <- y>runif(1000)
> answer <- boolean(y ~( ((cons+x1+x2+x3)|((cons+x4+x5)&(cons+x6))) ), method="probit")
> boolfirst(answer, "x4")
> boolprof(answer, "x1", instance=2)
> 
> 
> 
> cleanEx(); ..nameEx <- "boolprep"
> 
> ### * boolprep
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: boolprep
> ### Title: Generate Functional Form for Boolean Logit or Probit
> ### Aliases: boolprep
> ### Keywords: logic optimize distribution models
> 
> ### ** Examples
> 
> boolprep("a&b", "y", "x1+x2", "x3+x4+x5")
[1] "(a&b)"
[1] "y ~( ((cons+x1+x2)&(cons+x3+x4+x5)) )"
> 
> boolprep("a|(b&c)", "y", "x1+x2+x3", "x4+x5", "x6")
[1] "(a|(b&c))"
[1] "y ~( ((cons+x1+x2+x3)|((cons+x4+x5)&(cons+x6))) )"
> 
> boolprep("(a|b|c)&(d|e)&f", "y", "x1+x2", "x3+x4+x5", "x6", "x7+x8", "x9+x10+x11", "x12+x13")
[1] "((a|b|c)&(d|e)&f)"
[1] "y ~( (((cons+x1+x2)|(cons+x3+x4+x5)|(cons+x6))&((cons+x7+x8)|(cons+x9+x10+x11))&(cons+x12+x13)) )"
> 
> 
> 
> cleanEx(); ..nameEx <- "boolprof"
> 
> ### * boolprof
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: boolprof
> ### Title: Graph Profile Likelihoods for Boolean Logit/Probit
> ### Aliases: boolprof
> ### Keywords: logic optimize distribution models
> 
> ### ** Examples
> 
> library("boolean")
> set.seed(50)
> x1<-rnorm(1000)
> x2<-rnorm(1000)
> x3<-rnorm(1000)
> x4<-rnorm(1000)
> x5<-rnorm(1000)
> x6<-rnorm(1000)
> e1<-rnorm(1000)/3
> e2<-rnorm(1000)/3
> e3<-rnorm(1000)/3
> y<-1-(1-pnorm(-2+0.33*x1+0.66*x2+1*x3+e1)*1-(pnorm(1+1.5*x4-0.25*x5+e2)*pnorm(1+0.2*x6+e3)))
> y <- y>runif(1000)
> answer <- boolean(y ~( ((cons+x1+x2+x3)|((cons+x4+x5)&(cons+x6))) ), method="probit")
> boolprof(answer, "x1")
> boolprof(answer, "cons", instance=2)
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  29.9 0.07 30.12 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')