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("eba-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('eba')
Loading required package: nlme
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "OptiPt"
> 
> ### * OptiPt
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: OptiPt
> ### Title: Elimination-By-Aspects (EBA) Models
> ### Aliases: OptiPt summary.eba print.summary.eba print.eba L
> ### Keywords: models
> 
> ### ** Examples
> 
> data(celebrities)  # absolute choice frequencies
> A <- list(c(1,10), c(2,10), c(3,10), c(4,11), c(5,11), c(6,11),
+           c(7,12), c(8,12), c(9,12))  # the structure of aspects
> eba <- OptiPt(celebrities, A)  # Fit a preference tree
Warning in dbinom(x, size, prob, log) : NaNs produced
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> 
> summary(eba)  # goodness of fit

Parameter estimates:
      Estimate Std. Error z value Pr(>|z|)    
 [1,] 0.223609   0.024872   8.990  < 2e-16 ***
 [2,] 0.121112   0.019593   6.181 6.35e-10 ***
 [3,] 0.087820   0.016366   5.366 8.05e-08 ***
 [4,] 0.040326   0.009572   4.213 2.52e-05 ***
 [5,] 0.016307   0.004648   3.509  0.00045 ***
 [6,] 0.040139   0.010088   3.979 6.92e-05 ***
 [7,] 0.036679   0.006549   5.601 2.13e-08 ***
 [8,] 0.093101   0.012080   7.707 1.29e-14 ***
 [9,] 0.143109   0.015352   9.322  < 2e-16 ***
[10,] 0.071635   0.028930   2.476  0.01328 *  
[11,] 0.054775   0.009606   5.702 1.18e-08 ***
[12,] 0.056997   0.011805   4.828 1.38e-06 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

          Df1 Df2 logLik1 logLik2 Deviance Pr(>|Chi|)    
Overall     1  72  -763.6  -235.2  1056.85     <2e-16 ***
EBA        11  36  -119.0  -103.9    30.17      0.218    
Effect      0  11  -632.4  -119.0  1026.68     <2e-16 ***
Imbalance   1  36  -131.3  -131.3     0.00      1.000    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

AIC:  260.03 
Pearson Chi2: 30.05
> plot(eba)  # residuals versus predicted values
> 
> ci <- 1.96 * sqrt(diag(cov.u(eba)))  # 95%-ci of the preference scale
> a <- barplot(eba$u, ylim = c(0, .35))  # plot the scale
> arrows(a, eba$u-ci, a, eba$u+ci, .05, 90, 3)  # error bars
> 
> 
> 
> cleanEx(); ..nameEx <- "boot"
> 
> ### * boot
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: boot
> ### Title: Bootstrap for Elimination-By-Aspects Models
> ### Aliases: boot eba.boot
> ### Keywords: models
> 
> ### ** Examples
> 
> data(pork)  # pork tasting data, 10 individual paired comparison matrices
> eba <- OptiPt(apply(pork, 1:2, sum))  # fit a BTL model
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> b <- boot(pork, 200)  # resample 200 times
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> 
> plot(eba$estimate, b$stat[,'mean'], log='xy')
> abline(0, 1, lty=2)
> 
> 
> 
> cleanEx(); ..nameEx <- "group.test"
> 
> ### * group.test
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: group.test
> ### Title: Group Effects in EBA Models
> ### Aliases: group.test print.group.test
> ### Keywords: models
> 
> ### ** Examples
> 
> data(pork)  # Is there a difference between Judge 1 and Judge 2?
> groups <- array(c(apply(pork[,,1:5], 1:2, sum),
+                   apply(pork[,,6:10], 1:2, sum)), c(3,3,2))
> group.test(groups)
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Group test for EBA models:

          Df1 Df2  logLik1  logLik2 Deviance Pr(>|Chi|)  
Overall     1  12 -21.1096 -15.4144   11.390     0.4112  
EBA.g       4   6  -5.8795  -4.9726    1.814     0.4038  
Group       2   4  -4.4099  -0.1598    8.500     0.0143 *
Effect      0   2  -4.9481  -4.4099    1.076     0.5838  
Imbalance   1   6 -10.4418 -10.4418    0.000     1.0000  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

> 
> 
> 
> cleanEx(); ..nameEx <- "pcX"
> 
> ### * pcX
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: pcX
> ### Title: Paired-Comparison Design Matrix
> ### Aliases: pcX
> ### Keywords: models
> 
> ### ** Examples
> 
> data(celebrities)  # absolute choice frequencies
> btl <- OptiPt(celebrities)
Warning in dbinom(x, size, prob, log) : NaNs produced
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> summary(btl)

Parameter estimates:
      Estimate Std. Error z value Pr(>|z|)    
 [1,] 0.214363   0.008602   24.92   <2e-16 ***
 [2,] 0.131504   0.005555   23.67   <2e-16 ***
 [3,] 0.103027   0.004459   23.10   <2e-16 ***
 [4,] 0.067545   0.003089   21.86   <2e-16 ***
 [5,] 0.041434   0.002082   19.90   <2e-16 ***
 [6,] 0.070081   0.003187   21.99   <2e-16 ***
 [7,] 0.056838   0.002677   21.23   <2e-16 ***
 [8,] 0.106189   0.004582   23.18   <2e-16 ***
 [9,] 0.146221   0.006116   23.91   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

          Df1 Df2 logLik1 logLik2 Deviance Pr(>|Chi|)    
Overall     1  72  -763.6  -235.2  1056.85    < 2e-16 ***
EBA         8  36  -143.0  -103.9    78.22   1.23e-06 ***
Effect      0   8  -632.4  -143.0   978.63    < 2e-16 ***
Imbalance   1  36  -131.3  -131.3     0.00          1    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

AIC:  302.08 
Pearson Chi2: 77.25
> 
> btl.glm <- glm(cbind(btl$y1, btl$y0) ~ pcX(9)[,-1] - 1, binomial)
> summary(btl.glm)  # BTL model does not fit these data!

Call:
glm(formula = cbind(btl$y1, btl$y0) ~ pcX(9)[, -1] - 1, family = binomial)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.6298  -1.1204  -0.1534   0.8881   4.3003  

Coefficients:
              Estimate Std. Error z value Pr(>|z|)    
pcX(9)[, -1]1 -0.48863    0.06654  -7.343 2.09e-13 ***
pcX(9)[, -1]2 -0.73268    0.06649 -11.020  < 2e-16 ***
pcX(9)[, -1]3 -1.15486    0.06746 -17.119  < 2e-16 ***
pcX(9)[, -1]4 -1.64356    0.07031 -23.375  < 2e-16 ***
pcX(9)[, -1]5 -1.11802    0.06732 -16.607  < 2e-16 ***
pcX(9)[, -1]6 -1.32745    0.06824 -19.451  < 2e-16 ***
pcX(9)[, -1]7 -0.70245    0.06647 -10.568  < 2e-16 ***
pcX(9)[, -1]8 -0.38255    0.06672  -5.734 9.82e-09 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1056.850  on 36  degrees of freedom
Residual deviance:   78.217  on 28  degrees of freedom
AIC: 302.08

Number of Fisher Scoring iterations: 4

> 
> 
> 
> cleanEx(); ..nameEx <- "plot.eba"
> 
> ### * plot.eba
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: plot.eba
> ### Title: Diagnostic Plot for EBA Models
> ### Aliases: plot.eba
> ### Keywords: models
> 
> ### ** Examples
> 
> data(celebrities)  # absolute choice frequencies
> btl <- OptiPt(celebrities)
Warning in dbinom(x, size, prob, log) : NaNs produced
Warning: NA/Inf replaced by maximum positive value
Warning in dbinom(x, size, prob, log) : NaNs produced
Warning: NA/Inf replaced by maximum positive value
Warning in dbinom(x, size, prob, log) : NaNs produced
Warning: NA/Inf replaced by maximum positive value
Warning in dbinom(x, size, prob, log) : NaNs produced
Warning: NA/Inf replaced by maximum positive value
> plot(btl)
> 
> 
> 
> cleanEx(); ..nameEx <- "residuals.eba"
> 
> ### * residuals.eba
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: residuals.eba
> ### Title: Residuals for EBA Models
> ### Aliases: residuals.eba
> ### Keywords: models
> 
> ### ** Examples
> 
> data(celebrities)  # absolute choice frequencies
> btl <- OptiPt(celebrities)
Warning in dbinom(x, size, prob, log) : NaNs produced
Warning: NA/Inf replaced by maximum positive value
Warning in dbinom(x, size, prob, log) : NaNs produced
Warning: NA/Inf replaced by maximum positive value
Warning in dbinom(x, size, prob, log) : NaNs produced
Warning: NA/Inf replaced by maximum positive value
Warning in dbinom(x, size, prob, log) : NaNs produced
Warning: NA/Inf replaced by maximum positive value
> sum( resid(btl)^2 )  # Deviance
[1] 78.21721
> sum( resid(btl, "pearson")^2 )  # Pearson chi2
[1] 77.24702
> 
> 
> 
> cleanEx(); ..nameEx <- "strans"
> 
> ### * strans
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: strans
> ### Title: Stochastic Transitivity
> ### Aliases: strans print.strans
> ### Keywords: models
> 
> ### ** Examples
> 
> data(celebrities)  # absolute choice frequencies
> strans(celebrities)  # WST and MST hold, but not SST

Stochastic Transitivity

         violations error.ratio mean.dev max.dev
weak              0      0.0000   0.0000  0.0000
moderate          0      0.0000   0.0000  0.0000
strong           34      0.4048   0.0566  0.1966
---
Number of Tests:  84 

> strans(celebrities)$pcm  # relative frequencies
        LBJ        HW       CDG        JU        CY       AJF        BB
1 0.0000000 0.6794872 0.6965812 0.7478632 0.7820513 0.7649573 0.7393162
2 0.3205128 0.0000000 0.5897436 0.7008547 0.7350427 0.6837607 0.6666667
3 0.3034188 0.4102564 0.0000000 0.6196581 0.6709402 0.5897436 0.5982906
4 0.2521368 0.2991453 0.3803419 0.0000000 0.7521368 0.4914530 0.5299145
5 0.2179487 0.2649573 0.3290598 0.2478632 0.0000000 0.3290598 0.4059829
6 0.2350427 0.3162393 0.4102564 0.5085470 0.6709402 0.0000000 0.5726496
7 0.2606838 0.3333333 0.4017094 0.4700855 0.5940171 0.4273504 0.0000000
8 0.3162393 0.4786325 0.4786325 0.6324786 0.6923077 0.6068376 0.7136752
9 0.3931624 0.4786325 0.4871795 0.7393162 0.7393162 0.6965812 0.7948718
         ET        SL
1 0.6837607 0.6068376
2 0.5213675 0.5213675
3 0.5213675 0.5128205
4 0.3675214 0.2606838
5 0.3076923 0.2606838
6 0.3931624 0.3034188
7 0.2863248 0.2051282
8 0.0000000 0.3717949
9 0.6282051 0.0000000
> 
> 
> 
> cleanEx(); ..nameEx <- "wald.test"
> 
> ### * wald.test
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: wald.test
> ### Title: Testing Linear Hypotheses
> ### Aliases: wald.test print.wald.test
> ### Keywords: models
> 
> ### ** Examples
> 
> data(celebrities)  # absolute choice frequencies
> A <- list(c(1,10), c(2,10), c(3,10), c(4,11), c(5,11), c(6,11),
+           c(7,12), c(8,12), c(9,12))  # the structure of aspects
> eba <- OptiPt(celebrities, A)  # Fit a preference tree
Warning in dbinom(x, size, prob, log) : NaNs produced
Warning: NA/Inf replaced by maximum positive value
Warning in dbinom(x, size, prob, log) : NaNs produced
Warning: NA/Inf replaced by maximum positive value
Warning in dbinom(x, size, prob, log) : NaNs produced
Warning: NA/Inf replaced by maximum positive value
Warning in dbinom(x, size, prob, log) : NaNs produced
Warning: NA/Inf replaced by maximum positive value
> 
> # Test whether JU, CY, and AJF have equal preference scale values
> C1 <- matrix(c(0,0,0,1,-1,0,0,0,0,
+                0,0,0,1,0,-1,0,0,0), 2, 9, TRUE)
> wald.test(eba, C1)

Wald Test: Cp = 0

C:
     LBJ HW CDG JU CY AJF BB ET SL
[1,]   0  0   0  1 -1   0  0  0  0
[2,]   0  0   0  1  0  -1  0  0  0

W = 19.17297, df = 2, p-value = 6.865044e-05

> 
> # Test whether the three branch parameters are different
> C2 <- matrix(c(0,0,0,0,0,0,0,0,0,1,-1,0,
+                0,0,0,0,0,0,0,0,0,1,0,-1), 2, 12, TRUE)
> wald.test(eba, C2, u.scale = FALSE)

Wald Test: Cp = 0

C:
     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
[1,]    0    0    0    0    0    0    0    0    0     1    -1     0
[2,]    0    0    0    0    0    0    0    0    0     1     0    -1

W = 0.3947388, df = 2, p-value = 0.8208874

> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  2.47 0.13 2.62 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')