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("BradleyTerry-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('BradleyTerry')
Loading required package: brlr
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "BTabilities"
> 
> ### * BTabilities
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: BTabilities
> ### Title: estimated abilities in a Bradley-Terry model
> ### Aliases: BTabilities
> ### Keywords: models
> 
> ### ** Examples
> 
> data(citations)
> origin <- factor(c("UK", "USA", "USA", "UK"))
> citeModel2 <- BTm(citations ~ origin)
> BTabilities(citeModel2)
             ability       s.e.
Biometrika    0.0000 0.00000000
Comm.Statist -1.2732 0.04999872
JASA         -1.2732 0.04999872
JRSS.B        0.0000 0.00000000
> 
> 
> 
> cleanEx(); ..nameEx <- "BTm"
> 
> ### * BTm
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: BTm
> ### Title: Bradley-Terry model and extensions
> ### Aliases: BTm drop1.BTm add1.BTm terms.BTm formula.BTm
> ### Keywords: models
> 
> ### ** Examples
> 
> ##
> ##  Statistics journal citation data from Stigler (1994)
> ##  -- see also Agresti (2002, p448)
> data(citations)
>                  
> ##  First fit the "standard" Bradley-Terry model
> print(citeModel <- BTm(citations ~ ..))

Call:  BTm(formula = citations ~ ..) 

Coefficients:
..Comm.Statist          ..JASA        ..JRSS.B  
       -2.9491         -0.4796          0.2690  

Degrees of Freedom: 6 Total (i.e. Null);  3 Residual
Null Deviance:	    1925 
Residual Deviance: 4.293 	AIC: 46.39 
> 
> ##  Now the same thing with a different "reference" journal
> update(citeModel, . ~ ., refcat = "JASA")

Call:  BTm(formula = citations ~ .., refcat = "JASA") 

Coefficients:
  ..Biometrika  ..Comm.Statist        ..JRSS.B  
        0.4796         -2.4695          0.7485  

Degrees of Freedom: 6 Total (i.e. Null);  3 Residual
Null Deviance:	    1925 
Residual Deviance: 4.293 	AIC: 46.39 
> 
> ##  Is the "citeability" of a journal predicted by its country of origin?
> origin <- factor(c("UK", "USA", "USA", "UK"))
> print(citeModel2 <- BTm(citations ~ origin))

Call:  BTm(formula = citations ~ origin) 

Coefficients:
originUSA  
   -1.273  

Degrees of Freedom: 6 Total (i.e. Null);  5 Residual
Null Deviance:	    1925 
Residual Deviance: 1139 	AIC: 1177 
> 
> ##  Hmm... not so sure about the origin of "Comm Statist" ...
> is.na(origin[2]) <- TRUE
> citeModel2 <- update(citeModel2, . ~ .)
> 
> ##  Now an example with an order effect -- see Agresti (2002) p438
> data(baseball)
> 
> ##  Simple Bradley-Terry model as in Agresti p437
> print(baseballModel <- BTm(baseball ~ ..))

Call:  BTm(formula = baseball ~ ..) 

Coefficients:
   ..Boston  ..Cleveland    ..Detroit  ..Milwaukee   ..New.York    ..Toronto  
     1.1077       0.6839       1.4364       1.5814       1.2476       1.2945  

Degrees of Freedom: 21 Total (i.e. Null);  15 Residual
Null Deviance:	    49.7 
Residual Deviance: 15.74 	AIC: 87.32 
> 
> ##  Introduce order effect as in Agresti p438
> update(baseballModel, order.effect = baseball$home.adv)   

Call:  BTm(formula = baseball ~ .., order.effect = baseball$home.adv) 

Coefficients:
   ..Boston  ..Cleveland    ..Detroit  ..Milwaukee   ..New.York    ..Toronto  
     1.1438       0.7047       1.4754       1.6196       1.2813       1.3271  
     .order  
     0.3023  

Degrees of Freedom: 42 Total (i.e. Null);  35 Residual
Null Deviance:	    78.02 
Residual Deviance: 38.64 	AIC: 137.1 
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "BTresiduals"
> 
> ### * BTresiduals
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: BTresiduals
> ### Title: player-specific residuals from a Bradley-Terry model
> ### Aliases: BTresiduals
> ### Keywords: models
> 
> ### ** Examples
> 
> data(citations)
> origin <- factor(c("UK", "USA", "USA", "UK"))
> citeModel2 <- BTm(citations ~ origin)
> BTresiduals(citeModel2)
  Biometrika Comm.Statist         JASA       JRSS.B 
 -0.09767683  -1.38124450   1.25835736   0.15117998 
attr(,"weights")
  Biometrika Comm.Statist         JASA       JRSS.B 
    396.4048     400.6950     439.8255     256.1157 
> 
> 
> 
> cleanEx(); ..nameEx <- "baseball"
> 
> ### * baseball
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: baseball
> ### Title: Baseball data from Agresti (2002)
> ### Aliases: baseball
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(baseball)
> ##  The data in collapsed tabular form as on p438 of Agresti
> xtabs(Freq ~ winner + loser, baseball)
           loser
winner      Milwaukee Detroit Toronto New York Boston Cleveland Baltimore
  Milwaukee         0       7       9        7      7         9        11
  Detroit           6       0       7        5     11         9         9
  Toronto           4       6       0        7      7         8        12
  New York          6       8       6        0      6         7        10
  Boston            6       2       6        7      0         7        12
  Cleveland         4       4       5        6      6         0         6
  Baltimore         2       4       1        3      1         7         0
> ##  Simple Bradley-Terry model as in Agresti p437
> print(baseballModel <- BTm(baseball ~ ..))

Call:  BTm(formula = baseball ~ ..) 

Coefficients:
   ..Boston  ..Cleveland    ..Detroit  ..Milwaukee   ..New.York    ..Toronto  
     1.1077       0.6839       1.4364       1.5814       1.2476       1.2945  

Degrees of Freedom: 21 Total (i.e. Null);  15 Residual
Null Deviance:	    49.7 
Residual Deviance: 15.74 	AIC: 87.32 
> ##  Introduce order effect as in Agresti p438
> update(baseballModel, order.effect = baseball$home.adv) 

Call:  BTm(formula = baseball ~ .., order.effect = baseball$home.adv) 

Coefficients:
   ..Boston  ..Cleveland    ..Detroit  ..Milwaukee   ..New.York    ..Toronto  
     1.1438       0.7047       1.4754       1.6196       1.2813       1.3271  
     .order  
     0.3023  

Degrees of Freedom: 42 Total (i.e. Null);  35 Residual
Null Deviance:	    78.02 
Residual Deviance: 38.64 	AIC: 137.1 
> 
> 
> 
> cleanEx(); ..nameEx <- "citations"
> 
> ### * citations
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: citations
> ### Title: Statistics journal citation data from Stigler (1994)
> ### Aliases: citations
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(citations)
> ##  Data as a square table, as in Agresti p448
> xtabs(Freq ~ ., citations)
              loser
winner         Biometrika Comm Statist JASA JRSS-B
  Biometrika            0          730  498    221
  Comm Statist         33            0   68     17
  JASA                320          813    0    142
  JRSS-B              284          276  325      0
> ## Standard Bradley-Terry model fitted to these data
> BTm(citations ~ ..)

Call:  BTm(formula = citations ~ ..) 

Coefficients:
..Comm.Statist          ..JASA        ..JRSS.B  
       -2.9491         -0.4796          0.2690  

Degrees of Freedom: 6 Total (i.e. Null);  3 Residual
Null Deviance:	    1925 
Residual Deviance: 4.293 	AIC: 46.39 
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  0.84 0.02 0.87 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')