* using log directory ‘/data/gannet/ripley/R/packages/tests-Suggests/partykit.Rcheck’
* using R Under development (unstable) (2026-01-29 r89353)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc (GCC) 15.1.1 20250521 (Red Hat 15.1.1-2)
    GNU Fortran (GCC) 15.1.1 20250521 (Red Hat 15.1.1-2)
* running under: Fedora Linux 42 (Workstation Edition)
* using session charset: UTF-8
* using option ‘--no-stop-on-test-error’
* checking for file ‘partykit/DESCRIPTION’ ... OK
* this is package ‘partykit’ version ‘1.2-24’
* checking package namespace information ... OK
* checking package dependencies ... INFO
Package suggested but not available for checking: ‘pmml’
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for executable files ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘partykit’ can be installed ... [28s/152s] OK
* used C compiler: ‘gcc (GCC) 15.1.1 20250521 (Red Hat 15.1.1-2)’
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... [3s/14s] OK
* checking whether the package can be loaded with stated dependencies ... [3s/16s] OK
* checking whether the package can be unloaded cleanly ... [3s/16s] OK
* checking whether the namespace can be loaded with stated dependencies ... [3s/16s] OK
* checking whether the namespace can be unloaded cleanly ... [3s/15s] OK
* checking loading without being on the library search path ... OK
* checking whether startup messages can be suppressed ... [3s/16s] OK
* checking use of S3 registration ... OK
* checking dependencies in R code ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... [53s/257s] OK
* checking Rd files ... [2s/10s] OK
* checking Rd metadata ... OK
* checking Rd line widths ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking LazyData ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking line endings in shell scripts ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking line endings in Makefiles ... OK
* checking for GNU extensions in Makefiles ... OK
* checking include directives in Makefiles ... OK
* checking pragmas in C/C++ headers and code ... OK
* checking compilation flags used ... OK
* checking compiled code ... OK
* checking sizes of PDF files under ‘inst/doc’ ... OK
* checking installed files from ‘inst/doc’ ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... [19s/78s] ERROR
Running examples in ‘partykit-Ex.R’ failed
The error most likely occurred in:

> ### Name: varimp
> ### Title: Variable Importance
> ### Aliases: varimp varimp.constparty varimp.cforest
> ### Keywords: tree
> 
> ### ** Examples
> 
>     
>    set.seed(290875)
>    data("readingSkills", package = "party")
Error in find.package(package, lib.loc, verbose = verbose) : 
  there is no package called ‘party’
Calls: data -> find.package
Execution halted
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘bugfixes.R’ [5s/22s]
  Running ‘constparty.R’ [3s/15s]
  Running ‘regtest-MIA.R’ [3s/12s]
  Comparing ‘regtest-MIA.Rout’ to ‘regtest-MIA.Rout.save’ ... OK
  Running ‘regtest-cforest.R’
  Running ‘regtest-ctree.R’ [4s/11s]
  Comparing ‘regtest-ctree.Rout’ to ‘regtest-ctree.Rout.save’ ... OK
  Running ‘regtest-glmtree.R’ [61s/172s]
  Running ‘regtest-honesty.R’ [3s/19s]
  Running ‘regtest-lmtree.R’ [5s/18s]
  Running ‘regtest-nmax.R’ [3s/12s]
  Comparing ‘regtest-nmax.Rout’ to ‘regtest-nmax.Rout.save’ ... OK
  Running ‘regtest-node.R’ [3s/20s]
  Comparing ‘regtest-node.Rout’ to ‘regtest-node.Rout.save’ ... OK
  Running ‘regtest-party-random.R’ [4s/18s]
  Running ‘regtest-party.R’ [3s/15s]
  Running ‘regtest-split.R’ [3s/13s]
  Comparing ‘regtest-split.Rout’ to ‘regtest-split.Rout.save’ ... OK
  Running ‘regtest-weights.R’ [4s/13s]
  Comparing ‘regtest-weights.Rout’ to ‘regtest-weights.Rout.save’ ... OK
 [107s/364s] ERROR
Running the tests in ‘tests/bugfixes.R’ failed.
Complete output:
  > suppressWarnings(RNGversion("3.5.2"))
  > 
  > set.seed(290875)
  > 
  > datLB <-
  + structure(list(Site = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  + 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  + 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 
  + 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 
  + 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
  + 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
  + 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
  + 9L, 9L, 9L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
  + 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 
  + 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
  + 4L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
  + 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
  + 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
  + 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 
  + 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
  + 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 
  + 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 
  + 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
  + 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 
  + 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
  + 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 1L, 1L, 1L, 
  + 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  + 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
  + 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 
  + 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
  + 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 
  + 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 
  + 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 1L, 1L, 1L, 1L, 1L, 1L, 
  + 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  + 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
  + 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 
  + 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 
  + 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
  + 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 
  + 9L, 9L, 9L, 9L, 9L, 9L, 9L), ID = c(1.1, 2.1, 3.1, 4.1, 5.1, 
  + 6.1, 7.1, 8.1, 9.1, 10.1, 11.1, 12.1, 1.2, 2.2, 3.2, 4.2, 5.2, 
  + 6.2, 7.2, 8.2, 9.2, 10.2, 11.2, 12.2, 13.2, 14.2, 1.3, 2.3, 3.3, 
  + 4.3, 5.3, 6.3, 7.3, 8.3, 9.3, 10.3, 11.3, 12.3, 1.4, 2.4, 3.4, 
  + 4.4, 5.4, 6.4, 7.4, 8.4, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 1.6, 2.6, 
  + 3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 10.6, 11.6, 12.6, 13.6, 14.6, 
  + 15.6, 1.7, 2.7, 3.7, 4.7, 5.7, 6.7, 7.7, 8.7, 9.7, 10.7, 11.7, 
  + 12.7, 1.8, 2.8, 3.8, 4.8, 5.8, 6.8, 7.8, 8.8, 9.8, 10.8, 11.8, 
  + 12.8, 13.8, 14.8, 15.8, 16.8, 17.8, 18.8, 19.8, 1.9, 2.9, 3.9, 
  + 4.9, 5.9, 6.9, 7.9, 8.9, 9.9, 10.9, 11.9, 1.1, 2.1, 3.1, 4.1, 
  + 5.1, 6.1, 7.1, 8.1, 9.1, 10.1, 11.1, 12.1, 1.2, 2.2, 3.2, 4.2, 
  + 5.2, 6.2, 7.2, 8.2, 9.2, 10.2, 11.2, 12.2, 13.2, 14.2, 1.3, 2.3, 
  + 3.3, 4.3, 5.3, 6.3, 7.3, 8.3, 9.3, 10.3, 11.3, 12.3, 1.4, 2.4, 
  + 3.4, 4.4, 5.4, 6.4, 7.4, 8.4, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 1.6, 
  + 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 10.6, 11.6, 12.6, 13.6, 
  + 14.6, 15.6, 1.7, 2.7, 3.7, 4.7, 5.7, 6.7, 7.7, 8.7, 9.7, 10.7, 
  + 11.7, 12.7, 1.8, 2.8, 3.8, 4.8, 5.8, 6.8, 7.8, 8.8, 9.8, 10.8, 
  + 11.8, 12.8, 13.8, 14.8, 15.8, 16.8, 17.8, 18.8, 19.8, 1.9, 2.9, 
  + 3.9, 4.9, 5.9, 6.9, 7.9, 8.9, 9.9, 10.9, 11.9, 1.1, 2.1, 3.1, 
  + 4.1, 5.1, 6.1, 7.1, 8.1, 9.1, 10.1, 11.1, 12.1, 1.2, 2.2, 3.2, 
  + 4.2, 5.2, 6.2, 7.2, 8.2, 9.2, 10.2, 11.2, 12.2, 13.2, 14.2, 1.3, 
  + 2.3, 3.3, 4.3, 5.3, 6.3, 7.3, 8.3, 9.3, 10.3, 11.3, 12.3, 1.4, 
  + 2.4, 3.4, 4.4, 5.4, 6.4, 7.4, 8.4, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 
  + 1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 10.6, 11.6, 12.6, 
  + 13.6, 14.6, 15.6, 1.7, 2.7, 3.7, 4.7, 5.7, 6.7, 7.7, 8.7, 9.7, 
  + 10.7, 11.7, 12.7, 1.8, 2.8, 3.8, 4.8, 5.8, 6.8, 7.8, 8.8, 9.8, 
  + 10.8, 11.8, 12.8, 13.8, 14.8, 15.8, 16.8, 17.8, 18.8, 19.8, 1.9, 
  + 2.9, 3.9, 4.9, 5.9, 6.9, 7.9, 8.9, 9.9, 10.9, 11.9, 1.1, 2.1, 
  + 3.1, 4.1, 5.1, 6.1, 7.1, 8.1, 9.1, 10.1, 11.1, 12.1, 1.2, 2.2, 
  + 3.2, 4.2, 5.2, 6.2, 7.2, 8.2, 9.2, 10.2, 11.2, 12.2, 13.2, 14.2, 
  + 1.3, 2.3, 3.3, 4.3, 5.3, 6.3, 7.3, 8.3, 9.3, 10.3, 11.3, 12.3, 
  + 1.4, 2.4, 3.4, 4.4, 5.4, 6.4, 7.4, 8.4, 1.5, 2.5, 3.5, 4.5, 5.5, 
  + 6.5, 1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 10.6, 11.6, 
  + 12.6, 13.6, 14.6, 15.6, 1.7, 2.7, 3.7, 4.7, 5.7, 6.7, 7.7, 8.7, 
  + 9.7, 10.7, 11.7, 12.7, 1.8, 2.8, 3.8, 4.8, 5.8, 6.8, 7.8, 8.8, 
  + 9.8, 10.8, 11.8, 12.8, 13.8, 14.8, 15.8, 16.8, 17.8, 18.8, 19.8, 
  + 1.9, 2.9, 3.9, 4.9, 5.9, 6.9, 7.9, 8.9, 9.9, 10.9, 11.9, 1.1, 
  + 2.1, 3.1, 4.1, 5.1, 6.1, 7.1, 8.1, 9.1, 10.1, 11.1, 12.1, 1.2, 
  + 2.2, 3.2, 4.2, 5.2, 6.2, 7.2, 8.2, 9.2, 10.2, 11.2, 12.2, 13.2, 
  + 14.2, 1.3, 2.3, 3.3, 4.3, 5.3, 6.3, 7.3, 8.3, 9.3, 10.3, 11.3, 
  + 12.3, 1.4, 2.4, 3.4, 4.4, 5.4, 6.4, 7.4, 8.4, 1.5, 2.5, 3.5, 
  + 4.5, 5.5, 6.5, 1.6, 2.6, 3.6, 4.6, 5.6, 6.6, 7.6, 8.6, 9.6, 10.6, 
  + 11.6, 12.6, 13.6, 14.6, 15.6, 1.7, 2.7, 3.7, 4.7, 5.7, 6.7, 7.7, 
  + 8.7, 9.7, 10.7, 11.7, 12.7, 1.8, 2.8, 3.8, 4.8, 5.8, 6.8, 7.8, 
  + 8.8, 9.8, 10.8, 11.8, 12.8, 13.8, 14.8, 15.8, 16.8, 17.8, 18.8, 
  + 19.8, 1.9, 2.9, 3.9, 4.9, 5.9, 6.9, 7.9, 8.9, 9.9, 10.9, 11.9
  + ), Treat = structure(c(2L, 1L, 2L, 3L, 1L, 1L, 2L, 3L, 2L, 3L, 
  + 1L, 3L, 3L, 2L, 1L, 2L, 1L, 3L, 1L, 3L, 2L, 3L, 1L, 2L, 3L, 2L, 
  + 1L, 3L, 2L, 3L, 1L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 3L, 2L, 1L, 1L, 
  + 3L, 2L, 3L, 1L, 1L, 3L, 2L, 1L, 2L, 3L, 3L, 1L, 2L, 2L, 1L, 3L, 
  + 2L, 3L, 1L, 3L, 1L, 2L, 1L, 2L, 3L, 2L, 3L, 1L, 3L, 1L, 2L, 1L, 
  + 3L, 2L, 1L, 3L, 2L, 3L, 1L, 2L, 2L, 1L, 3L, 1L, 2L, 3L, 3L, 1L, 
  + 2L, 3L, 2L, 1L, 2L, 1L, 3L, 1L, 1L, 3L, 2L, 3L, 2L, 1L, 3L, 2L, 
  + 1L, 1L, 2L, 2L, 1L, 2L, 3L, 1L, 1L, 2L, 3L, 2L, 3L, 1L, 3L, 3L, 
  + 2L, 1L, 2L, 1L, 3L, 1L, 3L, 2L, 3L, 1L, 2L, 3L, 2L, 1L, 3L, 2L, 
  + 3L, 1L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 3L, 2L, 1L, 1L, 3L, 2L, 3L, 
  + 1L, 1L, 3L, 2L, 1L, 2L, 3L, 3L, 1L, 2L, 2L, 1L, 3L, 2L, 3L, 1L, 
  + 3L, 1L, 2L, 1L, 2L, 3L, 2L, 3L, 1L, 3L, 1L, 2L, 1L, 3L, 2L, 1L, 
  + 3L, 2L, 3L, 1L, 2L, 2L, 1L, 3L, 1L, 2L, 3L, 3L, 1L, 2L, 3L, 2L, 
  + 1L, 2L, 1L, 3L, 1L, 1L, 3L, 2L, 3L, 2L, 1L, 3L, 2L, 1L, 1L, 2L, 
  + 2L, 1L, 2L, 3L, 1L, 1L, 2L, 3L, 2L, 3L, 1L, 3L, 3L, 2L, 1L, 2L, 
  + 1L, 3L, 1L, 3L, 2L, 3L, 1L, 2L, 3L, 2L, 1L, 3L, 2L, 3L, 1L, 2L, 
  + 1L, 3L, 2L, 1L, 3L, 2L, 3L, 2L, 1L, 1L, 3L, 2L, 3L, 1L, 1L, 3L, 
  + 2L, 1L, 2L, 3L, 3L, 1L, 2L, 2L, 1L, 3L, 2L, 3L, 1L, 3L, 1L, 2L, 
  + 1L, 2L, 3L, 2L, 3L, 1L, 3L, 1L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 3L, 
  + 1L, 2L, 2L, 1L, 3L, 1L, 2L, 3L, 3L, 1L, 2L, 3L, 2L, 1L, 2L, 1L, 
  + 3L, 1L, 1L, 3L, 2L, 3L, 2L, 1L, 3L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 
  + 3L, 1L, 1L, 2L, 3L, 2L, 3L, 1L, 3L, 3L, 2L, 1L, 2L, 1L, 3L, 1L, 
  + 3L, 2L, 3L, 1L, 2L, 3L, 2L, 1L, 3L, 2L, 3L, 1L, 2L, 1L, 3L, 2L, 
  + 1L, 3L, 2L, 3L, 2L, 1L, 1L, 3L, 2L, 3L, 1L, 1L, 3L, 2L, 1L, 2L, 
  + 3L, 3L, 1L, 2L, 2L, 1L, 3L, 2L, 3L, 1L, 3L, 1L, 2L, 1L, 2L, 3L, 
  + 2L, 3L, 1L, 3L, 1L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 3L, 1L, 2L, 2L, 
  + 1L, 3L, 1L, 2L, 3L, 3L, 1L, 2L, 3L, 2L, 1L, 2L, 1L, 3L, 1L, 1L, 
  + 3L, 2L, 3L, 2L, 1L, 3L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 3L, 1L, 1L, 
  + 2L, 3L, 2L, 3L, 1L, 3L, 3L, 2L, 1L, 2L, 1L, 3L, 1L, 3L, 2L, 3L, 
  + 1L, 2L, 3L, 2L, 1L, 3L, 2L, 3L, 1L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 
  + 3L, 2L, 1L, 1L, 3L, 2L, 3L, 1L, 1L, 3L, 2L, 1L, 2L, 3L, 3L, 1L, 
  + 2L, 2L, 1L, 3L, 2L, 3L, 1L, 3L, 1L, 2L, 1L, 2L, 3L, 2L, 3L, 1L, 
  + 3L, 1L, 2L, 1L, 3L, 2L, 1L, 3L, 2L, 3L, 1L, 2L, 2L, 1L, 3L, 1L, 
  + 2L, 3L, 3L, 1L, 2L, 3L, 2L, 1L, 2L, 1L, 3L, 1L, 1L, 3L, 2L, 3L, 
  + 2L, 1L, 3L, 2L, 1L, 1L, 2L), .Label = c("10000U", "5000U", "Placebo"
  + ), class = "factor"), Age = c(65L, 70L, 64L, 59L, 76L, 59L, 72L, 
  + 40L, 52L, 47L, 57L, 47L, 70L, 49L, 59L, 64L, 45L, 66L, 49L, 54L, 
  + 47L, 31L, 53L, 61L, 40L, 67L, 54L, 41L, 66L, 68L, 41L, 77L, 41L, 
  + 56L, 46L, 46L, 47L, 35L, 58L, 62L, 73L, 52L, 53L, 69L, 55L, 52L, 
  + 51L, 56L, 65L, 35L, 43L, 61L, 43L, 64L, 57L, 60L, 44L, 41L, 51L, 
  + 57L, 42L, 48L, 57L, 39L, 67L, 39L, 69L, 54L, 67L, 58L, 72L, 65L, 
  + 68L, 75L, 26L, 36L, 72L, 54L, 64L, 39L, 54L, 48L, 83L, 74L, 41L, 
  + 65L, 79L, 63L, 63L, 34L, 42L, 57L, 68L, 51L, 51L, 61L, 42L, 73L, 
  + 57L, 59L, 57L, 68L, 55L, 46L, 79L, 43L, 50L, 39L, 57L, 65L, 70L, 
  + 64L, 59L, 76L, 59L, 72L, 40L, 52L, 47L, 57L, 47L, 70L, 49L, 59L, 
  + 64L, 45L, 66L, 49L, 54L, 47L, 31L, 53L, 61L, 40L, 67L, 54L, 41L, 
  + 66L, 68L, 41L, 77L, 41L, 56L, 46L, 46L, 47L, 35L, 58L, 62L, 73L, 
  + 52L, 53L, 69L, 55L, 52L, 51L, 56L, 65L, 35L, 43L, 61L, 43L, 64L, 
  + 57L, 60L, 44L, 41L, 51L, 57L, 42L, 48L, 57L, 39L, 67L, 39L, 69L, 
  + 54L, 67L, 58L, 72L, 65L, 68L, 75L, 26L, 36L, 72L, 54L, 64L, 39L, 
  + 54L, 48L, 83L, 74L, 41L, 65L, 79L, 63L, 63L, 34L, 42L, 57L, 68L, 
  + 51L, 51L, 61L, 42L, 73L, 57L, 59L, 57L, 68L, 55L, 46L, 79L, 43L, 
  + 50L, 39L, 57L, 65L, 70L, 64L, 59L, 76L, 59L, 72L, 40L, 52L, 47L, 
  + 57L, 47L, 70L, 49L, 59L, 64L, 45L, 66L, 49L, 54L, 47L, 31L, 53L, 
  + 61L, 40L, 67L, 54L, 41L, 66L, 68L, 41L, 77L, 41L, 56L, 46L, 46L, 
  + 47L, 35L, 58L, 62L, 73L, 52L, 53L, 69L, 55L, 52L, 51L, 56L, 65L, 
  + 35L, 43L, 61L, 43L, 64L, 57L, 60L, 44L, 41L, 51L, 57L, 42L, 48L, 
  + 57L, 39L, 67L, 39L, 69L, 54L, 67L, 58L, 72L, 65L, 68L, 75L, 26L, 
  + 36L, 72L, 54L, 64L, 39L, 54L, 48L, 83L, 74L, 41L, 65L, 79L, 63L, 
  + 63L, 34L, 42L, 57L, 68L, 51L, 51L, 61L, 42L, 73L, 57L, 59L, 57L, 
  + 68L, 55L, 46L, 79L, 43L, 50L, 39L, 57L, 65L, 70L, 64L, 59L, 76L, 
  + 59L, 72L, 40L, 52L, 47L, 57L, 47L, 70L, 49L, 59L, 64L, 45L, 66L, 
  + 49L, 54L, 47L, 31L, 53L, 61L, 40L, 67L, 54L, 41L, 66L, 68L, 41L, 
  + 77L, 41L, 56L, 46L, 46L, 47L, 35L, 58L, 62L, 73L, 52L, 53L, 69L, 
  + 55L, 52L, 51L, 56L, 65L, 35L, 43L, 61L, 43L, 64L, 57L, 60L, 44L, 
  + 41L, 51L, 57L, 42L, 48L, 57L, 39L, 67L, 39L, 69L, 54L, 67L, 58L, 
  + 72L, 65L, 68L, 75L, 26L, 36L, 72L, 54L, 64L, 39L, 54L, 48L, 83L, 
  + 74L, 41L, 65L, 79L, 63L, 63L, 34L, 42L, 57L, 68L, 51L, 51L, 61L, 
  + 42L, 73L, 57L, 59L, 57L, 68L, 55L, 46L, 79L, 43L, 50L, 39L, 57L, 
  + 65L, 70L, 64L, 59L, 76L, 59L, 72L, 40L, 52L, 47L, 57L, 47L, 70L, 
  + 49L, 59L, 64L, 45L, 66L, 49L, 54L, 47L, 31L, 53L, 61L, 40L, 67L, 
  + 54L, 41L, 66L, 68L, 41L, 77L, 41L, 56L, 46L, 46L, 47L, 35L, 58L, 
  + 62L, 73L, 52L, 53L, 69L, 55L, 52L, 51L, 56L, 65L, 35L, 43L, 61L, 
  + 43L, 64L, 57L, 60L, 44L, 41L, 51L, 57L, 42L, 48L, 57L, 39L, 67L, 
  + 39L, 69L, 54L, 67L, 58L, 72L, 65L, 68L, 75L, 26L, 36L, 72L, 54L, 
  + 64L, 39L, 54L, 48L, 83L, 74L, 41L, 65L, 79L, 63L, 63L, 34L, 42L, 
  + 57L, 68L, 51L, 51L, 61L, 42L, 73L, 57L, 59L, 57L, 68L, 55L, 46L, 
  + 79L, 43L, 50L, 39L, 57L), W0 = c(32L, 60L, 44L, 53L, 53L, 49L, 
  + 42L, 34L, 41L, 27L, 48L, 34L, 49L, 46L, 56L, 59L, 62L, 50L, 42L, 
  + 53L, 67L, 44L, 65L, 56L, 30L, 47L, 50L, 34L, 39L, 43L, 46L, 52L, 
  + 38L, 33L, 28L, 34L, 39L, 29L, 52L, 52L, 54L, 52L, 47L, 44L, 42L, 
  + 42L, 44L, 60L, 60L, 50L, 38L, 44L, 54L, 54L, 56L, 51L, 53L, 36L, 
  + 59L, 49L, 50L, 46L, 55L, 46L, 34L, 57L, 41L, 49L, 42L, 31L, 50L, 
  + 35L, 38L, 53L, 42L, 53L, 46L, 50L, 43L, 46L, 41L, 33L, 36L, 33L, 
  + 37L, 24L, 42L, 30L, 42L, 49L, 58L, 26L, 37L, 40L, 33L, 41L, 46L, 
  + 40L, 40L, 61L, 35L, 58L, 49L, 52L, 45L, 67L, 57L, 63L, 53L, 32L, 
  + 60L, 44L, 53L, 53L, 49L, 42L, 34L, 41L, 27L, 48L, 34L, 49L, 46L, 
  + 56L, 59L, 62L, 50L, 42L, 53L, 67L, 44L, 65L, 56L, 30L, 47L, 50L, 
  + 34L, 39L, 43L, 46L, 52L, 38L, 33L, 28L, 34L, 39L, 29L, 52L, 52L, 
  + 54L, 52L, 47L, 44L, 42L, 42L, 44L, 60L, 60L, 50L, 38L, 44L, 54L, 
  + 54L, 56L, 51L, 53L, 36L, 59L, 49L, 50L, 46L, 55L, 46L, 34L, 57L, 
  + 41L, 49L, 42L, 31L, 50L, 35L, 38L, 53L, 42L, 53L, 46L, 50L, 43L, 
  + 46L, 41L, 33L, 36L, 33L, 37L, 24L, 42L, 30L, 42L, 49L, 58L, 26L, 
  + 37L, 40L, 33L, 41L, 46L, 40L, 40L, 61L, 35L, 58L, 49L, 52L, 45L, 
  + 67L, 57L, 63L, 53L, 32L, 60L, 44L, 53L, 53L, 49L, 42L, 34L, 41L, 
  + 27L, 48L, 34L, 49L, 46L, 56L, 59L, 62L, 50L, 42L, 53L, 67L, 44L, 
  + 65L, 56L, 30L, 47L, 50L, 34L, 39L, 43L, 46L, 52L, 38L, 33L, 28L, 
  + 34L, 39L, 29L, 52L, 52L, 54L, 52L, 47L, 44L, 42L, 42L, 44L, 60L, 
  + 60L, 50L, 38L, 44L, 54L, 54L, 56L, 51L, 53L, 36L, 59L, 49L, 50L, 
  + 46L, 55L, 46L, 34L, 57L, 41L, 49L, 42L, 31L, 50L, 35L, 38L, 53L, 
  + 42L, 53L, 46L, 50L, 43L, 46L, 41L, 33L, 36L, 33L, 37L, 24L, 42L, 
  + 30L, 42L, 49L, 58L, 26L, 37L, 40L, 33L, 41L, 46L, 40L, 40L, 61L, 
  + 35L, 58L, 49L, 52L, 45L, 67L, 57L, 63L, 53L, 32L, 60L, 44L, 53L, 
  + 53L, 49L, 42L, 34L, 41L, 27L, 48L, 34L, 49L, 46L, 56L, 59L, 62L, 
  + 50L, 42L, 53L, 67L, 44L, 65L, 56L, 30L, 47L, 50L, 34L, 39L, 43L, 
  + 46L, 52L, 38L, 33L, 28L, 34L, 39L, 29L, 52L, 52L, 54L, 52L, 47L, 
  + 44L, 42L, 42L, 44L, 60L, 60L, 50L, 38L, 44L, 54L, 54L, 56L, 51L, 
  + 53L, 36L, 59L, 49L, 50L, 46L, 55L, 46L, 34L, 57L, 41L, 49L, 42L, 
  + 31L, 50L, 35L, 38L, 53L, 42L, 53L, 46L, 50L, 43L, 46L, 41L, 33L, 
  + 36L, 33L, 37L, 24L, 42L, 30L, 42L, 49L, 58L, 26L, 37L, 40L, 33L, 
  + 41L, 46L, 40L, 40L, 61L, 35L, 58L, 49L, 52L, 45L, 67L, 57L, 63L, 
  + 53L, 32L, 60L, 44L, 53L, 53L, 49L, 42L, 34L, 41L, 27L, 48L, 34L, 
  + 49L, 46L, 56L, 59L, 62L, 50L, 42L, 53L, 67L, 44L, 65L, 56L, 30L, 
  + 47L, 50L, 34L, 39L, 43L, 46L, 52L, 38L, 33L, 28L, 34L, 39L, 29L, 
  + 52L, 52L, 54L, 52L, 47L, 44L, 42L, 42L, 44L, 60L, 60L, 50L, 38L, 
  + 44L, 54L, 54L, 56L, 51L, 53L, 36L, 59L, 49L, 50L, 46L, 55L, 46L, 
  + 34L, 57L, 41L, 49L, 42L, 31L, 50L, 35L, 38L, 53L, 42L, 53L, 46L, 
  + 50L, 43L, 46L, 41L, 33L, 36L, 33L, 37L, 24L, 42L, 30L, 42L, 49L, 
  + 58L, 26L, 37L, 40L, 33L, 41L, 46L, 40L, 40L, 61L, 35L, 58L, 49L, 
  + 52L, 45L, 67L, 57L, 63L, 53L), Fem = c(1L, 1L, 1L, 1L, 1L, 1L, 
  + 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 
  + 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 
  + 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 
  + 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 
  + 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 
  + 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 
  + 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 
  + 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 
  + 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 
  + 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 
  + 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 
  + 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 
  + 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 
  + 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 
  + 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 
  + 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 
  + 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 
  + 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  + 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 
  + 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 
  + 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 
  + 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 
  + 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 
  + 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 
  + 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 
  + 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 
  + 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 
  + 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 
  + 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 
  + 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 
  + 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 
  + 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 
  + 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 
  + 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L), Week = c(2L, 2L, 
  + 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  + 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  + 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  + 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  + 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  + 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
  + 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 
  + 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
  + 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
  + 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
  + 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
  + 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
  + 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
  + 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
  + 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
  + 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
  + 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
  + 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
  + 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
  + 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 
  + 8L, 8L, 8L, 8L, 8L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
  + 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
  + 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
  + 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
  + 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
  + 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
  + 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
  + 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 
  + 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 16L, 16L, 16L, 16L, 
  + 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
  + 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
  + 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
  + 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
  + 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
  + 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
  + 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
  + 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 
  + 16L), Total = c(30L, 26L, 20L, 61L, 35L, 34L, 32L, 33L, 32L, 
  + 10L, 41L, 19L, 47L, 35L, 44L, 48L, 60L, 53L, 42L, 56L, 64L, 40L, 
  + 58L, 54L, 33L, NA, 43L, 29L, 41L, 31L, 26L, 44L, 19L, 38L, 16L, 
  + 23L, 37L, 42L, 55L, 30L, 52L, 44L, 45L, 34L, 39L, 14L, 34L, 57L, 
  + 53L, 50L, 27L, NA, 53L, 32L, 55L, 50L, 56L, 29L, 53L, 50L, 38L, 
  + 48L, 34L, 44L, 31L, 48L, 40L, 25L, 30L, 18L, 27L, 24L, 25L, 40L, 
  + 48L, 45L, 47L, 42L, 24L, 39L, 30L, 27L, 15L, 32L, NA, 29L, 23L, 
  + 22L, 46L, 25L, 46L, 26L, NA, 24L, 10L, 50L, NA, 28L, 16L, 52L, 
  + 21L, 38L, 45L, 46L, 46L, 63L, NA, 51L, 38L, 24L, 27L, 23L, 64L, 
  + 48L, 43L, 32L, 21L, 34L, 31L, 32L, 21L, 44L, 45L, 48L, 56L, 60L, 
  + 52L, 43L, 52L, 65L, 32L, 55L, 52L, 25L, 54L, 51L, 27L, 33L, 29L, 
  + 29L, 47L, 20L, 40L, 11L, 16L, 39L, 35L, 51L, 43L, 52L, 33L, 41L, 
  + 29L, 38L, 9L, 32L, 53L, 55L, NA, 16L, 46L, 51L, 40L, 44L, 50L, 
  + 47L, 24L, 45L, 48L, 42L, 46L, 26L, 47L, 25L, 50L, 42L, 30L, 40L, 
  + 23L, 43L, 34L, 21L, 38L, 26L, 52L, 45L, 52L, 17L, 25L, 44L, 25L, 
  + 16L, 31L, NA, 18L, 30L, 21L, 41L, 30L, 46L, 27L, 23L, 25L, 13L, 
  + 22L, 41L, 29L, 18L, 61L, 29L, 50L, 36L, 36L, 33L, 71L, 36L, 46L, 
  + NA, 37L, 41L, 26L, 62L, 49L, 48L, 43L, 27L, 35L, 32L, 35L, 24L, 
  + 48L, 49L, 54L, 55L, 64L, 57L, 33L, 54L, 64L, 36L, NA, 48L, 29L, 
  + 43L, 46L, 21L, 39L, 28L, 33L, 50L, 27L, 48L, 7L, 15L, 39L, 24L, 
  + 52L, 45L, 54L, 54L, 45L, 28L, 47L, 9L, 35L, 52L, 62L, 46L, 19L, 
  + 26L, 56L, 52L, 50L, 56L, 53L, 32L, 44L, 56L, 43L, 57L, 40L, 50L, 
  + NA, 50L, 38L, 41L, 43L, 26L, 32L, 28L, 33L, 44L, 37L, 51L, 45L, 
  + 60L, 37L, 15L, 46L, 30L, 17L, 27L, NA, 20L, 36L, 25L, 43L, 49L, 
  + 50L, 22L, 18L, 37L, 16L, 28L, 41L, 30L, 25L, 68L, 30L, 53L, NA, 
  + NA, 44L, 66L, 23L, 50L, 33L, 39L, 65L, 35L, NA, 41L, 48L, 42L, 
  + 32L, 37L, 6L, 57L, 28L, 44L, 53L, 49L, 57L, 67L, 61L, 37L, 55L, 
  + 62L, 42L, 56L, 52L, 32L, 46L, 49L, 22L, 37L, 33L, 45L, 50L, 29L, 
  + 49L, 13L, 17L, 45L, 29L, 54L, 47L, 51L, 46L, 43L, 35L, 39L, 16L, 
  + 54L, 53L, 67L, 50L, 23L, 30L, 39L, 42L, 53L, 59L, 51L, 45L, 50L, 
  + 49L, 42L, 57L, 49L, 46L, NA, 50L, 50L, 41L, 36L, 33L, 40L, 34L, 
  + 42L, 47L, 37L, 52L, 50L, 54L, 36L, 21L, 46L, 28L, 22L, 49L, NA, 
  + 25L, 41L, 26L, 49L, 55L, 56L, 38L, 34L, NA, 32L, 34L, 58L, 37L, 
  + 33L, 59L, 35L, 47L, 40L, 45L, 46L, 68L, NA, 50L, 36L, 36L, 67L, 
  + 35L, NA, 51L, 51L, 46L, 38L, 36L, 14L, 51L, 28L, 44L, 56L, 60L, 
  + 58L, 66L, 54L, 43L, 51L, 64L, 43L, 60L, 53L, 32L, 50L, 53L, 22L, 
  + 37L, 38L, 56L, 49L, 32L, 44L, 21L, 29L, 43L, 42L, 57L, 46L, 57L, 
  + 47L, 41L, 41L, 39L, 33L, 53L, 58L, NA, 57L, 26L, 34L, 9L, 47L, 
  + 52L, 53L, 51L, 36L, 48L, 57L, 46L, 49L, 47L, 51L, NA, 49L, 56L, 
  + 31L, 45L, 41L, 47L, 28L, 53L, 53L, 43L, 53L, 52L, 59L, 38L, 25L, 
  + 44L, 30L, 41L, 60L, NA, 41L, 43L, 33L, 54L, 58L, 60L, 35L, 36L, 
  + 38L, 16L, 36L, 53L, 44L, 48L, 71L, 48L, 59L, 52L, 54L, 48L, 71L, 
  + 52L, 54L, 51L)), .Names = c("Site", "ID", "Treat", "Age", "W0", 
  + "Fem", "Week", "Total"), class = "data.frame", row.names = c(NA, 
  + -545L))
  > 
  > 
  > library("partykit")
  Loading required package: grid
  Loading required package: libcoin
  Loading required package: mvtnorm
  > library("rpart")
  > 
  > fac <- c(1,3,6)
  > for(j in 1:length(fac)) datLB[,fac[j]] <- as.factor(datLB[,fac[j]])
  > dat <- subset(datLB,Week==16)
  > dat <- na.omit(dat)
  > fit <- rpart(Total ~ Site + Treat + Age + W0, 
  +              method = "anova", data = dat)
  > f <- as.party(fit)
  > plot(f,tp_args = list(id = FALSE))
  > f[10]$node$split
  $varid
  [1] 3
  
  $breaks
  NULL
  
  $index
  [1] 2 2 1
  
  $right
  [1] TRUE
  
  $prob
  [1] 0 1
  
  $info
  NULL
  
  attr(,"class")
  [1] "partysplit"
  > 
  > ### factors with empty levels in learning sample
  > if (require("mlbench")) {
  +     data("Vowel", package = "mlbench")
  +     ct <- ctree(V2 ~ V1, data = Vowel[1:200,]) ### only levels 1:4 in V1
  +     try(p1 <- predict(ct, newdata = Vowel)) ### 14 levels in V1
  + }
  Loading required package: mlbench
  Warning message:
  In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE,  :
    there is no package called 'mlbench'
  > 
  > ### deal with empty levels for teststat = "quad" by
  > ### removing elements of the teststatistic with zero variance
  > ### reported by Wei-Yin Loh <loh@stat.wisc.edu>
  > tdata <-
  + structure(list(ytrain = structure(c(3L, 7L, 3L, 2L, 1L, 6L, 2L, 
  + 1L, 1L, 2L, 1L, 2L, 3L, 3L, 2L, 1L, 2L, 6L, 2L, 4L, 6L, 1L, 2L, 
  + 3L, 7L, 6L, 4L, 6L, 2L, 2L, 1L, 2L, 6L, 1L, 7L, 1L, 3L, 6L, 2L, 
  + 1L, 7L, 2L, 7L, 2L, 3L, 2L, 1L, 1L, 3L, 1L, 6L, 2L, 2L, 2L, 2L, 
  + 2L, 1L, 1L, 6L, 6L, 7L, 2L, 2L, 2L, 2L, 2L, 1L, 3L, 6L, 5L, 1L, 
  + 1L, 4L, 7L, 2L, 3L, 3L, 3L, 1L, 8L, 1L, 6L, 2L, 8L, 3L, 4L, 6L, 
  + 2L, 7L, 3L, 6L, 6L, 1L, 1L, 2L, 6L, 3L, 3L, 1L, 2L, 3L, 1L, 2L, 
  + 7L, 2L, 3L, 6L, 2L, 5L, 2L, 2L, 2L, 1L, 3L, 3L, 7L, 3L, 2L, 3L, 
  + 3L, 1L, 6L, 1L, 1L, 1L, 7L, 1L, 3L, 7L, 6L, 1L, 3L, 3L, 6L, 4L, 
  + 2L, 3L, 2L, 8L, 3L, 4L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 4L, 6L, 
  + 4L, 8L, 2L, 2L, 3L, 3L, 2L, 3L, 6L, 2L, 1L, 2L, 2L, 7L, 2L, 1L, 
  + 1L, 7L, 2L, 7L, 6L, 6L, 6L), .Label = c("0", "1", "2", "3", "4", 
  + "5", "6", "7"), class = "factor"), landmass = c(5L, 3L, 4L, 6L, 
  + 3L, 4L, 1L, 2L, 2L, 6L, 3L, 1L, 5L, 5L, 1L, 3L, 1L, 4L, 1L, 5L, 
  + 4L, 2L, 1L, 5L, 3L, 4L, 5L, 4L, 4L, 1L, 4L, 1L, 4L, 2L, 5L, 2L, 
  + 4L, 4L, 6L, 1L, 1L, 3L, 3L, 3L, 4L, 1L, 1L, 2L, 4L, 1L, 4L, 4L, 
  + 3L, 2L, 6L, 3L, 3L, 2L, 4L, 4L, 3L, 3L, 3L, 3L, 1L, 6L, 1L, 4L, 
  + 4L, 2L, 1L, 1L, 5L, 3L, 3L, 6L, 5L, 5L, 3L, 5L, 3L, 4L, 1L, 5L, 
  + 5L, 5L, 4L, 6L, 5L, 5L, 4L, 4L, 3L, 3L, 4L, 4L, 5L, 5L, 3L, 6L, 
  + 4L, 1L, 6L, 5L, 1L, 4L, 4L, 6L, 5L, 3L, 1L, 6L, 1L, 4L, 4L, 5L, 
  + 5L, 3L, 5L, 5L, 2L, 6L, 2L, 2L, 6L, 3L, 1L, 5L, 3L, 4L, 4L, 5L, 
  + 4L, 4L, 5L, 6L, 4L, 4L, 5L, 5L, 5L, 1L, 1L, 1L, 4L, 2L, 3L, 3L, 
  + 5L, 5L, 4L, 5L, 4L, 6L, 2L, 4L, 5L, 1L, 5L, 4L, 3L, 2L, 1L, 1L, 
  + 5L, 6L, 3L, 2L, 5L, 6L, 3L, 4L, 4L, 4L), zone = c(1L, 1L, 1L, 
  + 3L, 1L, 2L, 4L, 3L, 3L, 2L, 1L, 4L, 1L, 1L, 4L, 1L, 4L, 1L, 4L, 
  + 1L, 2L, 3L, 4L, 1L, 1L, 4L, 1L, 2L, 1L, 4L, 4L, 4L, 1L, 3L, 1L, 
  + 4L, 2L, 2L, 3L, 4L, 4L, 1L, 1L, 1L, 1L, 4L, 4L, 3L, 1L, 4L, 1L, 
  + 1L, 4L, 3L, 2L, 1L, 1L, 4L, 2L, 4L, 1L, 1L, 4L, 1L, 4L, 1L, 4L, 
  + 4L, 4L, 4L, 4L, 4L, 1L, 1L, 4L, 2L, 1L, 1L, 4L, 1L, 1L, 4L, 4L, 
  + 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 4L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 
  + 1L, 4L, 4L, 1L, 1L, 4L, 4L, 2L, 2L, 1L, 1L, 4L, 2L, 4L, 1L, 1L, 
  + 1L, 1L, 1L, 1L, 1L, 4L, 2L, 3L, 3L, 1L, 1L, 4L, 1L, 1L, 2L, 1L, 
  + 1L, 4L, 4L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 4L, 4L, 4L, 1L, 4L, 1L, 
  + 1L, 1L, 1L, 2L, 1L, 1L, 2L, 4L, 1L, 1L, 4L, 1L, 1L, 4L, 3L, 4L, 
  + 4L, 1L, 2L, 1L, 4L, 1L, 3L, 1L, 2L, 2L, 2L), area = c(648L, 29L, 
  + 2388L, 0L, 0L, 1247L, 0L, 2777L, 2777L, 7690L, 84L, 19L, 1L, 
  + 143L, 0L, 31L, 23L, 113L, 0L, 47L, 600L, 8512L, 0L, 6L, 111L, 
  + 274L, 678L, 28L, 474L, 9976L, 4L, 0L, 623L, 757L, 9561L, 1139L, 
  + 2L, 342L, 0L, 51L, 115L, 9L, 128L, 43L, 22L, 0L, 49L, 284L, 1001L, 
  + 21L, 28L, 1222L, 1L, 12L, 18L, 337L, 547L, 91L, 268L, 10L, 108L, 
  + 249L, 0L, 132L, 0L, 0L, 109L, 246L, 36L, 215L, 28L, 112L, 1L, 
  + 93L, 103L, 1904L, 1648L, 435L, 70L, 21L, 301L, 323L, 11L, 372L, 
  + 98L, 181L, 583L, 0L, 236L, 10L, 30L, 111L, 0L, 3L, 587L, 118L, 
  + 333L, 0L, 0L, 0L, 1031L, 1973L, 1L, 1566L, 0L, 447L, 783L, 0L, 
  + 140L, 41L, 0L, 268L, 128L, 1267L, 925L, 121L, 195L, 324L, 212L, 
  + 804L, 76L, 463L, 407L, 1285L, 300L, 313L, 9L, 11L, 237L, 26L, 
  + 0L, 2150L, 196L, 72L, 1L, 30L, 637L, 1221L, 99L, 288L, 66L, 0L, 
  + 0L, 0L, 2506L, 63L, 450L, 41L, 185L, 36L, 945L, 514L, 57L, 1L, 
  + 5L, 164L, 781L, 0L, 84L, 236L, 245L, 178L, 0L, 9363L, 22402L, 
  + 15L, 0L, 912L, 333L, 3L, 256L, 905L, 753L, 391L), population = c(16L, 
  + 3L, 20L, 0L, 0L, 7L, 0L, 28L, 28L, 15L, 8L, 0L, 0L, 90L, 0L, 
  + 10L, 0L, 3L, 0L, 1L, 1L, 119L, 0L, 0L, 9L, 7L, 35L, 4L, 8L, 24L, 
  + 0L, 0L, 2L, 11L, 1008L, 28L, 0L, 2L, 0L, 2L, 10L, 1L, 15L, 5L, 
  + 0L, 0L, 6L, 8L, 47L, 5L, 0L, 31L, 0L, 0L, 1L, 5L, 54L, 0L, 1L, 
  + 1L, 17L, 61L, 0L, 10L, 0L, 0L, 8L, 6L, 1L, 1L, 6L, 4L, 5L, 11L, 
  + 0L, 157L, 39L, 14L, 3L, 4L, 57L, 7L, 2L, 118L, 2L, 6L, 17L, 0L, 
  + 3L, 3L, 1L, 1L, 0L, 0L, 9L, 6L, 13L, 0L, 0L, 0L, 2L, 77L, 0L, 
  + 2L, 0L, 20L, 12L, 0L, 16L, 14L, 0L, 2L, 3L, 5L, 56L, 18L, 9L, 
  + 4L, 1L, 84L, 2L, 3L, 3L, 14L, 48L, 36L, 3L, 0L, 22L, 5L, 0L, 
  + 9L, 6L, 3L, 3L, 0L, 5L, 29L, 39L, 2L, 15L, 0L, 0L, 0L, 20L, 0L, 
  + 8L, 6L, 10L, 18L, 18L, 49L, 2L, 0L, 1L, 7L, 45L, 0L, 1L, 13L, 
  + 56L, 3L, 0L, 231L, 274L, 0L, 0L, 15L, 60L, 0L, 22L, 28L, 6L, 
  + 8L), language = structure(c(10L, 6L, 8L, 1L, 6L, 10L, 1L, 2L, 
  + 2L, 1L, 4L, 1L, 8L, 6L, 1L, 6L, 1L, 3L, 1L, 10L, 10L, 6L, 1L, 
  + 10L, 5L, 3L, 10L, 10L, 3L, 1L, 6L, 1L, 10L, 2L, 7L, 2L, 3L, 10L, 
  + 1L, 2L, 2L, 6L, 5L, 6L, 3L, 1L, 2L, 2L, 8L, 2L, 10L, 10L, 6L, 
  + 1L, 1L, 9L, 3L, 3L, 10L, 1L, 4L, 4L, 1L, 6L, 1L, 1L, 2L, 3L, 
  + 6L, 1L, 3L, 2L, 7L, 9L, 6L, 10L, 6L, 8L, 1L, 10L, 6L, 3L, 1L, 
  + 9L, 8L, 10L, 10L, 1L, 10L, 8L, 10L, 10L, 4L, 4L, 10L, 10L, 10L, 
  + 10L, 10L, 10L, 8L, 2L, 10L, 10L, 1L, 8L, 10L, 10L, 10L, 6L, 6L, 
  + 1L, 2L, 3L, 10L, 10L, 8L, 6L, 8L, 6L, 2L, 1L, 2L, 2L, 10L, 5L, 
  + 2L, 8L, 6L, 10L, 6L, 8L, 3L, 1L, 7L, 1L, 10L, 6L, 10L, 8L, 10L, 
  + 1L, 1L, 1L, 8L, 6L, 6L, 4L, 8L, 7L, 10L, 10L, 3L, 10L, 1L, 8L, 
  + 9L, 1L, 8L, 10L, 1L, 2L, 1L, 1L, 5L, 6L, 6L, 2L, 10L, 1L, 6L, 
  + 10L, 10L, 10L), .Label = c("1", "2", "3", "4", "5", "6", "7", 
  + "8", "9", "10"), class = "factor"), bars = c(0L, 0L, 2L, 0L, 
  + 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 3L, 0L, 0L, 0L, 0L, 
  + 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 2L, 1L, 0L, 1L, 0L, 0L, 0L, 
  + 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  + 0L, 0L, 0L, 0L, 3L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 3L, 
  + 1L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 3L, 3L, 0L, 0L, 
  + 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 2L, 0L, 
  + 0L, 3L, 0L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 
  + 0L, 0L, 0L, 1L, 0L, 0L, 0L, 3L, 0L, 0L, 0L, 0L, 3L, 3L, 0L, 0L, 
  + 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 5L, 0L, 0L, 0L, 0L, 
  + 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 
  + 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 3L, 0L), stripes = c(3L, 0L, 
  + 0L, 0L, 0L, 2L, 1L, 3L, 3L, 0L, 3L, 3L, 0L, 0L, 0L, 0L, 2L, 0L, 
  + 0L, 0L, 5L, 0L, 0L, 0L, 3L, 2L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 2L, 
  + 0L, 3L, 0L, 0L, 0L, 5L, 5L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 3L, 3L, 
  + 3L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 5L, 3L, 3L, 1L, 9L, 0L, 0L, 
  + 0L, 0L, 2L, 0L, 0L, 3L, 0L, 3L, 0L, 2L, 3L, 3L, 0L, 2L, 0L, 0L, 
  + 0L, 0L, 3L, 0L, 5L, 0L, 3L, 2L, 0L, 11L, 2L, 3L, 2L, 3L, 14L, 
  + 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 5L, 3L, 0L, 3L, 1L, 0L, 3L, 
  + 3L, 0L, 5L, 3L, 0L, 2L, 0L, 0L, 0L, 3L, 0L, 0L, 2L, 5L, 0L, 0L, 
  + 0L, 3L, 0L, 0L, 3L, 2L, 0L, 0L, 3L, 0L, 3L, 0L, 0L, 0L, 0L, 3L, 
  + 5L, 0L, 0L, 3L, 0L, 0L, 5L, 5L, 0L, 0L, 0L, 0L, 0L, 3L, 6L, 0L, 
  + 9L, 0L, 13L, 0L, 0L, 0L, 3L, 0L, 0L, 3L, 0L, 0L, 7L), colours = c(5L, 
  + 3L, 3L, 5L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 3L, 2L, 2L, 3L, 3L, 8L, 
  + 2L, 6L, 4L, 3L, 4L, 6L, 4L, 5L, 3L, 3L, 3L, 3L, 2L, 5L, 6L, 5L, 
  + 3L, 2L, 3L, 2L, 3L, 4L, 3L, 3L, 3L, 3L, 2L, 4L, 6L, 3L, 3L, 4L, 
  + 2L, 4L, 3L, 3L, 6L, 7L, 2L, 3L, 3L, 3L, 4L, 3L, 3L, 3L, 2L, 3L, 
  + 7L, 2L, 3L, 4L, 5L, 2L, 2L, 6L, 3L, 3L, 2L, 3L, 4L, 3L, 2L, 3L, 
  + 3L, 3L, 2L, 4L, 2L, 4L, 4L, 3L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 4L, 
  + 3L, 3L, 3L, 2L, 4L, 2L, 3L, 7L, 2L, 5L, 3L, 3L, 3L, 3L, 3L, 2L, 
  + 3L, 2L, 3L, 4L, 3L, 3L, 2L, 3L, 4L, 6L, 2L, 4L, 2L, 3L, 2L, 7L, 
  + 4L, 4L, 2L, 3L, 3L, 2L, 4L, 2L, 5L, 4L, 4L, 4L, 5L, 4L, 4L, 4L, 
  + 4L, 2L, 2L, 4L, 3L, 4L, 3L, 4L, 2L, 3L, 2L, 2L, 6L, 4L, 5L, 3L, 
  + 3L, 6L, 3L, 2L, 4L, 4L, 7L, 2L, 3L, 4L, 4L, 4L, 5L), red = c(1L, 
  + 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 
  + 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  + 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  + 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 
  + 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 
  + 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  + 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 
  + 0L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 
  + 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 
  + 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  + 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), green = c(1L, 
  + 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 
  + 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 
  + 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 
  + 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 
  + 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 
  + 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 
  + 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
  + 1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 
  + 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 
  + 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 
  + 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L), blue = c(0L, 
  + 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 
  + 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 
  + 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 
  + 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 
  + 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 
  + 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 
  + 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 
  + 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 
  + 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 
  + 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 
  + 1L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L), gold = c(1L, 
  + 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 
  + 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 
  + 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 
  + 0L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, 
  + 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  + 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 
  + 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 
  + 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 
  + 1L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 0L, 
  + 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 
  + 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L), white = c(1L, 
  + 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 
  + 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 
  + 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 
  + 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 
  + 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  + 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 
  + 1L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  + 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  + 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 
  + 1L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
  + 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 1L), black = c(1L, 
  + 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 
  + 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 
  + 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 
  + 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 
  + 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
  + 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 
  + 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
  + 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
  + 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 
  + 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 
  + 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 1L, 1L), orange = c(0L, 
  + 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 
  + 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 
  + 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  + 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  + 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 
  + 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  + 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 
  + 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 
  + 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 
  + 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
  + 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L), mainhue = structure(c(5L, 
  + 7L, 5L, 2L, 4L, 7L, 8L, 2L, 2L, 2L, 7L, 2L, 7L, 5L, 2L, 4L, 2L, 
  + 5L, 7L, 6L, 2L, 5L, 2L, 4L, 7L, 7L, 7L, 7L, 4L, 7L, 4L, 2L, 4L, 
  + 7L, 7L, 4L, 5L, 7L, 2L, 2L, 2L, 8L, 8L, 7L, 2L, 5L, 2L, 4L, 1L, 
  + 2L, 5L, 5L, 8L, 2L, 2L, 8L, 8L, 8L, 5L, 7L, 4L, 1L, 8L, 2L, 4L, 
  + 2L, 2L, 4L, 4L, 5L, 1L, 2L, 2L, 7L, 2L, 7L, 7L, 7L, 8L, 8L, 8L, 
  + 8L, 5L, 8L, 1L, 7L, 7L, 7L, 7L, 7L, 2L, 7L, 7L, 7L, 7L, 7L, 7L, 
  + 7L, 7L, 2L, 5L, 5L, 2L, 7L, 2L, 7L, 4L, 2L, 3L, 7L, 8L, 2L, 2L, 
  + 6L, 5L, 2L, 7L, 7L, 7L, 5L, 7L, 1L, 7L, 7L, 2L, 8L, 7L, 3L, 7L, 
  + 7L, 5L, 5L, 5L, 5L, 8L, 5L, 2L, 6L, 8L, 7L, 4L, 5L, 2L, 5L, 7L, 
  + 7L, 2L, 7L, 7L, 7L, 5L, 7L, 5L, 7L, 7L, 7L, 7L, 2L, 5L, 4L, 7L, 
  + 8L, 8L, 8L, 7L, 7L, 4L, 7L, 7L, 7L, 7L, 5L, 5L, 5L), .Label = c("black", 
  + "blue", "brown", "gold", "green", "orange", "red", "white"), class = "factor"), 
  +     circles = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 1L, 0L, 0L, 1L, 0L, 1L, 4L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 
  +     1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 
  +     0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L), crosses = c(0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 
  +     1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 2L, 1L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 
  +     0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), saltires = c(0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L), quarters = c(0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 
  +     0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 
  +     0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
  +     0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 4L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 
  +     0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 
  +     0L, 0L, 0L, 0L), sunstars = c(1L, 1L, 1L, 0L, 0L, 1L, 0L, 
  +     0L, 1L, 6L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 22L, 
  +     0L, 0L, 1L, 1L, 14L, 3L, 1L, 0L, 1L, 4L, 1L, 1L, 5L, 0L, 
  +     4L, 1L, 15L, 0L, 1L, 0L, 0L, 0L, 1L, 10L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 7L, 
  +     0L, 0L, 0L, 1L, 0L, 0L, 5L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 1L, 
  +     0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
  +     1L, 1L, 0L, 0L, 1L, 1L, 0L, 4L, 1L, 0L, 1L, 1L, 1L, 2L, 0L, 
  +     6L, 4L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 2L, 5L, 1L, 0L, 4L, 
  +     0L, 1L, 0L, 2L, 0L, 2L, 0L, 1L, 0L, 5L, 5L, 1L, 0L, 0L, 1L, 
  +     0L, 2L, 0L, 0L, 0L, 1L, 0L, 0L, 2L, 1L, 0L, 0L, 1L, 0L, 0L, 
  +     1L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 50L, 1L, 0L, 0L, 7L, 1L, 
  +     5L, 1L, 0L, 0L, 1L), crescent = c(0L, 0L, 1L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 
  +     1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L), triangle = c(0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
  +     0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 
  +     0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 
  +     0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 
  +     0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     1L), icon = c(1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 
  +     1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
  +     1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 
  +     0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 
  +     1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 
  +     0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 
  +     1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
  +     0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L), animate = c(0L, 
  +     1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 
  +     1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 
  +     0L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     1L, 1L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 
  +     0L, 1L, 0L, 0L, 0L, 1L, 1L, 1L), text = c(0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 
  +     0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 
  +     0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 
  +     0L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
  +     0L, 0L, 0L, 0L, 0L), topleft = structure(c(1L, 6L, 4L, 2L, 
  +     2L, 6L, 7L, 2L, 2L, 7L, 6L, 2L, 7L, 4L, 2L, 1L, 6L, 4L, 7L, 
  +     5L, 2L, 4L, 7L, 7L, 7L, 6L, 2L, 7L, 4L, 6L, 6L, 7L, 2L, 2L, 
  +     6L, 3L, 4L, 6L, 7L, 2L, 2L, 7L, 7L, 6L, 7L, 4L, 2L, 3L, 6L, 
  +     2L, 4L, 4L, 7L, 7L, 7L, 7L, 2L, 2L, 4L, 6L, 1L, 1L, 7L, 2L, 
  +     6L, 6L, 2L, 6L, 6L, 1L, 1L, 2L, 7L, 6L, 2L, 6L, 4L, 6L, 4L, 
  +     2L, 4L, 6L, 3L, 7L, 1L, 6L, 1L, 6L, 6L, 6L, 4L, 2L, 2L, 6L, 
  +     7L, 1L, 2L, 6L, 7L, 2L, 4L, 4L, 2L, 6L, 7L, 6L, 4L, 2L, 2L, 
  +     6L, 7L, 7L, 2L, 5L, 4L, 2L, 6L, 6L, 6L, 7L, 7L, 6L, 6L, 6L, 
  +     2L, 7L, 6L, 7L, 2L, 6L, 4L, 4L, 4L, 4L, 6L, 2L, 2L, 5L, 7L, 
  +     6L, 3L, 4L, 2L, 2L, 6L, 4L, 2L, 6L, 6L, 2L, 4L, 6L, 6L, 7L, 
  +     7L, 6L, 6L, 7L, 6L, 1L, 7L, 7L, 7L, 2L, 6L, 1L, 3L, 3L, 6L, 
  +     2L, 2L, 4L, 4L, 4L), .Label = c("black", "blue", "gold", 
  +     "green", "orange", "red", "white"), class = "factor"), botright = structure(c(5L, 
  +     7L, 8L, 7L, 7L, 1L, 2L, 2L, 2L, 2L, 7L, 2L, 7L, 5L, 2L, 7L, 
  +     7L, 5L, 7L, 7L, 2L, 5L, 2L, 4L, 7L, 5L, 7L, 8L, 4L, 7L, 5L, 
  +     2L, 4L, 7L, 7L, 7L, 5L, 7L, 2L, 2L, 2L, 8L, 7L, 7L, 5L, 5L, 
  +     2L, 7L, 1L, 2L, 7L, 7L, 8L, 2L, 2L, 8L, 7L, 7L, 2L, 5L, 4L, 
  +     4L, 7L, 2L, 7L, 7L, 2L, 5L, 5L, 5L, 7L, 2L, 2L, 5L, 2L, 8L, 
  +     7L, 1L, 6L, 2L, 7L, 5L, 4L, 8L, 5L, 7L, 5L, 2L, 7L, 7L, 2L, 
  +     7L, 7L, 2L, 5L, 5L, 8L, 7L, 7L, 2L, 5L, 7L, 2L, 7L, 2L, 7L, 
  +     4L, 2L, 2L, 2L, 8L, 2L, 2L, 5L, 5L, 2L, 1L, 7L, 5L, 5L, 8L, 
  +     1L, 2L, 7L, 7L, 7L, 7L, 3L, 7L, 5L, 5L, 5L, 7L, 2L, 8L, 5L, 
  +     2L, 2L, 8L, 1L, 4L, 7L, 2L, 5L, 1L, 5L, 2L, 7L, 1L, 7L, 2L, 
  +     7L, 5L, 7L, 8L, 7L, 7L, 2L, 1L, 7L, 7L, 8L, 8L, 7L, 7L, 5L, 
  +     8L, 7L, 7L, 7L, 7L, 5L, 3L, 5L), .Label = c("black", "blue", 
  +     "brown", "gold", "green", "orange", "red", "white"), class = "factor")), .Names = c("ytrain", 
  + "landmass", "zone", "area", "population", "language", "bars", 
  + "stripes", "colours", "red", "green", "blue", "gold", "white", 
  + "black", "orange", "mainhue", "circles", "crosses", "saltires", 
  + "quarters", "sunstars", "crescent", "triangle", "icon", "animate", 
  + "text", "topleft", "botright"), row.names = c(NA, -174L), class = "data.frame")
  > tdata$language <-  factor(tdata$language)
  > tdata$ytrain <- factor(tdata$ytrain)
  > 
  > ### was: error
  > model <- ctree(ytrain ~ ., data = tdata, 
  +     control = ctree_control(testtype = "Univariate", splitstat = "maximum"))
  > 
  > if (require("coin")) {
  +   ### check against coin (independence_test automatically
  +   ### removes empty levels)
  +   p <- info_node(node_party(model))$criterion["p.value",]
  +   p[is.na(p)] <- 0
  +   p2 <- sapply(names(p), function(n)
  +     pvalue(independence_test(ytrain ~ .,
  +     data = tdata[, c("ytrain", n)], teststat = "quad")))
  +   stopifnot(max(abs(p - p2)) < sqrt(.Machine$double.eps))
  + 
  +   p <- info_node(node_party(model[2]))$criterion["p.value",]
  +   p[is.na(p)] <- 0
  +   p2 <- sapply(names(p), function(n)
  +     pvalue(independence_test(ytrain ~ .,
  +     data = tdata[tdata$language != "8", c("ytrain", n)], 
  +     teststat = "quad")))
  +   stopifnot(max(abs(p - p2)) < sqrt(.Machine$double.eps))
  + 
  +   p <- info_node(node_party(model[3]))$criterion["p.value",]
  +   p[is.na(p)] <- 0
  +   p2 <- sapply(names(p), function(n)
  +     pvalue(independence_test(ytrain ~ .,
  +     data = tdata[!(tdata$language %in% c("2", "4", "8")), 
  +                  c("ytrain", n)], 
  +     teststat = "quad")))
  +   stopifnot(max(abs(p - p2)) < sqrt(.Machine$double.eps))
  + }
  Loading required package: coin
  Warning message:
  In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE,  :
    there is no package called 'coin'
  > 
  > ### check coersion of constparties to simpleparties
  > ### containing terminal nodes without corresponding observations
  > ## create party
  > data("WeatherPlay", package = "partykit")
  > py <- party(
  +   partynode(1L,
  +     split = partysplit(1L, index = 1:3),
  +     kids = list(
  +       partynode(2L,
  +         split = partysplit(3L, breaks = 75),
  +         kids = list(
  +           partynode(3L, info = "yes"),
  +           partynode(4L, info = "no"))),
  +       partynode(5L, 
  +         split = partysplit(3L, breaks = 20),
  +         kids = list(
  +           partynode(6L, info = "no"),
  +           partynode(7L, info = "yes"))),
  +       partynode(8L,
  +         split = partysplit(4L, index = 1:2),
  +         kids = list(
  +           partynode(9L, info = "yes"),
  +           partynode(10L, info = "no"))))),
  +   WeatherPlay)
  > names(py) <- LETTERS[nodeids(py)]
  > 
  > pn <- node_party(py)
  > cp <- party(pn,
  +   data = WeatherPlay,
  +   fitted = data.frame(
  +     "(fitted)" = fitted_node(pn, data = WeatherPlay),
  +     "(response)" = WeatherPlay$play,
  +     check.names = FALSE),
  +   terms = terms(play ~ ., data = WeatherPlay),
  + )
  > print(cp)
  [1] root
  |   [2] outlook in sunny
  |   |   [3] humidity <= 75: yes
  |   |   [4] humidity > 75: no
  |   [5] outlook in overcast
  |   |   [6] humidity <= 20: no
  |   |   [7] humidity > 20: yes
  |   [8] outlook in rainy
  |   |   [9] windy in false: yes
  |   |   [10] windy in true: no
  > cp <- as.constparty(cp)
  > 
  > nd <- data.frame(outlook = factor("overcast", levels = levels(WeatherPlay$outlook)), 
  +                  humidity = 10, temperature = 10, windy = "yes")
  > try(predict(cp, type = "node", newdata = nd))
  Error in model.frame.default(delete.response(object$terms), newdata, xlev = xlev) : 
    factor windy has new level yes
  > try(predict(cp, type = "response", newdata = nd))
  Error in model.frame.default(delete.response(object$terms), newdata, xlev = xlev) : 
    factor windy has new level yes
  > as.simpleparty(cp)
  
  Model formula:
  play ~ outlook + temperature + humidity + windy
  
  Fitted party:
  [1] root
  |   [2] outlook in sunny
  |   |   [3] humidity <= 75: yes (n = 2, err = 0.0%)
  |   |   [4] humidity > 75: no (n = 3, err = 0.0%)
  |   [5] outlook in overcast
  |   |   [6] humidity <= 20: NA (n = 0, err = NA)
  |   |   [7] humidity > 20: yes (n = 4, err = 0.0%)
  |   [8] outlook in rainy
  |   |   [9] windy in false: yes (n = 3, err = 0.0%)
  |   |   [10] windy in true: no (n = 2, err = 0.0%)
  
  Number of inner nodes:    4
  Number of terminal nodes: 6
  > print(cp)
  
  Model formula:
  play ~ outlook + temperature + humidity + windy
  
  Fitted party:
  [1] root
  |   [2] outlook in sunny
  |   |   [3] humidity <= 75: yes (n = 2, err = 0.0%)
  |   |   [4] humidity > 75: no (n = 3, err = 0.0%)
  |   [5] outlook in overcast
  |   |   [6] humidity <= 20: NA (n = 0, err = NA)
  |   |   [7] humidity > 20: yes (n = 4, err = 0.0%)
  |   [8] outlook in rainy
  |   |   [9] windy in false: yes (n = 3, err = 0.0%)
  |   |   [10] windy in true: no (n = 2, err = 0.0%)
  
  Number of inner nodes:    4
  Number of terminal nodes: 6
  > 
  > ### scores
  > y <- gl(3, 10, ordered = TRUE)
  > x <- rnorm(length(y))  
  > x <- ordered(cut(x, 3))
  > d <- data.frame(y = y, x = x)
  > 
  > ### partykit with scores
  > ct11 <- partykit::ctree(y ~ x, data = d) 
  > ct12 <- partykit::ctree(y ~ x, data = d, 
  +                         scores = list(y = c(1, 4, 5)))
  > ct13 <- partykit::ctree(y ~ x, data = d, 
  +                         scores = list(y = c(1, 4, 5), x = c(1, 5, 6)))
  > 
  > ### party with scores
  > ct21 <- party::ctree(y ~ x, data = d) 
  Error in loadNamespace(x) : there is no package called 'party'
  Calls: loadNamespace -> withRestarts -> withOneRestart -> doWithOneRestart
  Execution halted
Running the tests in ‘tests/constparty.R’ failed.
Complete output:
  > ### R code from vignette source 'constparty.Rnw'
  > 
  > ### test here after removal of RWeka dependent code
  > 
  > ###################################################
  > ### code chunk number 1: setup
  > ###################################################
  > options(width = 70)
  > library("partykit")
  Loading required package: grid
  Loading required package: libcoin
  Loading required package: mvtnorm
  > library("XML") ### for pmmlTreeModel
  Error in library("XML") : there is no package called 'XML'
  Execution halted
Running the tests in ‘tests/regtest-cforest.R’ failed.
Complete output:
  > suppressWarnings(RNGversion("3.5.2"))
  > 
  > library("partykit")
  Loading required package: grid
  Loading required package: libcoin
  Loading required package: mvtnorm
  > stopifnot(require("party"))
  Loading required package: party
  Error: require("party") is not TRUE
  In addition: Warning message:
  In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE,  :
    there is no package called 'party'
  Execution halted
Running the tests in ‘tests/regtest-glmtree.R’ failed.
Complete output:
  > suppressWarnings(RNGversion("3.5.2"))
  > 
  > library("partykit")
  Loading required package: grid
  Loading required package: libcoin
  Loading required package: mvtnorm
  > 
  > set.seed(29)
  > n <- 1000
  > x <- runif(n)
  > z <- runif(n)
  > y <- rnorm(n, mean = x * c(-1, 1)[(z > 0.7) + 1], sd = 3)
  > z_noise <- factor(sample(1:3, size = n, replace = TRUE))
  > d <- data.frame(y = y, x = x, z = z, z_noise = z_noise)
  > 
  > 
  > fmla <- as.formula("y ~ x | z + z_noise")
  > fmly <- gaussian()
  > fit <- partykit:::glmfit
  > 
  > # versions of the data
  > d1 <- d
  > d1$z <- signif(d1$z, digits = 1)
  > 
  > k <- 20
  > zs_noise <- matrix(rnorm(n*k), nrow = n)
  > colnames(zs_noise) <- paste0("z_noise_", 1:k)
  > d2 <- cbind(d, zs_noise)
  > fmla2 <- as.formula(paste("y ~ x | z + z_noise +",
  +                           paste0("z_noise_", 1:k, collapse = " + ")))
  > 
  > 
  > d3 <- d2
  > d3$z <- factor(sample(1:3, size = n, replace = TRUE, prob = c(0.1, 0.5, 0.4)))
  > d3$y <- rnorm(n, mean = x * c(-1, 1)[(d3$z == 2) + 1], sd = 3)
  > 
  > ## check weights
  > w <- rep(1, n)
  > w[1:10] <- 2
  > (mw1 <- glmtree(formula = fmla, data = d, weights = w))
  Generalized linear model tree (family: gaussian)
  
  Model formula:
  y ~ x | z + z_noise
  
  Fitted party:
  [1] root
  |   [2] z <= 0.70311: n = 706
  |       (Intercept)           x 
  |        -0.1447422  -0.8138701 
  |   [3] z > 0.70311: n = 304
  |       (Intercept)           x 
  |        0.07006626  0.73278593 
  
  Number of inner nodes:    1
  Number of terminal nodes: 2
  Number of parameters per node: 2
  Objective function (negative log-likelihood): 2551.48
  > (mw2 <- glmtree(formula = fmla, data = d, weights = w, caseweights = FALSE))
  Generalized linear model tree (family: gaussian)
  
  Model formula:
  y ~ x | z + z_noise
  
  Fitted party:
  [1] root
  |   [2] z <= 0.70311: n = 704
  |       (Intercept)           x 
  |        -0.1447422  -0.8138701 
  |   [3] z > 0.70311: n = 296
  |       (Intercept)           x 
  |        0.07006626  0.73278593 
  
  Number of inner nodes:    1
  Number of terminal nodes: 2
  Number of parameters per node: 2
  Objective function (negative log-likelihood): 2551.48
  > 
  > 
  > 
  > ## check dfsplit
  > (mmfluc2 <- mob(formula = fmla, data = d, fit = partykit:::glmfit))
  Model-based recursive partitioning (partykit:::glmfit)
  
  Model formula:
  y ~ x | z + z_noise
  
  Fitted party:
  [1] root
  |   [2] z <= 0.70311: n = 704
  |       (Intercept)           x 
  |        -0.1619978  -0.7896293 
  |   [3] z > 0.70311: n = 296
  |       (Intercept)           x 
  |        0.08683535  0.65598287 
  
  Number of inner nodes:    1
  Number of terminal nodes: 2
  Number of parameters per node: 2
  Objective function: 2551.673
  > (mmfluc3 <- glmtree(formula = fmla, data = d))
  Generalized linear model tree (family: gaussian)
  
  Model formula:
  y ~ x | z + z_noise
  
  Fitted party:
  [1] root
  |   [2] z <= 0.70311: n = 704
  |       (Intercept)           x 
  |        -0.1619978  -0.7896293 
  |   [3] z > 0.70311: n = 296
  |       (Intercept)           x 
  |        0.08683535  0.65598287 
  
  Number of inner nodes:    1
  Number of terminal nodes: 2
  Number of parameters per node: 2
  Objective function (negative log-likelihood): 2551.673
  > (mmfluc3_dfsplit <- glmtree(formula = fmla, data = d, dfsplit = 10))
  Generalized linear model tree (family: gaussian)
  
  Model formula:
  y ~ x | z + z_noise
  
  Fitted party:
  [1] root
  |   [2] z <= 0.70311: n = 704
  |       (Intercept)           x 
  |        -0.1619978  -0.7896293 
  |   [3] z > 0.70311: n = 296
  |       (Intercept)           x 
  |        0.08683535  0.65598287 
  
  Number of inner nodes:    1
  Number of terminal nodes: 2
  Number of parameters per node: 2
  Objective function (negative log-likelihood): 2551.673
  > 
  > 
  > ## check tests
  > if (require("strucchange"))
  +     print(sctest(mmfluc3, node = 1)) # does not yet work
  Loading required package: strucchange
  Warning message:
  In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE,  :
    there is no package called 'strucchange'
  > 
  > x <- mmfluc3
  > (tst3 <- nodeapply(x, ids = nodeids(x), function(n) n$info$criterion))
  $`1`
  NULL
  
  $`2`
  NULL
  
  $`3`
  NULL
  
  > 
  > 
  > 
  > 
  > ## check logLik and AIC
  > logLik(mmfluc2)
  'log Lik.' -2551.673 (df=7)
  > logLik(mmfluc3)
  'log Lik.' -2551.673 (df=7)
  > logLik(mmfluc3_dfsplit)
  'log Lik.' -2551.673 (df=16)
  > logLik(glm(y ~ x, data = d))
  'log Lik.' -2563.694 (df=3)
  > 
  > AIC(mmfluc3)
  [1] 5117.347
  > AIC(mmfluc3_dfsplit)
  [1] 5135.347
  > 
  > ## check pruning
  > pr2 <- prune.modelparty(mmfluc2)
  > AIC(mmfluc2)
  [1] 5117.347
  > AIC(pr2)
  [1] 5117.347
  > 
  > mmfluc_dfsplit3 <- glmtree(formula = fmla, data = d, alpha = 0.5, dfsplit = 3)
  > mmfluc_dfsplit4 <- glmtree(formula = fmla, data = d, alpha = 0.5, dfsplit = 4)
  > pr_dfsplit3 <- prune.modelparty(mmfluc_dfsplit3)
  > pr_dfsplit4 <- prune.modelparty(mmfluc_dfsplit4)
  > AIC(mmfluc_dfsplit3)
  [1] 5142.774
  > AIC(mmfluc_dfsplit4)
  [1] 5156.774
  > AIC(pr_dfsplit3)
  [1] 5142.774
  > AIC(pr_dfsplit4)
  [1] 5124.456
  > 
  > width(mmfluc_dfsplit3)
  [1] 8
  > width(mmfluc_dfsplit4)
  [1] 8
  > width(pr_dfsplit3)
  [1] 8
  > width(pr_dfsplit4)
  [1] 3
  > 
  > ## check inner and terminal
  > options <- list(NULL,
  +                 "object",
  +                 "estfun",
  +                 c("object", "estfun"))
  > 
  > arguments <- list("inner",
  +                   "terminal",
  +                   c("inner", "terminal"))
  > 
  > 
  > for (o in options) {
  +   print(o)
  +   x <- glmtree(formula = fmla, data = d, inner = o)
  +   str(nodeapply(x, ids = nodeids(x), function(n) n$info[c("object", "estfun")]), 2)
  + }
  NULL
  List of 3
   $ 1:List of 2
    ..$ NA: NULL
    ..$ NA: NULL
   $ 2:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ NA    : NULL
   $ 3:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ NA    : NULL
  [1] "object"
  List of 3
   $ 1:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ NA    : NULL
   $ 2:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ NA    : NULL
   $ 3:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ NA    : NULL
  [1] "estfun"
  List of 3
   $ 1:List of 2
    ..$ NA    : NULL
    ..$ estfun: num [1:1000, 1:2] -0.1375 -0.0583 -0.0553 0.1043 -0.0744 ...
    .. ..- attr(*, "dimnames")=List of 2
   $ 2:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ NA    : NULL
   $ 3:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ NA    : NULL
  [1] "object" "estfun"
  List of 3
   $ 1:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ estfun: num [1:1000, 1:2] -0.1375 -0.0583 -0.0553 0.1043 -0.0744 ...
    .. ..- attr(*, "dimnames")=List of 2
   $ 2:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ NA    : NULL
   $ 3:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ NA    : NULL
  > 
  > for (o in options) {
  +   print(o)
  +   x <- glmtree(formula = fmla, data = d, terminal = o)
  +   str(nodeapply(x, ids = nodeids(x), function(n) n$info[c("object", "estfun")]), 2)
  + }
  NULL
  List of 3
   $ 1:List of 2
    ..$ NA: NULL
    ..$ NA: NULL
   $ 2:List of 2
    ..$ NA: NULL
    ..$ NA: NULL
   $ 3:List of 2
    ..$ NA: NULL
    ..$ NA: NULL
  [1] "object"
  List of 3
   $ 1:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ NA    : NULL
   $ 2:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ NA    : NULL
   $ 3:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ NA    : NULL
  [1] "estfun"
  List of 3
   $ 1:List of 2
    ..$ NA    : NULL
    ..$ estfun: num [1:1000, 1:2] -0.1375 -0.0583 -0.0553 0.1043 -0.0744 ...
    .. ..- attr(*, "dimnames")=List of 2
   $ 2:List of 2
    ..$ NA    : NULL
    ..$ estfun: num [1:704, 1:2] -0.1291 0.5104 -0.0603 -0.1868 -0.0981 ...
    .. ..- attr(*, "dimnames")=List of 2
   $ 3:List of 2
    ..$ NA    : NULL
    ..$ estfun: num [1:296, 1:2] -0.1053 -0.0877 0.0544 -0.1581 0.43 ...
    .. ..- attr(*, "dimnames")=List of 2
  [1] "object" "estfun"
  List of 3
   $ 1:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ estfun: num [1:1000, 1:2] -0.1375 -0.0583 -0.0553 0.1043 -0.0744 ...
    .. ..- attr(*, "dimnames")=List of 2
   $ 2:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ estfun: num [1:704, 1:2] -0.1291 0.5104 -0.0603 -0.1868 -0.0981 ...
    .. ..- attr(*, "dimnames")=List of 2
   $ 3:List of 2
    ..$ object:List of 24
    .. ..- attr(*, "class")= chr [1:2] "glm" "lm"
    ..$ estfun: num [1:296, 1:2] -0.1053 -0.0877 0.0544 -0.1581 0.43 ...
    .. ..- attr(*, "dimnames")=List of 2
  > 
  > 
  > ## check model
  > m_mt <- glmtree(formula = fmla, data = d, model = TRUE)
  > m_mf <- glmtree(formula = fmla, data = d, model = FALSE)
  > 
  > dim(m_mt$data)
  [1] 1000    4
  > dim(m_mf$data)
  [1] 0 4
  > 
  > 
  > ## check multiway
  > (m_mult <- glmtree(formula = fmla2, data = d3, catsplit = "multiway", minsize = 80))
  Generalized linear model tree (family: gaussian)
  
  Model formula:
  y ~ x | z + z_noise + z_noise_1 + z_noise_2 + z_noise_3 + z_noise_4 + 
      z_noise_5 + z_noise_6 + z_noise_7 + z_noise_8 + z_noise_9 + 
      z_noise_10 + z_noise_11 + z_noise_12 + z_noise_13 + z_noise_14 + 
      z_noise_15 + z_noise_16 + z_noise_17 + z_noise_18 + z_noise_19 + 
      z_noise_20
  
  Fitted party:
  [1] root
  |   [2] z in 1: n = 76
  |       (Intercept)           x 
  |         0.9859847  -3.2600047 
  |   [3] z in 2: n = 537
  |       (Intercept)           x 
  |       -0.06970187  1.12305074 
  |   [4] z in 3: n = 387
  |       (Intercept)           x 
  |         0.3824392  -1.8337151 
  
  Number of inner nodes:    1
  Number of terminal nodes: 3
  Number of parameters per node: 2
  Objective function (negative log-likelihood): 2511.927
  > 
  > 
  > ## check parm
  > fmla_p <- as.formula("y ~ x + z_noise + z_noise_1 | z + z_noise_2")
  > (m_interc <- glmtree(formula = fmla_p, data = d2, parm = 1))
  Generalized linear model tree (family: gaussian)
  
  Model formula:
  y ~ x + z_noise + z_noise_1 | z + z_noise_2
  
  Fitted party:
  [1] root
  |   [2] z <= 0.65035: n = 644
  |       (Intercept)           x    z_noise2    z_noise3   z_noise_1 
  |       -0.05585503 -1.01257554  0.34044520 -0.16384987  0.24197601 
  |   [3] z > 0.65035: n = 356
  |       (Intercept)           x    z_noise2    z_noise3   z_noise_1 
  |        0.06411865  0.78733976 -0.67811149 -0.14240432 -0.01239154 
  
  Number of inner nodes:    1
  Number of terminal nodes: 2
  Number of parameters per node: 5
  Objective function (negative log-likelihood): 2548.32
  > 
  > (m_p3 <- glmtree(formula = fmla_p, data = d2, parm = 3))
  Generalized linear model tree (family: gaussian)
  
  Model formula:
  y ~ x + z_noise + z_noise_1 | z + z_noise_2
  
  Fitted party:
  [1] root: n = 1000
       (Intercept)            x     z_noise2     z_noise3    z_noise_1 
      -0.058855295 -0.340314311 -0.008404682 -0.109839080  0.154798281  
  
  Number of inner nodes:    0
  Number of terminal nodes: 1
  Number of parameters per node: 5
  Objective function (negative log-likelihood): 2562.32
  > 
  > 
  > ## check trim
  > (m_tt <- glmtree(formula = fmla, data = d, trim = 0.2))
  Generalized linear model tree (family: gaussian)
  
  Model formula:
  y ~ x | z + z_noise
  
  Fitted party:
  [1] root
  |   [2] z <= 0.70311: n = 704
  |       (Intercept)           x 
  |        -0.1619978  -0.7896293 
  |   [3] z > 0.70311: n = 296
  |       (Intercept)           x 
  |        0.08683535  0.65598287 
  
  Number of inner nodes:    1
  Number of terminal nodes: 2
  Number of parameters per node: 2
  Objective function (negative log-likelihood): 2551.673
  > 
  > (m_tf <- glmtree(formula = fmla, data = d, trim = 300, minsize = 300))
  Generalized linear model tree (family: gaussian)
  
  Model formula:
  y ~ x | z + z_noise
  
  Fitted party:
  [1] root
  |   [2] z <= 0.6892: n = 691
  |       (Intercept)           x 
  |        -0.1778199  -0.7692901 
  |   [3] z > 0.6892: n = 309
  |       (Intercept)           x 
  |         0.1065746   0.5562243 
  
  Number of inner nodes:    1
  Number of terminal nodes: 2
  Number of parameters per node: 2
  Objective function (negative log-likelihood): 2552.12
  > 
  > 
  > 
  > ## check breakties
  > m_bt <- glmtree(formula = fmla, data = d1, breakties = TRUE)
  > m_df <- glmtree(formula = fmla, data = d1, breakties = FALSE)
  > 
  > all.equal(m_bt, m_df, check.environment = FALSE)
  [1] "Component \"node\": Component \"kids\": Component 1: Component 5: Component 6: Mean relative difference: 0.1237503"
  [2] "Component \"node\": Component \"kids\": Component 2: Component 5: Component 5: Mean relative difference: 0.1746109"
  [3] "Component \"node\": Component \"kids\": Component 2: Component 5: Component 6: Mean relative difference: 0.0443985"
  [4] "Component \"node\": Component \"info\": Component \"p.value\": Mean relative difference: 1.100407"                 
  [5] "Component \"node\": Component \"info\": Component \"test\": Mean relative difference: 0.07721086"                  
  [6] "Component \"info\": Component \"call\": target, current do not match when deparsed"                                
  [7] "Component \"info\": Component \"control\": Component \"breakties\": 1 element mismatch"                            
  > 
  > unclass(m_bt)$node$info$criterion
  NULL
  > unclass(m_df)$node$info$criterion
  NULL
  > 
  > 
  > ### example from mob vignette
  > data("PimaIndiansDiabetes", package = "mlbench")
  Error in find.package(package, lib.loc, verbose = verbose) : 
    there is no package called 'mlbench'
  Calls: data -> find.package
  Execution halted
Running the tests in ‘tests/regtest-party.R’ failed.
Complete output:
  > suppressWarnings(RNGversion("3.5.2"))
  > 
  > ## load package and fix seed
  > library("partykit")
  Loading required package: grid
  Loading required package: libcoin
  Loading required package: mvtnorm
  > set.seed(1)
  > 
  > ## rpart: kyphosis data
  > library("rpart")
  > data("kyphosis", package = "rpart")
  > fit <- rpart(Kyphosis ~ Age + Number + Start, data = kyphosis)
  > pfit <- as.party(fit)
  > all(predict(pfit, newdata = kyphosis, type = "node") == fit$where)
  [1] TRUE
  > 
  > ## J48: iris data
  > if (require("RWeka")) {
  +     data("iris", package = "datasets")
  +     itree <- J48(Species ~ ., data = iris)
  +     pitree <- as.party(itree)
  +     stopifnot(all(predict(pitree) == predict(pitree, newdata = iris[, 3:4])))
  + 
  +     print(all.equal(predict(itree, type = "prob", newdata = iris),
  +               predict(pitree, type = "prob", newdata = iris)))
  +     print(all.equal(predict(itree,  newdata = iris),
  +               predict(pitree, newdata = iris)))
  + 
  +     ## rpart/J48: GlaucomaM data
  +     data("GlaucomaM", package = "TH.data")
  +     w <- runif(nrow(GlaucomaM))
  +     fit <- rpart(Class ~ ., data = GlaucomaM, weights = w)
  +     pfit <- as.party(fit)
  +     print(all(predict(pfit, type = "node") == fit$where))
  +     tmp <- GlaucomaM[sample(1:nrow(GlaucomaM), 100),]
  +     print(all.equal(predict(fit, type = "prob", newdata = tmp), predict(pfit, type = "prob", newdata = tmp)))
  +     print(all.equal(predict(fit, type = "class", newdata = tmp), predict(pfit, newdata = tmp)))
  +     itree <- J48(Class ~ ., data = GlaucomaM)
  +     pitree <- as.party(itree)
  +     print(all.equal(predict(itree, newdata = tmp, type = "prob"), predict(pitree, newdata = tmp, type = "prob")))
  + }
  Loading required package: RWeka
  Warning message:
  In library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE,  :
    there is no package called 'RWeka'
  > 
  > ## rpart: airquality data
  > data("airquality")
  > aq <- subset(airquality, !is.na(Ozone))
  > w <- runif(nrow(aq), max = 3)
  > aqr <- rpart(Ozone ~ ., data = aq, weights = w)
  > aqp <- as.party(aqr)
  > tmp <- subset(airquality, is.na(Ozone))
  > all.equal(predict(aqr, newdata = tmp), predict(aqp, newdata = tmp))
  [1] TRUE
  > 
  > ## rpart: GBSG2 data
  > data("GBSG2", package = "TH.data")
  Error in find.package(package, lib.loc, verbose = verbose) : 
    there is no package called 'TH.data'
  Calls: data -> find.package
  Execution halted
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes ... OK
* checking running R code from vignettes ...
  ‘ctree.Rnw’ using ‘UTF-8’... failed
  ‘partykit.Rnw’... [4s/20s] OK
 [9s/40s] WARNING
Errors in running code in vignettes:
when running code in ‘ctree.Rnw’

> suppressWarnings(RNGversion("3.5.2"))

> options(width = 70, SweaveHooks = list(leftpar = function() par(mai = par("mai") * 
+     c(1, 1.1, 1, 1))))

> require("partykit")
Loading required package: partykit
Loading required package: grid
Loading required package: libcoin
Loading required package: mvtnorm

> require("coin")
Loading required package: coin
Warning in library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE,  :
  there is no package called ‘coin’

> require("strucchange")
Loading required package: strucchange
Warning in library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE,  :
  there is no package called ‘strucchange’

> require("coin")
Loading required package: coin
Warning in library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE,  :
  there is no package called ‘coin’

> require("Formula")
Loading required package: Formula

> require("survival")
Loading required package: survival

> require("sandwich")
Loading required package: sandwich
Warning in library(package, lib.loc = lib.loc, character.only = TRUE, logical.return = TRUE,  :
  there is no package called ‘sandwich’

> set.seed(290875)

> ctree_control(teststat = "max")
$criterion
[1] "p.value"

$logmincriterion
[1] -0.05129329

$minsplit
[1] 20

$minbucket
[1] 7

$minprob
[1] 0.01

$maxvar
[1] Inf

$stump
[1] FALSE

$nmax
 yx   z 
Inf Inf 

$lookahead
[1] FALSE

$mtry
[1] Inf

$maxdepth
[1] Inf

$multiway
[1] FALSE

$splittry
[1] 2

$maxsurrogate
[1] 0

$numsurrogate
[1] FALSE

$majority
[1] FALSE

$caseweights
[1] TRUE

$applyfun
function (X, FUN, ...) 
{
    FUN <- match.fun(FUN)
    if (!is.vector(X) || is.object(X)) 
        X <- as.list(X)
    .Internal(lapply(X, FUN))
}
<bytecode: 0x2c035d80>
<environment: namespace:base>

$saveinfo
[1] TRUE

$bonferroni
[1] TRUE

$update
NULL

$selectfun
function (model, trafo, data, subset, weights, whichvar, ctrl) 
{
    args <- list(...)
    ctrl[names(args)] <- args
    .select(model, trafo, data, subset, weights, whichvar, ctrl, 
        FUN = .ctree_test)
}
<bytecode: 0x34139348>
<environment: 0x3413eae8>

$splitfun
function (model, trafo, data, subset, weights, whichvar, ctrl) 
{
    args <- list(...)
    ctrl[names(args)] <- args
    .split(model, trafo, data, subset, weights, whichvar, ctrl, 
        FUN = .ctree_test)
}
<bytecode: 0x3413dab8>
<environment: 0x3413cee8>

$svselectfun
function (model, trafo, data, subset, weights, whichvar, ctrl) 
{
    args <- list(...)
    ctrl[names(args)] <- args
    .select(model, trafo, data, subset, weights, whichvar, ctrl, 
        FUN = .ctree_test)
}
<bytecode: 0x34139348>
<environment: 0x3413ce08>

$svsplitfun
function (model, trafo, data, subset, weights, whichvar, ctrl) 
{
    args <- list(...)
    ctrl[names(args)] <- args
    .split(model, trafo, data, subset, weights, whichvar, ctrl, 
        FUN = .ctree_test)
}
<bytecode: 0x3413dab8>
<environment: 0x3413ccb8>

$teststat
[1] "maximum"

$splitstat
[1] "quadratic"

$splittest
[1] FALSE

$pargs
$maxpts
[1] 25000

$abseps
[1] 0.001

$releps
[1] 0

attr(,"class")
[1] "GenzBretz"

$testtype
[1] "Bonferroni"

$nresample
[1] 9999

$tol
[1] 1.490116e-08

$intersplit
[1] FALSE

$MIA
[1] FALSE


> ctree_control(teststat = "quad")
$criterion
[1] "p.value"

$logmincriterion
[1] -0.05129329

$minsplit
[1] 20

$minbucket
[1] 7

$minprob
[1] 0.01

$maxvar
[1] Inf

$stump
[1] FALSE

$nmax
 yx   z 
Inf Inf 

$lookahead
[1] FALSE

$mtry
[1] Inf

$maxdepth
[1] Inf

$multiway
[1] FALSE

$splittry
[1] 2

$maxsurrogate
[1] 0

$numsurrogate
[1] FALSE

$majority
[1] FALSE

$caseweights
[1] TRUE

$applyfun
function (X, FUN, ...) 
{
    FUN <- match.fun(FUN)
    if (!is.vector(X) || is.object(X)) 
        X <- as.list(X)
    .Internal(lapply(X, FUN))
}
<bytecode: 0x2c035d80>
<environment: namespace:base>

$saveinfo
[1] TRUE

$bonferroni
[1] TRUE

$update
NULL

$selectfun
function (model, trafo, data, subset, weights, whichvar, ctrl) 
{
    args <- list(...)
    ctrl[names(args)] <- args
    .select(model, trafo, data, subset, weights, whichvar, ctrl, 
        FUN = .ctree_test)
}
<bytecode: 0x34139348>
<environment: 0x34162828>

$splitfun
function (model, trafo, data, subset, weights, whichvar, ctrl) 
{
    args <- list(...)
    ctrl[names(args)] <- args
    .split(model, trafo, data, subset, weights, whichvar, ctrl, 
        FUN = .ctree_test)
}
<bytecode: 0x3413dab8>
<environment: 0x34162748>

$svselectfun
function (model, trafo, data, subset, weights, whichvar, ctrl) 
{
    args <- list(...)
    ctrl[names(args)] <- args
    .select(model, trafo, data, subset, weights, whichvar, ctrl, 
        FUN = .ctree_test)
}
<bytecode: 0x34139348>
<environment: 0x34162668>

$svsplitfun
function (model, trafo, data, subset, weights, whichvar, ctrl) 
{
    args <- list(...)
    ctrl[names(args)] <- args
    .split(model, trafo, data, subset, weights, whichvar, ctrl, 
        FUN = .ctree_test)
}
<bytecode: 0x3413dab8>
<environment: 0x34162518>

$teststat
[1] "quadratic"

$splitstat
[1] "quadratic"

$splittest
[1] FALSE

$pargs
$maxpts
[1] 25000

$abseps
[1] 0.001

$releps
[1] 0

attr(,"class")
[1] "GenzBretz"

$testtype
[1] "Bonferroni"

$nresample
[1] 9999

$tol
[1] 1.490116e-08

$intersplit
[1] FALSE

$MIA
[1] FALSE


> ctree_control(testtype = "Bonferroni")
$criterion
[1] "p.value"

$logmincriterion
[1] -0.05129329

$minsplit
[1] 20

$minbucket
[1] 7

$minprob
[1] 0.01

$maxvar
[1] Inf

$stump
[1] FALSE

$nmax
 yx   z 
Inf Inf 

$lookahead
[1] FALSE

$mtry
[1] Inf

$maxdepth
[1] Inf

$multiway
[1] FALSE

$splittry
[1] 2

$maxsurrogate
[1] 0

$numsurrogate
[1] FALSE

$majority
[1] FALSE

$caseweights
[1] TRUE

$applyfun
function (X, FUN, ...) 
{
    FUN <- match.fun(FUN)
    if (!is.vector(X) || is.object(X)) 
        X <- as.list(X)
    .Internal(lapply(X, FUN))
}
<bytecode: 0x2c035d80>
<environment: namespace:base>

$saveinfo
[1] TRUE

$bonferroni
[1] TRUE

$update
NULL

$selectfun
function (model, trafo, data, subset, weights, whichvar, ctrl) 
{
    args <- list(...)
    ctrl[names(args)] <- args
    .select(model, trafo, data, subset, weights, whichvar, ctrl, 
        FUN = .ctree_test)
}
<bytecode: 0x34139348>
<environment: 0x34177bd8>

$splitfun
function (model, trafo, data, subset, weights, whichvar, ctrl) 
{
    args <- list(...)
    ctrl[names(args)] <- args
    .split(model, trafo, data, subset, weights, whichvar, ctrl, 
        FUN = .ctree_test)
}
<bytecode: 0x3413dab8>
<environment: 0x34177af8>

$svselectfun
function (model, trafo, data, subset, weights, whichvar, ctrl) 
{
    args <- list(...)
    ctrl[names(args)] <- args
    .select(model, trafo, data, subset, weights, whichvar, ctrl, 
        FUN = .ctree_test)
}
<bytecode: 0x34139348>
<environment: 0x34177a18>

$svsplitfun
function (model, trafo, data, subset, weights, whichvar, ctrl) 
{
    args <- list(...)
    ctrl[names(args)] <- args
    .split(model, trafo, data, subset, weights, whichvar, ctrl, 
        FUN = .ctree_test)
}
<bytecode: 0x3413dab8>
<environment: 0x341778c8>

$teststat
[1] "quadratic"

$splitstat
[1] "quadratic"

$splittest
[1] FALSE

$pargs
$maxpts
[1] 25000

$abseps
[1] 0.001

$releps
[1] 0

attr(,"class")
[1] "GenzBretz"

$testtype
[1] "Bonferroni"

$nresample
[1] 9999

$tol
[1] 1.490116e-08

$intersplit
[1] FALSE

$MIA
[1] FALSE


> ctree_control(minsplit = 20)
$criterion
[1] "p.value"

$logmincriterion
[1] -0.05129329

$minsplit
[1] 20

$minbucket
[1] 7

$minprob
[1] 0.01

$maxvar
[1] Inf

$stump
[1] FALSE

$nmax
 yx   z 
Inf Inf 

$lookahead
[1] FALSE

$mtry
[1] Inf

$maxdepth
[1] Inf

$multiway
[1] FALSE

$splittry
[1] 2

$maxsurrogate
[1] 0

$numsurrogate
[1] FALSE

$majority
[1] FALSE

$caseweights
[1] TRUE

$applyfun
function (X, FUN, ...) 
{
    FUN <- match.fun(FUN)
    if (!is.vector(X) || is.object(X)) 
        X <- as.list(X)
    .Internal(lapply(X, FUN))
}
<bytecode: 0x2c035d80>
<environment: namespace:base>

$saveinfo
[1] TRUE

$bonferroni
[1] TRUE

$update
NULL

$selectfun
function (model, trafo, data, subset, weights, whichvar, ctrl) 
{
    args <- list(...)
    ctrl[names(args)] <- args
    .select(model, trafo, data, subset, weights, whichvar, ctrl, 
        FUN = .ctree_test)
}
<bytecode: 0x34139348>
<environment: 0x34189008>

$splitfun
function (model, trafo, data, subset, weights, whichvar, ctrl) 
{
    args <- list(...)
    ctrl[names(args)] <- args
    .split(model, trafo, data, subset, weights, whichvar, ctrl, 
        FUN = .ctree_test)
}
<bytecode: 0x3413dab8>
<environment: 0x34188f28>

$svselectfun
function (model, trafo, data, subset, weights, whichvar, ctrl) 
{
    args <- list(...)
    ctrl[names(args)] <- args
    .select(model, trafo, data, subset, weights, whichvar, ctrl, 
        FUN = .ctree_test)
}
<bytecode: 0x34139348>
<environment: 0x34188e48>

$svsplitfun
function (model, trafo, data, subset, weights, whichvar, ctrl) 
{
    args <- list(...)
    ctrl[names(args)] <- args
    .split(model, trafo, data, subset, weights, whichvar, ctrl, 
        FUN = .ctree_test)
}
<bytecode: 0x3413dab8>
<environment: 0x34188cf8>

$teststat
[1] "quadratic"

$splitstat
[1] "quadratic"

$splittest
[1] FALSE

$pargs
$maxpts
[1] 25000

$abseps
[1] 0.001

$releps
[1] 0

attr(,"class")
[1] "GenzBretz"

$testtype
[1] "Bonferroni"

$nresample
[1] 9999

$tol
[1] 1.490116e-08

$intersplit
[1] FALSE

$MIA
[1] FALSE


> ctree_control(maxsurrogate = 3)
$criterion
[1] "p.value"

$logmincriterion
[1] -0.05129329

$minsplit
[1] 20

$minbucket
[1] 7

$minprob
[1] 0.01

$maxvar
[1] Inf

$stump
[1] FALSE

$nmax
 yx   z 
Inf Inf 

$lookahead
[1] FALSE

$mtry
[1] Inf

$maxdepth
[1] Inf

$multiway
[1] FALSE

$splittry
[1] 2

$maxsurrogate
[1] 3

$numsurrogate
[1] FALSE

$majority
[1] FALSE

$caseweights
[1] TRUE

$applyfun
function (X, FUN, ...) 
{
    FUN <- match.fun(FUN)
    if (!is.vector(X) || is.object(X)) 
        X <- as.list(X)
    .Internal(lapply(X, FUN))
}
<bytecode: 0x2c035d80>
<environment: namespace:base>

$saveinfo
[1] TRUE

$bonferroni
[1] TRUE

$update
NULL

$selectfun
function (model, trafo, data, subset, weights, whichvar, ctrl) 
{
    args <- list(...)
    ctrl[names(args)] <- args
    .select(model, trafo, data, subset, weights, whichvar, ctrl, 
        FUN = .ctree_test)
}
<bytecode: 0x34139348>
<environment: 0x341a3fc8>

$splitfun
function (model, trafo, data, subset, weights, whichvar, ctrl) 
{
    args <- list(...)
    ctrl[names(args)] <- args
    .split(model, trafo, data, subset, weights, whichvar, ctrl, 
        FUN = .ctree_test)
}
<bytecode: 0x3413dab8>
<environment: 0x341a3ee8>

$svselectfun
function (model, trafo, data, subset, weights, whichvar, ctrl) 
{
    args <- list(...)
    ctrl[names(args)] <- args
    .select(model, trafo, data, subset, weights, whichvar, ctrl, 
        FUN = .ctree_test)
}
<bytecode: 0x34139348>
<environment: 0x341a3e08>

$svsplitfun
function (model, trafo, data, subset, weights, whichvar, ctrl) 
{
    args <- list(...)
    ctrl[names(args)] <- args
    .split(model, trafo, data, subset, weights, whichvar, ctrl, 
        FUN = .ctree_test)
}
<bytecode: 0x3413dab8>
<environment: 0x341a3cb8>

$teststat
[1] "quadratic"

$splitstat
[1] "quadratic"

$splittest
[1] FALSE

$pargs
$maxpts
[1] 25000

$abseps
[1] 0.001

$releps
[1] 0

attr(,"class")
[1] "GenzBretz"

$testtype
[1] "Bonferroni"

$nresample
[1] 9999

$tol
[1] 1.490116e-08

$intersplit
[1] FALSE

$MIA
[1] FALSE


> ls <- data.frame(y = gl(3, 50, labels = c("A", "B", 
+     "C")), x1 = rnorm(150) + rep(c(1, 0, 0), c(50, 50, 50)), 
+     x2 = runif(150))

> library("partykit")

> ctree(y ~ x1 + x2, data = ls)

Model formula:
y ~ x1 + x2

Fitted party:
[1] root
|   [2] x1 <= 0.82552: C (n = 96, err = 57.3%)
|   [3] x1 > 0.82552: A (n = 54, err = 42.6%)

Number of inner nodes:    1
Number of terminal nodes: 2

> ct <- ctree(y ~ x1 + x2, data = ls)

> ct

Model formula:
y ~ x1 + x2

Fitted party:
[1] root
|   [2] x1 <= 0.82552: C (n = 96, err = 57.3%)
|   [3] x1 > 0.82552: A (n = 54, err = 42.6%)

Number of inner nodes:    1
Number of terminal nodes: 2

> plot(ct)

> ct[1]

Model formula:
y ~ x1 + x2

Fitted party:
[1] root
|   [2] x1 <= 0.82552: C (n = 96, err = 57.3%)
|   [3] x1 > 0.82552: A (n = 54, err = 42.6%)

Number of inner nodes:    1
Number of terminal nodes: 2

> class(ct[1])
[1] "constparty" "party"     

> predict(ct, newdata = ls)
  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17 
  A   A   A   A   C   A   C   A   C   C   A   A   C   A   A   A   A 
 18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34 
  C   A   C   A   A   A   C   A   A   A   C   C   A   A   C   A   A 
 35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51 
  C   A   A   C   C   C   A   A   C   C   C   C   A   A   A   A   A 
 52  53  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68 
  A   C   C   C   C   A   C   C   A   C   C   C   C   C   C   A   A 
 69  70  71  72  73  74  75  76  77  78  79  80  81  82  83  84  85 
  A   A   A   C   C   A   C   A   C   C   C   C   C   C   C   C   C 
 86  87  88  89  90  91  92  93  94  95  96  97  98  99 100 101 102 
  C   C   C   A   C   A   C   A   C   C   C   C   C   C   C   C   A 
103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 
  C   C   C   A   C   C   A   C   C   C   C   C   C   C   A   C   C 
120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 
  C   C   C   C   C   C   C   C   C   C   C   C   C   C   C   C   C 
137 138 139 140 141 142 143 144 145 146 147 148 149 150 
  C   A   C   C   C   C   A   C   C   A   C   A   C   A 
Levels: A B C

> predict(ct, newdata = ls[c(1, 51, 101), ], type = "prob")
            A         B         C
1   0.5740741 0.2592593 0.1666667
51  0.5740741 0.2592593 0.1666667
101 0.1979167 0.3750000 0.4270833

> predict(ct, newdata = ls[c(1, 51, 101), ], type = "node")
  1  51 101 
  3   3   2 

> library("strucchange")

  When sourcing ‘ctree.R’:
Error: there is no package called ‘strucchange’
Execution halted

* checking re-building of vignette outputs ... [19s/257s] NOTE
Note: skipping ‘constparty.Rnw’ due to unavailable dependencies:
  'RWeka', 'pmml'
Note: skipping ‘ctree.Rnw’ due to unavailable dependencies:
  'coin', 'TH.data', 'strucchange', 'sandwich'
Note: skipping ‘mob.Rnw’ due to unavailable dependencies:
  'AER', 'mlbench', 'sandwich', 'strucchange', 'TH.data', 'vcd',
  'psychotools', 'psychotree'
* checking PDF version of manual ... [13s/45s] OK
* checking HTML version of manual ... [9s/123s] OK
* checking for non-standard things in the check directory ... OK
* checking for detritus in the temp directory ... OK
* DONE

Status: 2 ERRORs, 1 WARNING, 1 NOTE
See
  ‘/data/gannet/ripley/R/packages/tests-Suggests/partykit.Rcheck/00check.log’
for details.

Command exited with non-zero status 1
Time 31:40.16, 320.75 + 25.11