Check results for 'partykit'

using R Under development (unstable) (2018-04-18 r74618)
using platform: x86_64-pc-linux-gnu (64-bit)
using session charset: UTF-8
checking for file ‘partykit/DESCRIPTION’ ... OK
this is package ‘partykit’ version ‘1.2-0’
checking package namespace information ... OK
checking package dependencies ... OK
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 serialization versions ... OK
checking whether package ‘partykit’ can be installed ... OK
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 R files for non-ASCII characters ... OK
checking R files for syntax errors ... OK
checking whether the package can be loaded ... OK
checking whether the package can be loaded with stated dependencies ... OK
checking whether the package can be unloaded cleanly ... OK
checking whether the namespace can be loaded with stated dependencies ... OK
checking whether the namespace can be unloaded cleanly ... OK
checking loading without being on the library search path ... 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 ... [30s/32s] OK
checking Rd files ... 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 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 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 ... [23s/26s] OK
checking differences from ‘partykit-Ex.Rout’ to ‘partykit-Ex.Rout.save’ ... OK

304,310d303
< Attaching package: 'survival'
<
< The following object is masked from 'package:rpart':
<
< solder
<
<
935,941d927
< Attaching package: 'survival'
<
< The following object is masked from 'package:rpart':
<
< solder
<
<
1077,1078c1063
< 'package:lmtest', 'package:zoo', 'package:car', 'package:carData',
< 'package:mlbench'
---
> 'package:lmtest', 'package:zoo', 'package:car', 'package:mlbench'
checking for unstated dependencies in ‘tests’ ... OK
checking tests ... [34s/41s] ERROR
  Running ‘bugfixes.R’ [7s/8s]
  Comparing ‘bugfixes.Rout’ to ‘bugfixes.Rout.save’ ...724,730d723
<
< Attaching package: 'survival'
<
< The following object is masked from 'package:rpart':
<
< solder
<
  Running ‘regtest-MIA.R’ [2s/3s]
  Comparing ‘regtest-MIA.Rout’ to ‘regtest-MIA.Rout.save’ ... OK
  Running ‘regtest-cforest.R’ [11s/13s]
  Comparing ‘regtest-cforest.Rout’ to ‘regtest-cforest.Rout.save’ ... OK
  Running ‘regtest-ctree.R’ [2s/3s]
  Comparing ‘regtest-ctree.Rout’ to ‘regtest-ctree.Rout.save’ ... OK
  Running ‘regtest-glmtree.R’ [12s/13s]
Running the tests in ‘tests/regtest-glmtree.R’ failed.
Complete output:
  >
  > library("partykit")
  Loading required package: grid
  Loading required package: libcoin
  Loading required package: mvtnorm
  Loading required package: rpart
  > library("sandwich")
  >
  > 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
  > library("strucchange")
  Loading required package: zoo

  Attaching package: 'zoo'

  The following objects are masked from 'package:base':

      as.Date, as.Date.numeric

  > sctest(mmfluc3, node = 1) # does not yet work
  Error in UseMethod("estfun") :
    no applicable method for 'estfun' applied to an object of class "c('glmtree', 'modelparty', 'party')"
  Calls: sctest -> sctest.default -> gefp -> scores
  Execution halted
checking for unstated dependencies in vignettes ... OK
checking package vignettes in ‘inst/doc’ ... OK
checking running R code from vignettes ... [24s/30s] ERROR
Errors in running code in vignettes:
when running code in ‘ctree.Rnw’
  ...

> 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
Loading required package: rpart

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

Attaching package: ‘survival’

The following object is masked from ‘package:rpart’:

    solder

> require("strucchange")
Loading required package: strucchange
Loading required package: zoo

Attaching package: ‘zoo’

The following objects are masked from ‘package:base’:

    as.Date, as.Date.numeric

Loading required package: sandwich

> require("coin")

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

> require("survival")

> require("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

$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: 0x246ab10>
<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: 0xe097e00>
<environment: 0xe097230>

$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: 0xe096318>
<environment: 0xe09d8b8>

$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: 0xe097e00>
<environment: 0xe09d7d8>

$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: 0xe096318>
<environment: 0xe09d688>

$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

$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: 0x246ab10>
<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: 0xe097e00>
<environment: 0xe0c1ed0>

$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: 0xe096318>
<environment: 0xe0c1df0>

$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: 0xe097e00>
<environment: 0xe0c1d10>

$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: 0xe096318>
<environment: 0xe0c1bc0>

$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

$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: 0x246ab10>
<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: 0xe097e00>
<environment: 0xe0d6988>

$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: 0xe096318>
<environment: 0xe0d68a8>

$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: 0xe097e00>
<environment: 0xe0d67c8>

$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: 0xe096318>
<environment: 0xe0d6678>

$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

$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: 0x246ab10>
<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: 0xe097e00>
<environment: 0xe0e6a40>

$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: 0xe096318>
<environment: 0xe0e6960>

$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: 0xe097e00>
<environment: 0xe0e6880>

$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: 0xe096318>
<environment: 0xe0e6730>

$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

$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: 0x246ab10>
<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: 0xe097e00>
<environment: 0xe0f7578>

$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: 0xe096318>
<environment: 0xe0fb2c8>

$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: 0xe097e00>
<environment: 0xe0fb1e8>

$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: 0xe096318>
<environment: 0xe0fb098>

$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")

> sctest(ct)

  When sourcing ‘ctree.R’:
Error: no applicable method for ‘estfun’ applied to an object of class "c('constparty', 'party')"
Execution halted
when running code in ‘mob.Rnw’
  ...

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

> options(prompt = "R> ", continue = "+ ", digits = 4,
+ useFancyQuotes = FALSE)

> data("PimaIndiansDiabetes", package = "mlbench")

> pid_formula <- diabetes ~ glucose | pregnant + pressure +
+ triceps + insulin + mass + pedigree + age

> logit <- function(y, x, start = NULL, weights = NULL,
+ offset = NULL, ...) {
+ glm(y ~ 0 + x, family = binomial, start = start, ...)
+ }

> pid_tree <- mob(pid_formula, data = PimaIndiansDiabetes,
+ fit = logit)

> pid_tree
Model-based recursive partitioning (logit)

Model formula:
diabetes ~ glucose | pregnant + pressure + triceps + insulin +
    mass + pedigree + age

Fitted party:
[1] root
| [2] mass <= 26.3: n = 167
| x(Intercept) xglucose
| -9.95151 0.05871
| [3] mass > 26.3
| | [4] age <= 30: n = 304
| | x(Intercept) xglucose
| | -6.70559 0.04684
| | [5] age > 30: n = 297
| | x(Intercept) xglucose
| | -2.77095 0.02354

Number of inner nodes: 2
Number of terminal nodes: 3
Number of parameters per node: 2
Objective function: 355.5

> pid_tree2 <- glmtree(diabetes ~ glucose | pregnant +
+ pressure + triceps + insulin + mass + pedigree + age, data = PimaIndiansDiabetes,
+ .... [TRUNCATED]

> plot(pid_tree)

> plot(pid_tree2, tp_args = list(ylines = 1, margins = c(1.5,
+ 1.5, 1.5, 2.5)))
Loading required namespace: vcd

> library("strucchange")
Loading required package: zoo

Attaching package: 'zoo'

The following objects are masked from 'package:base':

    as.Date, as.Date.numeric

Loading required package: sandwich

> sctest(pid_tree, node = 1)

  When sourcing 'mob.R':
Error: no applicable method for ‘estfun’ applied to an object of class "c('modelparty', 'party')"
Execution halted
checking re-building of vignette outputs ... [18s/29s] WARNING
Error in re-building vignettes:
  ...
Loading required package: grid
Loading required package: libcoin
Loading required package: mvtnorm
Loading required package: rpart
Loading required package: RWeka
Loading required namespace: XML
Loading required package: XML

Attaching package: ‘XML’

The following object is masked from ‘package:tools’:

    toHTML

Loading required package: coin
Loading required package: survival

Attaching package: ‘survival’

The following object is masked from ‘package:rpart’:

    solder

Loading required package: strucchange
Loading required package: zoo

Attaching package: ‘zoo’

The following objects are masked from ‘package:base’:

    as.Date, as.Date.numeric

Loading required package: sandwich
Loading required package: Formula

Error: processing vignette ‘ctree.Rnw’ failed with diagnostics:
chunk 17 (label = party-sctest)
Error in UseMethod("estfun") :
  no applicable method for ‘estfun’ applied to an object of class "c('constparty', 'party')"
Execution halted
checking PDF version of manual ... OK
DONE

Status: 2 ERRORs, 1 WARNING