R : Copyright 2005, The R Foundation for Statistical Computing
Version 2.1.1  (2005-06-20), ISBN 3-900051-07-0

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> ### * <HEADER>
> ###
> attach(NULL, name = "CheckExEnv")
> assign(".CheckExEnv", as.environment(2), pos = length(search())) # base
> ## add some hooks to label plot pages for base and grid graphics
> setHook("plot.new", ".newplot.hook")
> setHook("persp", ".newplot.hook")
> setHook("grid.newpage", ".gridplot.hook")
> 
> assign("cleanEx",
+        function(env = .GlobalEnv) {
+ 	   rm(list = ls(envir = env, all.names = TRUE), envir = env)
+            RNGkind("default", "default")
+ 	   set.seed(1)
+    	   options(warn = 1)
+ 	   delayedAssign("T", stop("T used instead of TRUE"),
+ 		  assign.env = .CheckExEnv)
+ 	   delayedAssign("F", stop("F used instead of FALSE"),
+ 		  assign.env = .CheckExEnv)
+ 	   sch <- search()
+ 	   newitems <- sch[! sch %in% .oldSearch]
+ 	   for(item in rev(newitems))
+                eval(substitute(detach(item), list(item=item)))
+ 	   missitems <- .oldSearch[! .oldSearch %in% sch]
+ 	   if(length(missitems))
+ 	       warning("items ", paste(missitems, collapse=", "),
+ 		       " have been removed from the search path")
+        },
+        env = .CheckExEnv)
> assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now
> assign("ptime", proc.time(), env = .CheckExEnv)
> grDevices::postscript("coin-Examples.ps")
> assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv)
> options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly"))
> options(warn = 1)    
> library('coin')
Loading required package: survival
Loading required package: splines
Loading required package: mvtnorm
Loading required package: modeltools
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "ContingencyTests"
> 
> ### * ContingencyTests
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ContingencyTests
> ### Title: Independence in Three-Way Contingency Tables
> ### Aliases: chisq_test chisq_test.formula chisq_test.table
> ###   chisq_test.IndependenceProblem cmh_test.formula cmh_test.table
> ###   cmh_test.IndependenceProblem cmh_test lbl_test.formula lbl_test.table
> ###   lbl_test.IndependenceProblem lbl_test
> ### Keywords: htest
> 
> ### ** Examples
> 
> 
> ## Don't show: 
>     set.seed(290875)
> ## End Don't show
> 
> data(jobsatisfaction, package = "coin")
> 
> ### for females only
> chisq_test(as.table(jobsatisfaction[,,"Female"]), 
+     distribution = approximate(B = 9999))

	Approximative Pearson's Chi-Squared Test

data:  Job.Satisfaction by
	 groups <5000, 5000-15000, 15000-25000, >25000 
T = 6.8203, p-value = 0.6891

> 
> ### both Income and Job.Satisfaction unordered
> cmh_test(jobsatisfaction)

	Asymptotic Generalised Cochran-Mantel-Haenszel Test

data:  Job.Satisfaction by
	 groups <5000, 5000-15000, 15000-25000, >25000 
	 stratified by Gender 
T = 10.2001, df = 9, p-value = 0.3345

> 
> ### both Income and Job.Satisfaction ordered, default scores
> lbl_test(jobsatisfaction)

	Asymptotic Linear-by-Linear Association Test

data:  Job.Satisfaction (ordered) by
	 groups <5000 < 5000-15000 < 15000-25000 < >25000 
	 stratified by Gender 
T = 6.6235, df = 1, p-value = 0.01006

> 
> ### both Income and Job.Satisfaction ordered, alternative scores
> lbl_test(jobsatisfaction, scores = list(Job.Satisfaction = c(1, 3, 4, 5),
+                                         Income = c(3, 10, 20, 35)))

	Asymptotic Linear-by-Linear Association Test

data:  Job.Satisfaction (ordered) by
	 groups <5000 < 5000-15000 < 15000-25000 < >25000 
	 stratified by Gender 
T = 6.1563, df = 1, p-value = 0.01309

> 
> ### the same, null distribution approximated
> cmh_test(jobsatisfaction, scores = list(Job.Satisfaction = c(1, 3, 4, 5),
+                                         Income = c(3, 10, 20, 35)),
+          distribution = approximate(B = 10000))

	Approximative Linear-by-Linear Association Test

data:  Job.Satisfaction (ordered) by
	 groups <5000 < 5000-15000 < 15000-25000 < >25000 
	 stratified by Gender 
T = 6.1563, p-value = 0.0123

> 
> ### Smoking and HDL cholesterin status
> ### (from Jeong, Jhun and Kim, 2005, CSDA 48, 623-631, Table 2)
> smokingHDL <- as.table(
+     matrix(c(15,  8, 11,  5, 
+               3,  4,  6,  1, 
+               6,  7, 15, 11, 
+               1,  2,  3,  5), ncol = 4,
+            dimnames = list(smoking = c("none", "< 5", "< 10", ">=10"), 
+                            HDL = c("normal", "low", "borderline", "abnormal"))
+     ))
> ### use interval mid-points as scores for smoking
> lbl_test(smokingHDL, scores = list(smoking = c(0, 2.5, 7.5, 15)))

	Asymptotic Linear-by-Linear Association Test

data:  HDL (ordered) by groups none < < 5 < < 10 < >=10 
T = 9.8557, df = 1, p-value = 0.001693

> 
> 
> 
> 
> cleanEx(); ..nameEx <- "IndependenceTest"
> 
> ### * IndependenceTest
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: IndependenceTest
> ### Title: General Independence Tests
> ### Aliases: independence_test independence_test.formula
> ###   independence_test.IndependenceProblem independence_test.table
> ### Keywords: htest
> 
> ### ** Examples
> 
> 
> data(asat, package = "coin")
> 
> ### independence of asat and group via normal scores test
> independence_test(asat ~ group, data = asat,
+ 
+     ### exact null distribution
+     distribution = "exact", 
+ 
+     ### one-sided test
+     alternative = "greater",
+ 
+     ### apply normal scores to asat$asat
+     ytrafo = function(data) trafo(data, numeric_trafo = normal_trafo),
+ 
+     ### indicator matrix of 1st level of group
+     xtrafo = function(data) trafo(data, factor_trafo = function(x)
+         matrix(x == levels(x)[1], ncol = 1))
+ )

	Exact General Independence Test

data:  asat by groups Compound, Control 
T = 1.4269, p-value = 0.07809
alternative hypothesis: greater 

> 
> ### same as
> normal_test(asat ~ group, data = asat, distribution = "exact", 
+             alternative = "greater")

	Exact Normal Quantile (van der Waerden) Test

data:  asat by groups Compound, Control 
T = 1.4269, p-value = 0.07809
alternative hypothesis: true mu is greater than 0 

> 
> 
> 
> 
> cleanEx(); ..nameEx <- "LocationTests"
> 
> ### * LocationTests
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: LocationTests
> ### Title: Independent Two- and K-Sample Location Tests
> ### Aliases: oneway_test oneway_test.formula
> ###   oneway_test.IndependenceProblem wilcox_test.formula
> ###   wilcox_test.IndependenceProblem wilcox_test normal_test.formula
> ###   normal_test.IndependenceProblem normal_test median_test.formula
> ###   median_test.IndependenceProblem median_test kruskal_test.formula
> ###   kruskal_test.IndependenceProblem kruskal_test
> ### Keywords: htest
> 
> ### ** Examples
> 
> 
> ### Tritiated Water Diffusion Across Human Chorioamnion
> ### Hollander & Wolfe (1999), Table 4.1, page 110
> water_transfer <- data.frame(
+     pd = c(0.80, 0.83, 1.89, 1.04, 1.45, 1.38, 1.91, 1.64, 0.73, 1.46,
+            1.15, 0.88, 0.90, 0.74, 1.21),
+     age = factor(c(rep("At term", 10), rep("12-26 Weeks", 5))))
> 
> ### Wilcoxon-Mann-Whitney test, cf. Hollander & Wolfe (1999), page 111
> ### exact p-value and confidence interval for the difference in location
> ### (At term - 12-26 Weeks)
> wt <- wilcox_test(pd ~ age, data = water_transfer, 
+             distribution = "exact", conf.int = TRUE)
> print(wt)

	Exact Wilcoxon Mann-Whitney Rank Sum Test

data:  pd by groups 12-26 Weeks, At term 
T = -1.2247, p-value = 0.2544
alternative hypothesis: true mu is not equal to 0 
95 percent confidence interval:
 -0.76  0.15 
sample estimates:
difference in location 
                -0.305 

> 
> ### extract observed Wilcoxon statistic, i.e, the sum of the
> ### ranks for age = "12-26 Weeks"
> statistic(wt, "linear")
            [,1]
12-26 Weeks   30
> 
> ### its expectation
> expectation(wt)
[1] 40
> 
> ### and variance
> covariance(wt)
         [,1]
[1,] 66.66667
> 
> ### and, finally, the exact two-sided p-value
> pvalue(wt)
[1] 0.2544123
> 
> ### Confidence interval for difference (12-26 Weeks - At term)
> wilcox_test(pd ~ age, data = water_transfer, 
+     xtrafo = function(data) 
+         trafo(data, factor_trafo = function(x) as.numeric(x == levels(x)[2])),
+         distribution = "exact", conf.int = TRUE)

	Exact Wilcoxon Mann-Whitney Rank Sum Test

data:  pd by groups 12-26 Weeks, At term 
T = 1.2247, p-value = 0.2544
alternative hypothesis: true mu is not equal to 0 
95 percent confidence interval:
 -0.15  0.76 
sample estimates:
difference in location 
                 0.305 

> 
> ### Permutation test, asymptotic p-value
> oneway_test(pd ~ age, data = water_transfer)

	Asymptotic 2-Sample Permutation Test

data:  pd by groups 12-26 Weeks, At term 
T = -1.5225, p-value = 0.1279
alternative hypothesis: true mu is not equal to 0 

> 
> ### approximate p-value (with 99% confidence interval)
> pvalue(oneway_test(pd ~ age, data = water_transfer, 
+                  distribution = approximate(B = 9999)))
[1] 0.1311131
99 percent confidence interval:
 0.1225462 0.1400337 

> ### exact p-value
> pt <- oneway_test(pd ~ age, data = water_transfer, distribution = "exact")
> pvalue(pt)
[1] 0.1318681
> 
> ### plot density and distribution of the standardised 
> ### test statistic
> layout(matrix(1:2, nrow = 2))
> s <- support(pt)
> d <- sapply(s, function(x) dperm(pt, x))
> p <- sapply(s, function(x) pperm(pt, x))
> plot(s, d, type = "S", xlab = "Teststatistic", ylab = "Density")
> plot(s, p, type = "S", xlab = "Teststatistic", ylab = "Cumm. Probability")
> 
> ### Length of YOY Gizzard Shad from Kokosing Lake, Ohio,
> ### sampled in Summer 1984, Hollander & Wolfe (1999), Table 6.3, page 200
> YOY <- data.frame(length = c(46, 28, 46, 37, 32, 41, 42, 45, 38, 44, 
+                              42, 60, 32, 42, 45, 58, 27, 51, 42, 52, 
+                              38, 33, 26, 25, 28, 28, 26, 27, 27, 27, 
+                              31, 30, 27, 29, 30, 25, 25, 24, 27, 30),
+                   site = factor(c(rep("I", 10), rep("II", 10),
+                                   rep("III", 10), rep("IV", 10))))
> 
> ### Kruskal-Wallis test, approximate exact p-value
> kw <- kruskal_test(length ~ site, data = YOY, 
+              distribution = approximate(B = 9999))
> kw

	Approximative Kruskal-Wallis Test

data:  length by groups I, II, III, IV 
T = 22.8524, p-value < 2.2e-16

> pvalue(kw)
[1] 0
99 percent confidence interval:
 0.0000000000 0.0005297444 

> 
> ### Nemenyi-Damico-Wolfe-Dunn test (joint ranking)
> ### Hollander & Wolfe (1999), page 244 
> ### (where Steel-Dwass results are given)
> if (require(multcomp)) {
+ 
+     NDWD <- oneway_test(length ~ site, data = YOY,
+         ytrafo = function(data) trafo(data, numeric_trafo = rank),
+         xtrafo = function(data) trafo(data, factor_trafo = function(x)
+             model.matrix(~x - 1) %*% t(contrMat(table(x), "Tukey"))),
+         teststat = "maxtype", distribution = approximate(B = 90000))
+ 
+     ### global p-value
+     print(pvalue(NDWD))
+ 
+     ### sites (I = II) != (III = IV) at alpha = 0.01 (page 244)
+     print(pvalue(NDWD, adjusted = TRUE))
+ }
Loading required package: multcomp
[1] 0.0004888889
99 percent confidence interval:
 0.0003199269 0.0007126921 

               [,1]
II-I   0.9469666667
III-I  0.0089333333
IV-I   0.0070666667
III-II 0.0006777778
IV-II  0.0004777778
IV-III 0.9998777778
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "MarginalHomogeneityTest"
> 
> ### * MarginalHomogeneityTest
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: MarginalHomogeneityTest
> ### Title: Marginal Homogeneity Test
> ### Aliases: mh_test mh_test.table mh_test.formula mh_test.SymmetryProblem
> ### Keywords: htest
> 
> ### ** Examples
> 
> 
> ### Opinions on Pre- and Extramarital Sex, Agresti (2002), page 421
> opinions <- c("always wrong", "almost always wrong", 
+               "wrong only sometimes", "not wrong at all")
> 
> PreExSex <- as.table(matrix(c(144, 33, 84, 126, 
+                                 2,  4, 14,  29, 
+                                 0,  2,  6,  25, 
+                                 0,  0,  1,  5), nrow = 4, 
+                             dimnames = list(PremaritalSex = opinions,
+                                             ExtramaritalSex = opinions)))
> 
> ### treating response as nominal
> mh_test(PreExSex)

	Asymptotic Marginal-Homogenity Test

data:  response by
	 groups ExtramaritalSex, PremaritalSex 
	 stratified by  
T = 271.9222, df = 3, p-value < 2.2e-16

> 
> ### and as ordinal
> mh_test(PreExSex, scores = list(response = 1:length(opinions)))

	Asymptotic Marginal-Homogenity Test for Ordered Data

data:  response (ordered) by
	 groups ExtramaritalSex, PremaritalSex 
	 stratified by  
T = 270.7402, df = 1, p-value < 2.2e-16

> 
> 
> 
> 
> cleanEx(); ..nameEx <- "MaxstatTest"
> 
> ### * MaxstatTest
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: MaxstatTest
> ### Title: Maximally Selected Statistics
> ### Aliases: maxstat_test maxstat_test.formula
> ###   maxstat_test.IndependenceProblem
> ### Keywords: htest
> 
> ### ** Examples
> 
> 
> data(treepipit, package = "coin")
> 
> maxstat_test(counts ~ coverstorey, data = treepipit)

	Asymptotic Maxstat Test

data:  counts by coverstorey 
T = 4.3139, p-value = 0.0002121
sample estimates:
cutpoint in  coverstorey 
                      40 

> 
> 
> 
> 
> cleanEx(); ..nameEx <- "PermutationDistribution"
> 
> ### * PermutationDistribution
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: PermutationDistribution
> ### Title: Permutation Distribution of Conditional Independence Tests
> ### Aliases: pperm pperm-methods pperm,NullDistribution-method
> ###   pperm,IndependenceTest-method pperm,ScalarIndependenceTest-method
> ###   pperm,MaxTypeIndependenceTest-method
> ###   pperm,QuadTypeIndependenceTest-method qperm qperm-methods
> ###   qperm,NullDistribution-method qperm,IndependenceTest-method
> ###   qperm,ScalarIndependenceTest-method
> ###   qperm,MaxTypeIndependenceTest-method
> ###   qperm,QuadTypeIndependenceTest-method dperm dperm-methods
> ###   dperm,NullDistribution-method dperm,IndependenceTest-method
> ###   dperm,ScalarIndependenceTest-method
> ###   dperm,MaxTypeIndependenceTest-method
> ###   dperm,QuadTypeIndependenceTest-method support support-methods
> ###   support,NullDistribution-method support,IndependenceTest-method
> ###   support,ScalarIndependenceTest-method
> ###   support,MaxTypeIndependenceTest-method
> ###   support,QuadTypeIndependenceTest-method
> ### Keywords: methods htest
> 
> ### ** Examples
> 
> 
> ### artificial 2-sample problem
> df <- data.frame(y = rnorm(20), x = gl(2, 10))
> 
> ### Ansari-Bradley test
> at <- ansari_test(y ~ x, data = df, distribution = "exact")
> 
> ### density of the exact distribution of the Ansari-Bradley statistic
> dens <- sapply(support(at), dperm, object = at)
> 
> ### 95% quantile
> qperm(at, 0.95)
[1] 1.669331
> 
> ### one-sided p-value
> pperm(at, statistic(at))
[1] 0.698635
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "ScaleTests"
> 
> ### * ScaleTests
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ScaleTests
> ### Title: Independent Two- and K-Sample Scale Tests
> ### Aliases: ansari_test ansari_test.formula
> ###   ansari_test.IndependenceProblem fligner_test.formula
> ###   fligner_test.IndependenceProblem fligner_test
> ### Keywords: htest
> 
> ### ** Examples
> 
> 
> ### Serum Iron Determination Using Hyland Control Sera
> ### Hollander & Wolfe (1999), page 147
> sid <- data.frame(
+     serum = c(111, 107, 100, 99, 102, 106, 109, 108, 104, 99,
+               101, 96, 97, 102, 107, 113, 116, 113, 110, 98,
+               107, 108, 106, 98, 105, 103, 110, 105, 104,
+               100, 96, 108, 103, 104, 114, 114, 113, 108, 106, 99),
+     method = factor(gl(2, 20), labels = c("Ramsay", "Jung-Parekh")))
> 
> ### Ansari-Bradley test, asymptotical p-value
> ansari_test(serum ~ method, data = sid)

	Asymptotic Ansari-Bradley Test

data:  serum by groups Ramsay, Jung-Parekh 
T = -1.3363, p-value = 0.1815
alternative hypothesis: true mu is not equal to 1 

> 
> ### exact p-value
> ansari_test(serum ~ method, data = sid, distribution = "exact")

	Exact Ansari-Bradley Test

data:  serum by groups Ramsay, Jung-Parekh 
T = -1.3363, p-value = 0.1881
alternative hypothesis: true mu is not equal to 1 

> 
> ### Platelet Counts of Newborn Infants
> ### Hollander & Wolfe (1999), Table 5.4, page 171
> platalet_counts <- data.frame(
+     counts = c(120, 124, 215, 90, 67, 95, 190, 180, 135, 399, 
+                12, 20, 112, 32, 60, 40),
+     treatment = factor(c(rep("Prednisone", 10), rep("Control", 6))))
> 
> ### Lepage test, Hollander & Wolfe (1999), page 172 
> lt <- independence_test(counts ~ treatment, data = platalet_counts,
+     ytrafo = function(data) trafo(data, numeric_trafo = function(x)       
+         cbind(rank(x), ansari_trafo(x))),
+     teststat = "quadtype", distribution = approximate(B = 9999))
> 
> lt

	Approximative General Independence Test

data:  counts by groups Control, Prednisone 
T = 9.3384, p-value = 0.0042

> 
> ### where did the rejection come from? Use maximum statistic
> ### instead of a quadratic form
> ltmax <- independence_test(counts ~ treatment, data = platalet_counts,
+     ytrafo = function(data) trafo(data, numeric_trafo = function(x) 
+         matrix(c(rank(x), ansari_trafo(x)), ncol = 2,
+                dimnames = list(1:length(x), c("Location", "Scale")))),
+     teststat = "maxtype")
> 
> ### points to a difference in location
> pvalue(ltmax, adjusted = TRUE)
         Location     Scale
Control 0.0067991 0.6189816
> 
> ### Funny: We could have used a simple Bonferroni procedure
> ### since the correlation between the Wilcoxon and Ansari-Bradley 
> ### test statistics is zero
> covariance(ltmax)
     [,1] [,2]
[1,]   85    0
[2,]    0   21
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "SpearmanTest"
> 
> ### * SpearmanTest
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: SpearmanTest
> ### Title: Spearman's Test on Independence
> ### Aliases: spearman_test spearman_test.formula
> ###   spearman_test.IndependenceProblem
> ### Keywords: htest
> 
> ### ** Examples
> 
> 
> spearman_test(CONT ~ INTG, data = USJudgeRatings)

	Asymptotic Spearman Correlation Test

data:  CONT by INTG 
T = -1.1437, p-value = 0.2527
alternative hypothesis: true mu is not equal to 0 

> 
> 
> 
> 
> cleanEx(); ..nameEx <- "SurvTest"
> 
> ### * SurvTest
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: SurvTest
> ### Title: Independent Two- and K-Sample Tests for Censored Data
> ### Aliases: surv_test surv_test.formula surv_test.IndependenceProblem
> ### Keywords: htest
> 
> ### ** Examples
> 
> 
> ### asymptotic tests for carcinoma data
> data(ocarcinoma, package = "coin")
> surv_test(Surv(time, event) ~ stadium, data = ocarcinoma)

	Asymptotic Logrank Test

data:  y by groups II, IIA 
T = -2.3373, p-value = 0.01942
alternative hypothesis: two.sided 

> survdiff(Surv(time, event) ~ stadium, data = ocarcinoma)
Call:
survdiff(formula = Surv(time, event) ~ stadium, data = ocarcinoma)

             N Observed Expected (O-E)^2/E (O-E)^2/V
stadium=II  15        6     11.3      2.51      5.57
stadium=IIA 20       16     10.7      2.67      5.57

 Chisq= 5.6  on 1 degrees of freedom, p= 0.0183 
> 
> ### example data given in Callaert (2003)
> exdata <- data.frame(time = c(1, 1, 5, 6, 6, 6, 6, 2, 2, 2, 3, 4, 4, 5, 5),
+                      event = rep(TRUE, 15),
+                      group = factor(c(rep(0, 7), rep(1, 8))))
> ### p = 0.0523
> survdiff(Surv(time, event) ~ group, data = exdata)
Call:
survdiff(formula = Surv(time, event) ~ group, data = exdata)

        N Observed Expected (O-E)^2/E (O-E)^2/V
group=0 7        7     9.84      0.82      3.76
group=1 8        8     5.16      1.56      3.76

 Chisq= 3.8  on 1 degrees of freedom, p= 0.0523 
> ### p = 0.0505
> surv_test(Surv(time, event) ~ group, data = exdata, 
+           distribution = exact())

	Exact Logrank Test

data:  y by groups 0, 1 
T = -1.9201, p-value = 0.05051
alternative hypothesis: two.sided 

> 
> 
> 
> 
> cleanEx(); ..nameEx <- "SymmetryTests"
> 
> ### * SymmetryTests
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: SymmetryTests
> ### Title: Symmetry Tests
> ### Aliases: friedman_test friedman_test.formula
> ###   friedman_test.SymmetryProblem wilcoxsign_test.formula
> ###   wilcoxsign_test.IndependenceProblem wilcoxsign_test
> ### Keywords: htest
> 
> ### ** Examples
> 
> 
> ### Hollander & Wolfe (1999), Table 7.1, page 274
> ### Comparison of three methods ("round out", "narrow angle", and
> ###  "wide angle") for rounding first base. 
> RoundingTimes <- data.frame(
+     times = c(5.40, 5.50, 5.55,
+               5.85, 5.70, 5.75,
+               5.20, 5.60, 5.50,
+               5.55, 5.50, 5.40,
+               5.90, 5.85, 5.70,
+               5.45, 5.55, 5.60,
+               5.40, 5.40, 5.35,
+               5.45, 5.50, 5.35,
+               5.25, 5.15, 5.00,
+               5.85, 5.80, 5.70,
+               5.25, 5.20, 5.10,
+               5.65, 5.55, 5.45,
+               5.60, 5.35, 5.45,
+               5.05, 5.00, 4.95,
+               5.50, 5.50, 5.40,
+               5.45, 5.55, 5.50,
+               5.55, 5.55, 5.35,
+               5.45, 5.50, 5.55,
+               5.50, 5.45, 5.25,
+               5.65, 5.60, 5.40,
+               5.70, 5.65, 5.55,
+               6.30, 6.30, 6.25),
+     methods = factor(rep(c("Round Out", "Narrow Angle", "Wide Angle"), 22)),
+     block = factor(rep(1:22, rep(3, 22))))
> 
> ### classical global test
> friedman_test(times ~ methods | block, data = RoundingTimes)

	Asymptotic Friedman Test

data:  times by
	 groups Narrow Angle, Round Out, Wide Angle 
	 stratified by block 
T = 11.1429, df = 2, p-value = 0.003805

> 
> ### parallel coordinates plot
> matplot(t(matrix(RoundingTimes$times, ncol = 3, byrow = TRUE)), 
+         type = "l", col = 1, lty = 1, axes = FALSE, ylab = "Time", 
+         xlim = c(0.5, 3.5))
> axis(1, at = 1:3, labels = levels(RoundingTimes$methods))
> axis(2)
> 
> ### where do the differences come from?
> ### Wilcoxon-Nemenyi-McDonald-Thompson test
> ### Hollander & Wolfe (1999), page 295
> if (require(multcomp)) {
+ 
+     ### all pairwise comparisons
+     rtt <- symmetry_test(times ~ methods | block, data = RoundingTimes,
+          teststat = "maxtype",
+          xtrafo = function(data)
+              trafo(data, factor_trafo = function(x)
+                  model.matrix(~ x - 1) %*% t(contrMat(table(x), "Tukey"))
+              ),
+          ytrafo = function(data)
+              trafo(data, numeric_trafo = rank, block = RoundingTimes$block)
+     )
+ 
+     ### a global test, again
+     print(pvalue(rtt))
+ 
+     ### simultaneous P-values for all pair comparisons
+     ### Wide Angle vs. Round Out differ (Hollander and Wolfe, 1999, page 296)
+     print(pvalue(rtt, adjusted = TRUE))
+ }
Loading required package: multcomp
[1] 0.003455914
 percent confidence interval:
 0.003205723 0.003706106 

                               [,1]
Round Out-Narrow Angle  0.623924061
Wide Angle-Narrow Angle 0.053791966
Wide Angle-Round Out    0.003398077
> 
> ### Strength Index of Cotton, Hollander & Wolfe (1999), Table 7.5, page 286
> sc <- data.frame(block = factor(c(rep(1, 5), rep(2, 5), rep(3, 5))),
+                  potash = ordered(rep(c(144, 108, 72, 54, 36), 3)),
+                  strength = c(7.46, 7.17, 7.76, 8.14, 7.63,
+                               7.68, 7.57, 7.73, 8.15, 8.00,
+                               7.21, 7.80, 7.74, 7.87, 7.93))
> 
> ### Page test for ordered alternatives
> ft <- friedman_test(strength ~ potash | block, data = sc)
> ft

	Asymptotic Page Test

data:  strength by
	 groups 36 < 54 < 72 < 108 < 144 
	 stratified by block 
T = 7.0533, df = 1, p-value = 0.007912

> 
> ### one-sided p-value
> 1 - pnorm(sqrt(statistic(ft)))
[1] 0.003955894
> 
> ### approximate null distribution via Monte-Carlo
> pvalue(friedman_test(strength ~ potash | block, data = sc, 
+                      distribution = approximate(B = 9999)))
[1] 0.00470047
99 percent confidence interval:
 0.003124471 0.006765311 

> 
> 
> 
> 
> cleanEx(); ..nameEx <- "Transformations"
> 
> ### * Transformations
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: Transformations
> ### Title: Functions for Data Transformations
> ### Aliases: trafo id_trafo ansari_trafo fligner_trafo normal_trafo
> ###   median_trafo consal_trafo maxstat_trafo logrank_trafo f_trafo
> ### Keywords: manip
> 
> ### ** Examples
> 
> 
> ### dummy matrices, 2-sample problem (only one column)
> f_trafo(y <- gl(2, 5))
   1
1  1
2  1
3  1
4  1
5  1
6  0
7  0
8  0
9  0
10 0
> 
> ### K-sample problem (K columns)
> f_trafo(y <- gl(5, 2))
   1 2 3 4 5
1  1 0 0 0 0
2  1 0 0 0 0
3  0 1 0 0 0
4  0 1 0 0 0
5  0 0 1 0 0
6  0 0 1 0 0
7  0 0 0 1 0
8  0 0 0 1 0
9  0 0 0 0 1
10 0 0 0 0 1
> 
> ### normal scores
> normal_trafo(x <- rnorm(10))
 [1] -0.6045853 -0.1141853 -1.3351777  1.3351777  0.1141853 -0.9084579
 [7]  0.3487557  0.9084579  0.6045853 -0.3487557
> 
> ### and now together
> trafo(data.frame(x = x, y = y), numeric_trafo = normal_trafo)
              1 2 3 4 5
1  -0.6045853 1 0 0 0 0
2  -0.1141853 1 0 0 0 0
3  -1.3351777 0 1 0 0 0
4   1.3351777 0 1 0 0 0
5   0.1141853 0 0 1 0 0
6  -0.9084579 0 0 1 0 0
7   0.3487557 0 0 0 1 0
8   0.9084579 0 0 0 1 0
9   0.6045853 0 0 0 0 1
10 -0.3487557 0 0 0 0 1
attr(,"assign")
[1] 1 2 2 2 2 2
> 
> ### maximally selected statistics
> maxstat_trafo(rnorm(10))
   x <= 0.39 x <= -0.621 x <= 1.125 x <= -0.045 x <= -0.016 x <= 0.944
1          0           0          0           0           0          0
2          1           0          1           0           0          1
3          1           1          1           1           1          1
4          1           1          1           1           1          1
5          0           0          1           0           0          0
6          1           0          1           1           1          1
7          1           0          1           0           1          1
8          0           0          1           0           0          1
9          0           0          1           0           0          1
10         0           0          1           0           0          1
   x <= 0.821 x <= 0.594
1           0          0
2           1          1
3           1          1
4           1          1
5           0          0
6           1          1
7           1          1
8           0          0
9           1          0
10          1          1
> 
> ### apply transformation blockwise (e.g. for Friedman test)
> trafo(data.frame(y = 1:20), numeric_trafo = rank, block = gl(4, 5))
      [,1]
 [1,]    1
 [2,]    2
 [3,]    3
 [4,]    4
 [5,]    5
 [6,]    1
 [7,]    2
 [8,]    3
 [9,]    4
[10,]    5
[11,]    1
[12,]    2
[13,]    3
[14,]    4
[15,]    5
[16,]    1
[17,]    2
[18,]    3
[19,]    4
[20,]    5
attr(,"assign")
[1] 1
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "asat"
> 
> ### * asat
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: asat
> ### Title: Toxicological Study on Female Wistar Rats
> ### Aliases: asat
> ### Keywords: datasets
> 
> ### ** Examples
> 
> 
> data(asat, package = "coin")
> 
> ### proof-of-safety based on ratio of medians
> pos <- wilcox_test(I(log(asat)) ~ group, data = asat, alternative = "less", 
+                    conf.int = TRUE, distribution = "exact")
> 
> ### one-sided confidence set. Safety cannot be concluded since the effect of
> ### the compound exceeds 20% of the control median
> exp(confint(pos)$conf.int)
[1] 0.000000 1.337778
attr(,"conf.level")
[1] 0.95
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "expectation-methods"
> 
> ### * expectation-methods
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: expectation-methods
> ### Title: Extract the Expectation and Variance / Covariance of Linear
> ###   Statistics
> ### Aliases: expectation expectation-methods
> ###   expectation,IndependenceTest-method
> ###   expectation,IndependenceTestStatistic-method
> ###   expectation,ScalarIndependenceTestStatistic-method
> ###   expectation,MaxTypeIndependenceTestStatistic-method
> ###   expectation,QuadTypeIndependenceTestStatistic-method covariance
> ###   covariance-methods covariance,CovarianceMatrix-method
> ###   covariance,IndependenceTest-method
> ###   covariance,IndependenceTestStatistic-method
> ###   covariance,ScalarIndependenceTestStatistic-method
> ###   covariance,MaxTypeIndependenceTestStatistic-method
> ###   covariance,QuadTypeIndependenceTestStatistic-method variance
> ###   variance-methods variance,Variance-method
> ###   variance,CovarianceMatrix-method variance,IndependenceTest-method
> ###   variance,IndependenceTestStatistic-method
> ###   variance,ScalarIndependenceTestStatistic-method
> ###   variance,MaxTypeIndependenceTestStatistic-method
> ###   variance,QuadTypeIndependenceTestStatistic-method
> ### Keywords: methods
> 
> ### ** Examples
> 
> 
> df <- data.frame(y = gl(3, 2), x = gl(3, 2)[sample(1:6)])
> 
> ### Cochran-Mantel-Haenzel Test
> ct <- cmh_test(y ~ x, data = df)
>  
> ### the linear statistic, i.e, the contingency table
> l <- statistic(ct, type = "linear")
> l
  1 2 3
1 1 0 1
2 0 2 0
3 1 0 1
> 
> ### expectation
> El <- expectation(ct)
> El
[1] 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667 0.6666667
[8] 0.6666667 0.6666667
> 
> ### covariance
> Vl <- covariance(ct)
> Vl
             [,1]        [,2]        [,3]        [,4]        [,5]        [,6]
 [1,]  0.35555556 -0.17777778 -0.17777778 -0.17777778  0.08888889  0.08888889
 [2,] -0.17777778  0.35555556 -0.17777778  0.08888889 -0.17777778  0.08888889
 [3,] -0.17777778 -0.17777778  0.35555556  0.08888889  0.08888889 -0.17777778
 [4,] -0.17777778  0.08888889  0.08888889  0.35555556 -0.17777778 -0.17777778
 [5,]  0.08888889 -0.17777778  0.08888889 -0.17777778  0.35555556 -0.17777778
 [6,]  0.08888889  0.08888889 -0.17777778 -0.17777778 -0.17777778  0.35555556
 [7,] -0.17777778  0.08888889  0.08888889 -0.17777778  0.08888889  0.08888889
 [8,]  0.08888889 -0.17777778  0.08888889  0.08888889 -0.17777778  0.08888889
 [9,]  0.08888889  0.08888889 -0.17777778  0.08888889  0.08888889 -0.17777778
             [,7]        [,8]        [,9]
 [1,] -0.17777778  0.08888889  0.08888889
 [2,]  0.08888889 -0.17777778  0.08888889
 [3,]  0.08888889  0.08888889 -0.17777778
 [4,] -0.17777778  0.08888889  0.08888889
 [5,]  0.08888889 -0.17777778  0.08888889
 [6,]  0.08888889  0.08888889 -0.17777778
 [7,]  0.35555556 -0.17777778 -0.17777778
 [8,] -0.17777778  0.35555556 -0.17777778
 [9,] -0.17777778 -0.17777778  0.35555556
> 
> ### the standardized contingency table (hard way)
> (l - El) / sqrt(variance(ct))
          1         2         3
1  0.559017 -1.118034  0.559017
2 -1.118034  2.236068 -1.118034
3  0.559017 -1.118034  0.559017
> 
> ### easy way
> statistic(ct, type = "standardized")
          1         2         3
1  0.559017 -1.118034  0.559017
2 -1.118034  2.236068 -1.118034
3  0.559017 -1.118034  0.559017
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "glioma"
> 
> ### * glioma
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: glioma
> ### Title: Malignant Glioma Pilot Study
> ### Aliases: glioma
> ### Keywords: datasets
> 
> ### ** Examples
> 
> 
> data(glioma, package = "coin")
> 
> par(mfrow=c(1,2))
> 
> ### Grade III glioma
> g3 <- subset(glioma, histology == "Grade3")
> 
> ### Plot Kaplan-Meier curves
> plot(survfit(Surv(time, event) ~ group, data=g3), 
+      main="Grade III Glioma", lty=c(2,1), 
+      legend.text=c("Control", "Treated"),
+      legend.bty=1, ylab="Probability", 
+      xlab="Survival Time in Month")
> 
> ### logrank test
> surv_test(Surv(time, event) ~ group, data = g3, 
+              distribution = "exact")

	Exact Logrank Test

data:  y by groups Control, RIT 
T = 2.1711, p-value = 0.02877
alternative hypothesis: two.sided 

> 
> ### Grade IV glioma
> gbm <- subset(glioma, histology == "GBM")
> 
> ### Plot Kaplan-Meier curves
> plot(survfit(Surv(time, event) ~ group, data=gbm), 
+      main="Grade IV Glioma", lty=c(2,1), 
+      legend.text=c("Control", "Treated"),
+      legend.bty=1, legend.pos=1, ylab="Probability", 
+      xlab="Survival Time in Month")
>    
> ### logrank test
> surv_test(Surv(time, event) ~ group, data = gbm, 
+              distribution = "exact")

	Exact Logrank Test

data:  y by groups Control, RIT 
T = 3.2215, p-value = 0.0001588
alternative hypothesis: two.sided 

> 
> ### stratified logrank test
> surv_test(Surv(time, event) ~ group | histology, data = glioma,
+              distribution = approximate(B = 10000))

	Approximative Logrank Test

data:  y by
	 groups Control, RIT 
	 stratified by histology 
T = 3.6704, p-value < 2.2e-16
alternative hypothesis: two.sided 

> 
> 
> 
> 
> graphics::par(get("par.postscript", env = .CheckExEnv))
> cleanEx(); ..nameEx <- "jobsatisfaction"
> 
> ### * jobsatisfaction
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: jobsatisfaction
> ### Title: Income and Job Satisfaction
> ### Aliases: jobsatisfaction
> ### Keywords: datasets
> 
> ### ** Examples
> 
> 
> data(jobsatisfaction, package = "coin")
> 
> # Generalized Cochran-Mantel-Haenzel test
> cmh_test(jobsatisfaction)

	Asymptotic Generalised Cochran-Mantel-Haenszel Test

data:  Job.Satisfaction by
	 groups <5000, 5000-15000, 15000-25000, >25000 
	 stratified by Gender 
T = 10.2001, df = 9, p-value = 0.3345

> 
> 
> 
> 
> cleanEx(); ..nameEx <- "mercuryfish"
> 
> ### * mercuryfish
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: mercuryfish
> ### Title: Chromosomal effects of mercury contaminated fish consumption
> ### Aliases: mercuryfish
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(mercuryfish)
> 
> coherence <- function(data) {
+     x <- as.matrix(data)
+     matrix(apply(x, 1, function(y)
+         sum(colSums(t(x) < y) == ncol(x)) - 
+         sum(colSums(t(x) > y) == ncol(x))), ncol = 1)
+ }
> 
> ### POSET-test
> poset <- independence_test(mercury + abnormal + ccells ~ group, data =
+                            mercuryfish, ytrafo = coherence)
> 
> ### linear statistic (T in Rosenbaum's, 1994, notation)
> statistic(poset, "linear")
        [,1]
control -237
> 
> ### expectation
> expectation(poset)
[1] 0
> 
> ### variance (Rosenbaum, 1994, uses the unconditional approach)
> covariance(poset)
         [,1]
[1,] 3097.954
> 
> ### the standardized statistic
> statistic(poset)
[1] -4.258051
> 
> ### and asymptotic p-value
> pvalue(poset)
[1] 2.062169e-05
> 
> ### exact p-value
> independence_test(mercury + abnormal + ccells ~ group, data =
+                   mercuryfish, ytrafo = coherence, distribution = "exact")

	Exact General Independence Test

data:  mercury, abnormal, ccells by groups control, exposed 
T = -4.2581, p-value = 4.486e-06
alternative hypothesis: two.sided 

> 
> ### multivariate analysis
> mvtest <- independence_test(mercury + abnormal + ccells ~ group, 
+                             data = mercuryfish)
> 
> ### global p-value
> pvalue(mvtest)
[1] 0.007140626
 percent confidence interval:
 0.006371666 0.007909585 

> 
> ### adjusted univariate p-value
> pvalue(mvtest, adjusted = TRUE)
                  mercury   abnormal     ccells
group.control 0.007991574 0.01726738 0.03830126
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "neuropathy"
> 
> ### * neuropathy
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: neuropathy
> ### Title: Acute Painful Diabetic Neuropathy
> ### Aliases: neuropathy
> ### Keywords: datasets
> 
> ### ** Examples
> 
> 
> data(neuropathy, package = "coin")
> 
> ### compare with Table 2 of Conover & Salsburg (1988)
> oneway_test(pain ~ group, data = neuropathy, alternative = "less",
+             distribution = "exact")

	Exact 2-Sample Permutation Test

data:  pain by groups control, treat 
T = -1.3191, p-value = 0.09589
alternative hypothesis: true mu is less than 0 

> 
> wilcox_test(pain ~ group, data = neuropathy, alternative = "less", 
+             distribution = "exact")

	Exact Wilcoxon Mann-Whitney Rank Sum Test

data:  pain by groups control, treat 
T = -0.9817, p-value = 0.1654
alternative hypothesis: true mu is less than 0 

> 
> oneway_test(pain ~ group, data = neuropathy, 
+             distribution = approximate(B = 10000),
+             alternative = "less", ytrafo = function(data) trafo(data,
+             numeric_trafo = consal_trafo))

	Approximative 2-Sample Permutation Test

data:  pain by groups control, treat 
T = -1.8683, p-value = 0.0284
alternative hypothesis: true mu is less than 0 

> 
> 
> 
> 
> cleanEx(); ..nameEx <- "ocarcinoma"
> 
> ### * ocarcinoma
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: ocarcinoma
> ### Title: Ovarian Carcinoma
> ### Aliases: ocarcinoma
> ### Keywords: datasets
> 
> ### ** Examples
> 
> 
> data(ocarcinoma, package = "coin")
> 
> ### logrank test with exact two-sided p-value
> lrt <- surv_test(Surv(time, event) ~ stadium, data = ocarcinoma,
+                     distribution = "exact")
> 
> ### the test statistic
> statistic(lrt)
[1] -2.337284
> 
> ### p-value
> pvalue(lrt)
[1] 0.01819758
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "pvalue-methods"
> 
> ### * pvalue-methods
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: pvalue-methods
> ### Title: Extract P-Values
> ### Aliases: pvalue pvalue-methods pvalue,NullDistribution-method
> ###   pvalue,IndependenceTest-method pvalue,ScalarIndependenceTest-method
> ###   pvalue,MaxTypeIndependenceTest-method
> ###   pvalue,QuadTypeIndependenceTest-method
> ### Keywords: methods htest
> 
> ### ** Examples
> 
> 
> ### artificial 2-sample problem
> df <- data.frame(y = rnorm(20), x = gl(2, 10))
>  
> ### Ansari-Bradley test
> at <- ansari_test(y ~ x, data = df, distribution = "exact")
> 
> at

	Exact Ansari-Bradley Test

data:  y by groups 1, 2 
T = 0.4553, p-value = 0.7102
alternative hypothesis: true mu is not equal to 1 

> 
> pvalue(at)
[1] 0.710234
> 
> 
> 
> 
> cleanEx(); ..nameEx <- "rotarod"
> 
> ### * rotarod
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: rotarod
> ### Title: Rotating Rats Data
> ### Aliases: rotarod
> ### Keywords: datasets
> 
> ### ** Examples
> 
> 
> data(rotarod, package = "coin")
> 
> ### Wilcoxon-Mann-Whitney Rank Sum Test
> 
> ### one-sided exact (0.0186)
> wilcox_test(time ~ group, data = rotarod, 
+     alternative = "greater", distribution = "exact")

	Exact Wilcoxon Mann-Whitney Rank Sum Test

data:  time by groups control, treatment 
T = 2.4389, p-value = 0.01863
alternative hypothesis: true mu is greater than 0 

> ### two-sided exact (0.0373)
> wilcox_test(time ~ group, data = rotarod, distribution = "exact")

	Exact Wilcoxon Mann-Whitney Rank Sum Test

data:  time by groups control, treatment 
T = 2.4389, p-value = 0.03727
alternative hypothesis: true mu is not equal to 0 

> ### two-sided asymptotical (0.0147)
> wilcox_test(time ~ group, data = rotarod)

	Asymptotic Wilcoxon Mann-Whitney Rank Sum Test

data:  time by groups control, treatment 
T = 2.4389, p-value = 0.01473
alternative hypothesis: true mu is not equal to 0 

> 
> 
> 
> 
> cleanEx(); ..nameEx <- "sphase"
> 
> ### * sphase
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: sphase
> ### Title: S-phase Fraction of Tumor Cells
> ### Aliases: sphase
> ### Keywords: datasets
> 
> ### ** Examples
> 
> 
> data(sphase, package = "coin")
> maxstat_test(Surv(RFS, event) ~ SPF, data = sphase)

	Asymptotic Maxstat Test

data:  y by SPF 
T = 2.3966, p-value = 0.1583
sample estimates:
cutpoint in  SPF 
             107 

> 
> ### reproduce the test statistic reported in Hothorn & Lausen (2003)
> maxstat_test(Surv(RFS, event) ~ SPF, data = sphase, 
+     ytrafo = function(data) trafo(data, surv_trafo = function(x) 
+         logrank_trafo(x, ties.method = "HL")))

	Asymptotic Maxstat Test

data:  y by SPF 
T = 2.4033, p-value = 0.1548
sample estimates:
cutpoint in  SPF 
             107 

> 
> 
> 
> 
> cleanEx(); ..nameEx <- "statistic-methods"
> 
> ### * statistic-methods
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: statistic-methods
> ### Title: Extract Test Statistics, Linear Statistics and Standardized
> ###   Statistics
> ### Aliases: statistic statistic-methods statistic,IndependenceTest-method
> ###   statistic,ScalarIndependenceTestStatistic-method
> ###   statistic,MaxTypeIndependenceTestStatistic-method
> ###   statistic,QuadTypeIndependenceTestStatistic-method
> ### Keywords: methods
> 
> ### ** Examples
> 
> 
> df <- data.frame(y = gl(4, 5), x = gl(5, 4))
> 
> ### Cochran-Mantel-Haenzel Test
> ct <- cmh_test(y ~ x, data = df)
> 
> ### chisq-type statistics
> statistic(ct)
[1] 38
> 
> ### the linear statistic, i.e, the contingency table
> statistic(ct, type = "linear")
  1 2 3 4
1 4 0 0 0
2 1 3 0 0
3 0 2 2 0
4 0 0 3 1
5 0 0 0 4
> 
> ### the same
> table(df$x, df$y)
   
    1 2 3 4
  1 4 0 0 0
  2 1 3 0 0
  3 0 2 2 0
  4 0 0 3 1
  5 0 0 0 4
> 
> ### and the standardized contingency table for illustrating
> ### departures from the null hypothesis of independence of x and y
> statistic(ct, type = "standardized")
          1         2         3         4
1  3.774917 -1.258306 -1.258306 -1.258306
2  0.000000  2.516611 -1.258306 -1.258306
3 -1.258306  1.258306  1.258306 -1.258306
4 -1.258306 -1.258306  2.516611  0.000000
5 -1.258306 -1.258306 -1.258306  3.774917
> 
> 
> 
> cleanEx(); ..nameEx <- "treepipit"
> 
> ### * treepipit
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: treepipit
> ### Title: Tree Pipit (Anthus trivialis) Forest Data
> ### Aliases: treepipit
> ### Keywords: datasets
> 
> ### ** Examples
> 
> 
> data(treepipit, package = "coin")
> 
> maxstat_test(counts ~ age + coverstorey + coverregen + meanregen +
+                       coniferous + deadtree + cbpiles + ivytree,
+              data = treepipit)

	Asymptotic Maxstat Test

data:  counts by
	 age, coverstorey, coverregen, meanregen, coniferous, deadtree, cbpiles, ivytree 
T = 4.3139, p-value = 0.0006393
sample estimates:
cutpoint in  coverstorey 
                      40 

> 
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  25.89 0.67 26.7 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')