Revision d7f8f2c403437c864e17f92c46e0f4996ac54e7e authored by Torsten Hothorn on 17 November 2005, 00:00:00 UTC, committed by Gabor Csardi on 17 November 2005, 00:00:00 UTC
1 parent 5bae2c9
coin-Ex.R
### * <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)
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"))
library('coin')
assign(".oldSearch", search(), env = .CheckExEnv)
assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
cleanEx(); ..nameEx <- "ContingencyTests"
### * ContingencyTests
flush(stderr()); flush(stdout())
### Name: ContingencyTests
### Title: Independence in General I x K x J 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))
### both Income and Job.Satisfaction unordered
cmh_test(jobsatisfaction)
### both Income and Job.Satisfaction ordered, default scores
lbl_test(jobsatisfaction)
### 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)))
### 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))
### 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)))
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))
)
### same as
normal_test(asat ~ group, data = asat, distribution = "exact",
alternative = "greater")
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)
### extract observed Wilcoxon statistic, i.e, the sum of the
### ranks for age = "12-26 Weeks"
statistic(wt, "linear")
### its expectation
expectation(wt)
### and variance
covariance(wt)
### and, finally, the exact two-sided p-value
pvalue(wt)
### 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)
### Permutation test, asymptotic p-value
oneway_test(pd ~ age, data = water_transfer)
### approximate p-value (with 99% confidence interval)
pvalue(oneway_test(pd ~ age, data = water_transfer,
distribution = approximate(B = 9999)))
### exact p-value
pt <- oneway_test(pd ~ age, data = water_transfer, distribution = "exact")
pvalue(pt)
### 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, 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
pvalue(kw)
### 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, method = "single-step"))
}
cleanEx(); ..nameEx <- "MarginalHomogenityTest"
### * MarginalHomogenityTest
flush(stderr()); flush(stdout())
### Name: MarginalHomogenityTest
### Title: Marginal Homogenity 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)
### and as ordinal
mh_test(PreExSex, scores = list(response = 1:length(opinions)))
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)
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)
### one-sided p-value
pperm(at, statistic(at))
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)
### exact p-value
ansari_test(serum ~ method, data = sid, distribution = "exact")
### Platelet Counts of Newborn Infants
### Hollander & Wolfe, 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, 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
### 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, method = "single-step")
### Funny: We could have used a simple Bonferroni procedure
### since the correlation between the Wilcoxon and Ansari-Bradley
### test statistics is zero
covariance(ltmax)
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)
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)
survdiff(Surv(time, event) ~ stadium, data = ocarcinoma)
### 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)
### p = 0.0505
surv_test(Surv(time, event) ~ group, data = exdata,
distribution = exact())
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)
### 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$methods))
axis(2)
### where do the differences come from?
### Wilcoxon-Nemenyi-McDonald-Thompson test
### Hollander & Wolfe, page 295
if (require(multcomp)) {
### all pairwise comparisons
rtt <- symmetry_test(times ~ methods | block, data = RoundingTimes,
teststat = "maxtype",
xtrafo = function(data)
trafo(data, factor_trafo = function(x)
model.matrix(~ x - 1)
),
ytrafo = function(data)
trafo(data, numeric_trafo = rank, block = RoundingTimes$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, method = "single-step"))
}
### 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
ft <- friedman_test(strength ~ potash | block, data = sc)
ft
### one-sided p-value
1 - pnorm(sqrt(statistic(ft)))
### approximate null distribution via Monte-Carlo
pvalue(friedman_test(strength ~ potash | block, data = sc,
distribution = approximate(B = 9999)))
cleanEx(); ..nameEx <- "Transformations"
### * Transformations
flush(stderr()); flush(stdout())
### Name: Transformations
### Title: Functions for Data Transformations and Score Computations
### 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))
### K-sample problem (K columns)
f_trafo(y <- gl(5, 2))
### normal scores
normal_trafo(x <- rnorm(10))
### and now together
trafo(data.frame(x = x, y = y), numeric_trafo = normal_trafo)
### maximally selected statistics
maxstat_trafo(rnorm(10))
### apply transformation blockwise (e.g. for Friedman test)
trafo(data.frame(y = 1:20), numeric_trafo = rank, block = gl(4, 5))
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)
cleanEx(); ..nameEx <- "expectation-methods"
### * expectation-methods
flush(stderr()); flush(stdout())
### Name: expectation-methods
### Title: Extract the Expectation and Covariance of Linear Statistics
### Aliases: expectation expectation-methods
### expectation,IndependenceTest-method
### expectation,ScalarIndependenceTestStatistic-method
### expectation,MaxTypeIndependenceTestStatistic-method
### expectation,QuadTypeIndependenceTestStatistic-method covariance
### covariance-methods covariance,IndependenceTest-method
### covariance,ScalarIndependenceTestStatistic-method
### covariance,MaxTypeIndependenceTestStatistic-method
### covariance,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
### expectation
El <- expectation(ct)
El
### covariance
Vl <- covariance(ct)
Vl
### the standardized contingency table (hard way)
(l - El) / sqrt(variance(ct))
### easy way
statistic(ct, type = "standardized")
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")
### 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")
### stratified logrank test
surv_test(Surv(time, event) ~ group | histology, data = glioma,
distribution = approximate(B = 10000))
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)
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")
wilcox_test(pain ~ group, data = neuropathy, alternative = "less",
distribution = "exact")
oneway_test(pain ~ group, data = neuropathy,
distribution = approximate(B = 10000),
alternative = "less", ytrafo = function(data) trafo(data,
numeric_trafo = consal_trafo))
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)
### p-value
pvalue(lrt)
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
pvalue(at)
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")
### two-sided exact (0.0373)
wilcox_test(time ~ group, data = rotarod, distribution = "exact")
### two-sided asymptotical (0.0147)
wilcox_test(time ~ group, data = rotarod)
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)
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)
### the linear statistic, i.e, the contingency table
statistic(ct, type = "linear")
### the same
table(df$x, df$y)
### and the standardized contingency table for illustrating
### departures from the null-hypothesis of independence of x and y
statistic(ct, type = "standardized")
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)
### * <FOOTER>
###
cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
grDevices::dev.off()
###
### Local variables: ***
### mode: outline-minor ***
### outline-regexp: "\\(> \\)?### [*]+" ***
### End: ***
quit('no')
Computing file changes ...
