# LocationTests

0th

Percentile

##### Independent Two- and K-Sample Location Tests

Testing the equality of the distributions of a numeric response in two or more independent groups against shift alternatives.

Keywords
htest
##### Usage
## S3 method for class 'formula':
oneway_test(formula, data, subset = NULL, weights = NULL, \dots)
## S3 method for class 'IndependenceProblem':
oneway_test(object, ...)## S3 method for class 'formula':
wilcox_test(formula, data, subset = NULL, weights = NULL, \dots)
## S3 method for class 'IndependenceProblem':
wilcox_test(object,
conf.int = FALSE, conf.level = 0.95, ...)## S3 method for class 'formula':
normal_test(formula, data, subset = NULL, weights = NULL, \dots)
## S3 method for class 'IndependenceProblem':
normal_test(object,
ties.method = c("mid-ranks", "average-scores"),
conf.int = FALSE, conf.level = 0.95, ...)## S3 method for class 'formula':
median_test(formula, data, subset = NULL, weights = NULL, \dots)
## S3 method for class 'IndependenceProblem':
median_test(object,
conf.int = FALSE, conf.level = 0.95, ...)## S3 method for class 'formula':
kruskal_test(formula, data, subset = NULL, weights = NULL, \dots)
## S3 method for class 'IndependenceProblem':
kruskal_test(object,
distribution = c("asymptotic", "approximate"), ...)
##### Details

The null hypothesis of the equality of the distribution of y in the groups given by x is tested. In particular, the methods documented here are designed to detect shift alternatives. For a general description of the test procedures documented here we refer to Hollander & Wolfe (1999).

The test procedures apply a rank transformation to the response values y, except of oneway_test which computes a test statistic using the untransformed response values.

The asymptotic null distribution is computed by default for all procedures. Exact p-values may be computed for the two-sample problems and can be approximated via Monte-Carlo resampling for all procedures. Exact p-values are computed either by the shift algorithm (Streitberg & R"ohmel, 1986, 1987) or by the split-up algorithm (van de Wiel, 2001).

The linear rank tests for two samples (wilcox_test, normal_test and median_test) can be used to test the two-sided hypothesis $H_0: Y_1 - Y_2 = 0$, where $Y_i$ is the median of the responses in the ith group. Confidence intervals for the difference in location are available for the rank-based procedures and are computed according to Bauer (1972). In case alternative = "less", the null hypothesis $H_0: Y_1 - Y_2 \ge 0$ is tested and alternative = "greater" corresponds to a null hypothesis $H_0: Y_1 - Y_2 \le 0$.

In case x is an ordered factor, kruskal_test computes the linear-by-linear association test for ordered alternatives.

For the adjustment of scores for tied values see Hajek, Sidak and Sen (1999), page 131ff.

##### Value

• An object inheriting from class IndependenceTest-class with methods show, statistic, expectation, covariance and pvalue. The null distribution can be inspected by pperm, dperm, qperm and support methods. Confidence intervals can be extracted by confint.

##### References

Myles Hollander & Douglas A. Wolfe (1999). Nonparametric Statistical Methods, 2nd Edition. New York: John Wiley & Sons.

Bernd Streitberg & Joachim R"ohmel (1986). Exact distributions for permutations and rank tests: An introduction to some recently published algorithms. Statistical Software Newsletter 12(1), 10--17.

Bernd Streitberg & Joachim R"ohmel (1987). Exakte Verteilungen f"ur Rang- und Randomisierungstests im allgemeinen $c$-Stichprobenfall. EDV in Medizin und Biologie 18(1), 12--19.

Mark A. van de Wiel (2001). The split-up algorithm: a fast symbolic method for computing p-values of rank statistics. Computational Statistics 16, 519--538.

David F. Bauer (1972). Constructing confidence sets using rank statistics. Journal of the American Statistical Association 67, 687--690.

Jaroslav Hajek, Zbynek Sidak & Pranab K. Sen (1999), Theory of Rank Tests. San Diego, London: Academic Press.

##### 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 standardized
### 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
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 = "max", 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"))
}
Documentation reproduced from package coin, version 1.0-7, License: GPL-2

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