ScaleTests

From coin v1.3-1 by Torsten Hothorn

Two- and \(K\)-Sample Scale Tests

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

Keywords: htest

Usage

# S3 method for formula
taha_test(formula, data, subset = NULL, weights = NULL, …)
# S3 method for IndependenceProblem
taha_test(object, conf.int = FALSE, conf.level = 0.95, …)
# S3 method for formula
klotz_test(formula, data, subset = NULL, weights = NULL, …)
# S3 method for IndependenceProblem
klotz_test(object, ties.method = c("mid-ranks", "average-scores"),
           conf.int = FALSE, conf.level = 0.95, …)
# S3 method for formula
mood_test(formula, data, subset = NULL, weights = NULL, …)
# S3 method for IndependenceProblem
mood_test(object, ties.method = c("mid-ranks", "average-scores"),
          conf.int = FALSE, conf.level = 0.95, …)
# S3 method for formula
ansari_test(formula, data, subset = NULL, weights = NULL, …)
# S3 method for IndependenceProblem
ansari_test(object, ties.method = c("mid-ranks", "average-scores"),
            conf.int = FALSE, conf.level = 0.95, …)
# S3 method for formula
fligner_test(formula, data, subset = NULL, weights = NULL, …)
# S3 method for IndependenceProblem
fligner_test(object, ties.method = c("mid-ranks", "average-scores"),
             conf.int = FALSE, conf.level = 0.95, …)
# S3 method for formula
conover_test(formula, data, subset = NULL, weights = NULL, …)
# S3 method for IndependenceProblem
conover_test(object, conf.int = FALSE, conf.level = 0.95, …)

Arguments

formula: a formula of the form y ~ x | block where y is a numeric variable, x is a factor and block is an optional factor for stratification.
data: an optional data frame containing the variables in the model formula.
subset: an optional vector specifying a subset of observations to be used. Defaults to NULL.
weights: an optional formula of the form ~ w defining integer valued case weights for each observation. Defaults to NULL, implying equal weight for all observations.
object: an object inheriting from class "'>IndependenceProblem".
conf.int: a logical indicating whether a confidence interval for the ratio of scales should be computed. Defaults to FALSE.
conf.level: a numeric, confidence level of the interval. Defaults to 0.95.
ties.method: a character, the method used to handle ties: the score generating function either uses mid-ranks ("mid-ranks", default) or averages the scores of randomly broken ties ("average-scores").
…: further arguments to be passed to independence_test.

Details

taha_test, klotz_test, mood_test, ansari_test, fligner_test and conover_test provide the Taha test, the Klotz test, the Mood test, the Ansari-Bradley test, the Fligner-Killeen test and the Conover-Iman test. A general description of these methods is given by Hollander and Wolfe (1999). For the adjustment of scores for tied values see H<U+00E1>jek, <U+0160>id<U+00E1>k and Sen (1999, pp. 133--135).

The null hypothesis of equality, or conditional equality given block, of the distribution of y in the groups defined by x is tested against scale alternatives. In the two-sample case, the two-sided null hypothesis is \(H_0\!: V(Y_1) / V(Y_2) = 1\), where \(V(Y_s)\) is the variance of the responses in the \(s\)th sample. In case alternative = "less", the null hypothesis is \(H_0\!: V(Y_1) / V(Y_2) \ge 1\). When alternative = "greater", the null hypothesis is \(H_0\!: V(Y_1) / V(Y_2) \le 1\). Confidence intervals for the ratio of scales are available and computed according to Bauer (1972).

The Fligner-Killeen test uses median centering in each of the samples, as suggested by Conover, Johnson and Johnson (1981), whereas the Conover-Iman test, following Conover and Iman (1978), uses mean centering in each of the samples.

The conditional null distribution of the test statistic is used to obtain \(p\)-values and an asymptotic approximation of the exact distribution is used by default (distribution = "asymptotic"). Alternatively, the distribution can be approximated via Monte Carlo resampling or computed exactly for univariate two-sample problems by setting distribution to "approximate" or "exact" respectively. See asymptotic, approximate and exact for details.

Value

An object inheriting from class "'>IndependenceTest". Confidence intervals can be extracted by confint.

Note

In the two-sample case, a large value of the Ansari-Bradley statistic indicates that sample 1 is less variable than sample 2, whereas a large value of the statistics due to Taha, Klotz, Mood, Fligner-Killeen, and Conover-Iman indicate that sample 1 is more variable than sample 2.

References

Bauer, D. F. (1972). Constructing confidence sets using rank statistics. Journal of the American Statistical Association 67(339), 687--690. 10.1080/01621459.1972.10481279

Conover, W. J. and Iman, R. L. (1978). Some exact tables for the squared ranks test. Communications in Statistics -- Simulation and Computation 7(5), 491--513. 10.1080/03610917808812093

Conover, W. J., Johnson, M. E. and Johnson, M. M. (1981). A comparative study of tests for homogeneity of variances, with applications to the outer continental shelf bidding data. Technometrics 23(4), 351--361. 10.1080/00401706.1981.10487680

H<U+00E1>jek, J., <U+0160>id<U+00E1>k, Z. and Sen, P. K. (1999). Theory of Rank Tests, Second Edition. San Diego: Academic Press.

Hollander, M. and Wolfe, D. A. (1999). Nonparametric Statistical Methods, Second Edition. York: John Wiley & Sons.

Examples

# NOT RUN {
## Serum Iron Determination Using Hyland Control Sera
## Hollander and Wolfe (1999, p. 147, Tab 5.1)
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 = gl(2, 20, labels = c("Ramsay", "Jung-Parekh"))
)

## Asymptotic Ansari-Bradley test
ansari_test(serum ~ method, data = sid)

## Exact Ansari-Bradley test
pvalue(ansari_test(serum ~ method, data = sid,
                   distribution = "exact"))


## Platelet Counts of Newborn Infants
## Hollander and Wolfe (1999, p. 171, Tab. 5.4)
platelet <- data.frame(
    counts = c(120, 124, 215, 90, 67, 95, 190, 180, 135, 399,
               12, 20, 112, 32, 60, 40),
    treatment = factor(rep(c("Prednisone", "Control"), c(10, 6)))
)

## Approximative (Monte Carlo) Lepage test
## Hollander and Wolfe (1999, p. 172)
lepage_trafo <- function(y)
    cbind("Location" = rank_trafo(y), "Scale" = ansari_trafo(y))

independence_test(counts ~ treatment, data = platelet,
                  distribution = approximate(nresample = 10000),
                  ytrafo = function(data)
                      trafo(data, numeric_trafo = lepage_trafo),
                  teststat = "quadratic")

## Why was the null hypothesis rejected?
## Note: maximum statistic instead of quadratic form
ltm <- independence_test(counts ~ treatment, data = platelet,
                         distribution = approximate(nresample = 10000),
                         ytrafo = function(data)
                             trafo(data, numeric_trafo = lepage_trafo))

## Step-down adjustment suggests a difference in location
pvalue(ltm, method = "step-down")

## The same results are obtained from the simple Sidak-Holm procedure since the
## correlation between Wilcoxon and Ansari-Bradley test statistics is zero
cov2cor(covariance(ltm))
pvalue(ltm, method = "step-down", distribution = "marginal", type = "Sidak")
# }

Documentation reproduced from package coin, version 1.3-1, License: GPL-2

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