Description Usage Arguments Details Value Note References Examples

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | ```
## S3 method for class 'formula'
taha_test(formula, data, subset = NULL, weights = NULL, ...)
## S3 method for class 'IndependenceProblem'
taha_test(object, conf.int = FALSE, conf.level = 0.95, ...)
## S3 method for class 'formula'
klotz_test(formula, data, subset = NULL, weights = NULL, ...)
## S3 method for class 'IndependenceProblem'
klotz_test(object, ties.method = c("mid-ranks", "average-scores"),
conf.int = FALSE, conf.level = 0.95, ...)
## S3 method for class 'formula'
mood_test(formula, data, subset = NULL, weights = NULL, ...)
## S3 method for class 'IndependenceProblem'
mood_test(object, ties.method = c("mid-ranks", "average-scores"),
conf.int = FALSE, conf.level = 0.95, ...)
## S3 method for class 'formula'
ansari_test(formula, data, subset = NULL, weights = NULL, ...)
## S3 method for class 'IndependenceProblem'
ansari_test(object, ties.method = c("mid-ranks", "average-scores"),
conf.int = FALSE, conf.level = 0.95, ...)
## S3 method for class 'formula'
fligner_test(formula, data, subset = NULL, weights = NULL, ...)
## S3 method for class 'IndependenceProblem'
fligner_test(object, ties.method = c("mid-ranks", "average-scores"),
conf.int = FALSE, conf.level = 0.95, ...)
## S3 method for class 'formula'
conover_test(formula, data, subset = NULL, weights = NULL, ...)
## S3 method for class 'IndependenceProblem'
conover_test(object, conf.int = FALSE, conf.level = 0.95, ...)
``` |

`formula` |
a formula of the form |

`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 |

`weights` |
an optional formula of the form |

`object` |
an object inheriting from class |

`conf.int` |
a logical indicating whether a confidence interval for the ratio of scales
should be computed. Defaults to |

`conf.level` |
a numeric, confidence level of the interval. Defaults to |

`ties.method` |
a character, the method used to handle ties: the score generating function
either uses mid-ranks ( |

`...` |
further arguments to be passed to |

`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 Hájek, Šidák 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) >= 1*. When
`alternative = "greater"`

, the null hypothesis is *H_0: V(Y_1) / V(Y_2) <= 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.

An object inheriting from class `"IndependenceTest"`

.
Confidence intervals can be extracted by confint.

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.

Bauer, D. F. (1972). Constructing confidence sets using rank statistics.
*Journal of the American Statistical Association* **67**(339),
687–690. doi: 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. doi: 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.
doi: 10.1080/00401706.1981.10487680

Hájek, J., Šidák, 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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | ```
## 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")
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.