pvalue-methods {coin} | R Documentation |
Extracts the p-value from objects representing null distributions of independence tests.
pvalue(object, ...)
object |
an object inheriting from class
|
... |
additional arguments: |
Univariate p-values for maximum-type statistics come with associated 99%
confidence interval when resampling was used to determine the null
distribution (which may be the case even when distribution =
"asypmtotic"
was used).
By default, a global p-value is returned. When method =
"single-step"
, adjusted p-values are obtained from a
single-step max-T procedure
(Westfall & Young, 1993, algorithm 2.5 and formula 2.8). Note that the
minimum of the adjusted p-values always controls the familywise error
rate (FWER) but the maximum type I error, i.e. the error for
each of the individual tests, is only controlled when the subset
pivotality condition holds.
When method = "step-down"
the free step-down resampling method
(algorithm 2.8 and formula 2.8 in Westfall & Young, 1993) is used, the above
comments apply as well.
With method = "discrete"
, the Bonferroni adjustment as suggested by
Westfall & Wolfinger (1997) with improvements for highly discrete
permutation distributions is available, however, without taking
correlations between the test statistics into account. Here, the p-values are
valid even without assuming subset pivotality.
extracts the p-value from the specified object.
Peter H. Westfall \& S. Stanley Young (1993). Resampling-based Multiple Testing. New York: John Wiley & Sons.
Peter H. Westfall \& Russell D. Wolfinger (1997). Multiple tests with discrete distributions. The American Statistician 51, 3–8.
### 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) ### bivariate 2-sample problem df <- data.frame(y1 = rnorm(20) + c(rep(0, 10), rep(1, 10)), y2 = rnorm(20), x = gl(2, 10)) it <- independence_test(y1 + y2 ~ x, data = df, distribution = approximate(B = 9999)) pvalue(it, method = "single-step") pvalue(it, method = "step-down") pvalue(it, method = "discrete")