| 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
IndependenceTest-class. |
... |
additional arguments: method,
a character specifying the type of
adjustment (global, single-step, step-down or discrete)
should be used. The default is global. |
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.
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")