permF.mp: Permutation F-tests for massively parallel linear models

In vows: Voxelwise Semiparametrics

Description

Performs permutation F-tests for parallel linear models with a common design matrix. Currently restricted to testing with the intercept-only model as the null hypothesis. The permutation method controls the familywise error rate (FWER) at a desired level; see Details.

Usage

permF.mp(formula, nperm = 499, alpha = 0.05, report.every = 50)

Arguments

formula	a formula such as "Y ~ X", where Y is an n \times V response matrix and X is an n \times p design matrix common to all V models.
nperm	number of permutations.
alpha	level at which to control the FWER.
report.every	parameter controlling how often to report the number of permutations performed; by default, every 50.

Details

The observed F-statistics are referred to a permutation distribution of the maximum F-statistic over all V tests. This is a standard approach to FWER control in neuroimaging (Nichols and Holmes, 2001).

Value

maxF.perm	maximal F-statistics obtained from each of the permuted data sets.
F.obs	the observed F-statistics.
threshold	critical value obtained from the permutations.
pvalue	adjusted (familywise error rate-controlling) p-values.

Author(s)

Philip Reiss [e-mail] and Lei Huang [e-mail]

References

Nichols, T. E., and Holmes, A. P. (2001). Nonparametric permutation tests for functional neuroimaging: a primer with examples. Human Brain Mapping, 15, 1–25.

See Also

F.mp

Examples

Y = matrix(rnorm(6000), nrow=20)
X = rnorm(20)
t3 = permF.mp(Y~X)

vows documentation built on May 2, 2019, 9:26 a.m.