Description Usage Arguments Details Value Author(s) See Also Examples
View source: R/permutationTest.R
As test statistic the difference between mean scores
from model A and mean scores
from model B is used.
Under the null hypothesis of no difference, the actually observed
difference between mean scores should not be notably different from
the distribution of the test statistic under permutation.
As the computation of all possible permutations is only feasible for
small datasets, a random sample of permutations is used to obtain the
null distribution. The resulting pvalue thus depends on the
.Random.seed
.
1 2  permutationTest(score1, score2, nPermutation = 9999,
plot = FALSE, verbose = FALSE)

score1, score2 
numeric vectors of scores to compare 
nPermutation 
number of random permutations to conduct 
plot 
logical indicating if a 
verbose 
logical indicating if the results should be printed in one line. 
For each permutation, we first randomly assign the membership of the n
individual scores to either model A or B with probability 0.5. We then
compute the respective difference in mean for model A and B in this
permuted set of scores. The Monte Carlo pvalue is then given by
(1 + #permuted differences larger than observed difference (in
absolute value)) / (1 + nPermutation
).
a list of the following elements:
diffObs 
observed difference in mean scores, i.e.,

pVal.permut 
pvalue of the permutation test 
pVal.t 
pvalue of the corresponding

Michaela Paul with contributions by Sebastian Meyer
scores
to obtain individual scores for
oneStepAhead
predictions from a model.
Package coin for a comprehensive permutation test framework,
specifically its function symmetry_test
to compare
paired samples.
1  permutationTest(rnorm(50, 1.5), rnorm(50, 1), plot = TRUE)

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