CoreGx (version 0.1.1)

cosinePerm: Computes the cosine similarity and significance using permutation test

Description

Computes the cosine similarity and significance using permutation test

Usage

cosinePerm(x, y, nperm = 1000, alternative = c("two.sided", "less",
  "greater"), include.perm = FALSE, setseed = 12345, nthread = 1)

Arguments

x

[factor] is the factors for the first variable

y

[factor] is the factors for the second variable

nperm

[integer] is the number of permutations to comput ethe null distribution of MCC estimates

alternative

[string] indicates the alternative hypothesis and must be one of <U+2018>"two.sided"<U+2019>, <U+2018>"greater"<U+2019> or <U+2018>"less"<U+2019>. You can specify just the initial letter. <U+2018>"greater"<U+2019> corresponds to positive association, <U+2018>"less"<U+2019> to negative association. Options are "two.sided", "less", or "greater"

include.perm

[boolean] indicates whether the estimates for the null distribution should be returned. Default set to 'FALSE'

setseed

[integer] is the seed specified by the user. Defaults is '12345'

nthread

[integer] is the number of threads to be used to perform the permutations in parallel

Value

[list] estimate of the cosine similarity, p-value and estimates after random permutations (null distribution) in include.perm is set to 'TRUE'

Examples

# NOT RUN {
x <- factor(c(1,2,1,2,1))
y <- factor(c(2,2,1,1,1))
cosinePerm(x, y)

# }