AWvarTest: Test for equality of variance based on Ahn and Wang's (2013)...

In diffMeanVar: Detecting Gene Probes with Different Means or Variances Between Two Groups

Description

Test for equality of variance based on Ahn and Wang's (2013) score test.

Usage

AWvarTest(value, group)

Arguments

value	numeric. Measurements to be compared between two groups.
group	numeric. Subject's group membership. Must be binary (i.e., taking values 0 or 1).

Value

A list with 6 elements:

U2	score statistic
varU2	estimated variance of the score statistic
T2	score statistic U2^2/varU2
pval	p-value of the score test
z	squared deviation of value from mean value
zbar	sample average of z

Author(s)

Xuan Li <[e-mail]>, Weiliang Qiu <[e-mail]>, Yuejiao Fu <[e-mail]>, Xiaogang Wang <[e-mail]>

References

Ahn S. and Wang T. (2013) A Powerful Statistical Method for Indentifying Differentially Methylated Markers in Complex Diseases. Pacific Symposium on Biocomputing. 69-79.

Examples

    # generate simulated data set from t distribution
    set.seed(1234567)
    es.sim = genSimData.tDistr(nCpGs = 100, nCases = 20, nControls = 20,
      df0 = 10, ncp0 = 0, df1 = 6, ncp1 = 2.393, testPara = "var",
      eps = 1.0e-3, applier = lapply) 
    print(es.sim)
    print(exprs(es.sim)[1:2,1:3])

    # do AW score test for the first probe
    dat = exprs(es.sim)
    pDat = pData(es.sim)
    print(pDat[1:2,])

    res = AWvarTest(value = dat[1,], group = pDat$memSubj)
    print(names(res))
    print(res)

diffMeanVar documentation built on May 2, 2019, 2:54 a.m.