iAWvar.BF: Test for equality of variance based on improved Ahn and...

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

Description

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

Usage

iAWvar.BF(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	pvalue of the score test
z	absolute deviation of value from median of 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

Li X, Qiu W, Morrow J, DeMeo DL, Weiss ST, Fu Y, Wang X. (2015) A Comparative Study of Tests for Homogeneity of Variances with Application to DNA Methylation Data. PLoS ONE 10(12): e0145295. PMID: 26683022

Qiu W, Li X, Morrow J, DeMeo DL, Weiss ST, Wang X, Fu Y. New Score Tests for Equality of Variances in the Application of DNA Methylation Data Analysis [Version 2]. Insights Genet Genomics. (2017) 1: 3.2

Li X, Qiu W, Fu Y, Wang X. (2017) Robust Joint Score Tests in the Application of DNA Methylation Data Analysis. In submission.

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

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