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

Description Usage Arguments Value Author(s) References Examples

Description

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

Usage

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iAWvar.TrimMean(value, group, trim.alpha = 0.25)

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).

trim.alpha

numeric. Indicating proportion of data points to be trimmed from both ends before calculating sample mean.

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 trimmed-mean of value

zbar

sample average of z

Author(s)

Xuan Li <lixuan0759@mathstat.yorku.ca>, Weiliang Qiu <stwxq@channing.harvard.edu>, Yuejiao Fu <yuejiao@mathstat.yorku.ca>, Xiaogang Wang <stevenw@mathstat.yorku.ca>

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

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

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