# 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

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

## 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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16``` ``` # 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.