PartitionData

Partition y and X according to the missingness pattern of g.

Inverse normal transformation (INT) based genetic association testing. These tests are recommend for continuous traits with non-normally distributed residuals. INT-based tests robustly control the type I error in settings where standard linear regression does not, as when the residual distribution exhibits excess skew or kurtosis. Moreover, INT-based tests dominate standard linear regression in terms of power. These tests may be classified into two types. In direct INT (D-INT), the phenotype is itself transformed. In indirect INT (I-INT), phenotypic residuals are transformed. The omnibus test (O-INT) adaptively combines D-INT and I-INT into a single robust and statistically powerful approach. See McCaw ZR, Lane JM, Saxena R, Redline S, Lin X. "Operating characteristics of the rank-based inverse normal transformation for quantitative trait analysis in genome-wide association studies" <doi:10.1111/biom.13214>.

Zachary McCaw

RNOmni

Rank Normal Transformation Omnibus Test

PartitionData function

<dl><dt>e</dt>
<dd>Numeric residual vector.</dd>
<dt>g</dt>
<dd>Genotype vector.</dd>
<dt>X</dt>
<dd>Model matrix of covariates.</dd></dl>

Arguments

Partition Data — PartitionData

<dl>

<dt>e</dt>
<dd>Numeric residual vector.</dd>


<dt>g</dt>
<dd>Genotype vector.</dd>


<dt>X</dt>
<dd>Model matrix of covariates.</dd>

</dl>

PartitionData: Partition Data

Description

Usage

Value

Arguments