RankNorm

Applies the rank-based inverse normal transform (INT) to a numeric vector.
The INT can be broken down into a two-step procedure. In the first, the
observations are transformed onto the probability scale using the empirical
cumulative distribution function (ECDF). In the second, the observations are
transformed onto the real line, as Z-scores, using the probit function.

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

RankNorm function

<dl><dt>u</dt>
<dd>Numeric vector.</dd>
<dt>k</dt>
<dd>Offset. Defaults to (3/8), correspond to the Blom transform.</dd></dl>

Arguments

Rank-Normalize — RankNorm

<dl>

<dt>u</dt>
<dd>Numeric vector.</dd>


<dt>k</dt>
<dd>Offset. Defaults to (3/8), correspond to the Blom transform.</dd>

</dl>

RankNorm: Rank-Normalize

Description

Usage

Value

Arguments

See Also

Examples