calc_dens_ind_multiple

Calculate the density of K-dimensional multivariate normal (all dimensions are independent) under fitted acsGmm.

Implements the conditionally symmetric multidimensional Gaussian mixture model (csmGmm) for large-scale testing of composite null hypotheses in genetic association applications such as mediation analysis, pleiotropy analysis, and replication analysis. In such analyses, we typically have J sets of K test statistics where K is a small number (e.g. 2 or 3) and J is large (e.g. 1 million). For each one of the J sets, we want to know if we can reject all K individual nulls. Please see the vignette for a quickstart guide. The paper describing these methods is "Testing a Large Number of Composite Null Hypotheses Using Conditionally Symmetric Multidimensional Gaussian Mixtures in Genome-Wide Studies" by Sun R, McCaw Z, & Lin X (2024, <doi:10.1080/01621459.2024.2422124>). The paper is accepted and published online (but not yet in print) in the Journal of the American Statistical Association as of Dec 1 2024.

Ryan Sun

csmGmm

Conditionally Symmetric Multidimensional Gaussian Mixture Model

calc_dens_ind_multiple function

<dl><dt>x</dt>
<dd>K*1 vector of means.</dd>
<dt>Zmat</dt>
<dd>J*K matrix of test statistics.</dd></dl>

Arguments

Calculate the density of K-dimensional multivariate normal (all dimensions are independent) under fitted acsGmm. — calc_dens_ind_multiple

<dl>

<dt>x</dt>
<dd>K*1 vector of means.</dd>


<dt>Zmat</dt>
<dd>J*K matrix of test statistics.</dd>

</dl>

calc_dens_ind_multiple: Calculate the density of K-dimensional multivariate normal (all dimensions are independent) under fitted acsGmm.

Description

Usage

Value

Arguments

Examples