matIP

Performs estimation and inference on a partially missing target outcome while borrowing information from a correlated surrogate outcome to increase estimation precision and improve power. The target and surrogate outcomes are jointly modeled within a bivariate outcome regression framework. Unobserved values of either outcome are regarded as missing data. Estimation in the presence of bilateral outcome missingness is performed via an expectation conditional maximization algorithm. A flexible association test is provided for evaluating hypotheses about the target regression parameters. See McCaw ZR, Gaynor SM, Sun R, Lin X; “Cross-tissue eQTL mapping in the presence of missing data via surrogate outcome analysis” <doi:10.1101/2020.11.29.403063>.

Zachary McCaw

SurrogateRegression

Surrogate Outcome Regression Analysis

matIP function

<dl><dt>A</dt>
<dd>Numeric matrix.</dd>
<dt>B</dt>
<dd>Numeric matrix.</dd></dl>

Arguments

Matrix Inner Product — matIP

<dl>

<dt>A</dt>
<dd>Numeric matrix.</dd>


<dt>B</dt>
<dd>Numeric matrix.</dd>

</dl>

matIP: Matrix Inner Product

Description

Usage

Value

Arguments