bnr-class

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

bnr-class function

<dl>
<dt><code>Covariance</code></dt>
<dd>Residual covariance matrix.</dd></dl>
<dt><code>Covariance.info</code></dt>
<dd>Information for covariance parameters.</dd>
<dt><code>Covariance.tab</code></dt>
<dd>Table of covariance parameters.</dd>
<dt><code>Regression.info</code></dt>
<dd>Information for regression coefficients.</dd>
<dt><code>Regression.tab</code></dt>
<dd>Table of regression coefficients.</dd>
<dt><code>Residuals</code></dt>
<dd>Outcome residuals.</dd>

Slots

Bivariate Regression Model — bnr-class

<dl>
<dt><code>Covariance</code></dt>
<dd>Residual covariance matrix.</dd>


<dt><code>Covariance.info</code></dt>
<dd>Information for covariance parameters.</dd>


<dt><code>Covariance.tab</code></dt>
<dd>Table of covariance parameters.</dd>


<dt><code>Regression.info</code></dt>
<dd>Information for regression coefficients.</dd>


<dt><code>Regression.tab</code></dt>
<dd>Table of regression coefficients.</dd>


<dt><code>Residuals</code></dt>
<dd>Outcome residuals.</dd>


</dl>

bnr-class: Bivariate Regression Model

Description

Arguments

Slots