penalised.weights

Internal function for calculating penalized weights in conjunction with <code>r.weights</code>.
These weights are used in either the <code>mr_ivw</code> or <code>mr_egger</code> functions when <code>penalized = TRUE</code>, or in the <code>mr_median</code> function when <code>method = "penalized"</code>.

internal

Encodes several methods for performing Mendelian randomization
analyses with summarized data. Summarized data on genetic associations with the
exposure and with the outcome can be obtained from large consortia. These data
can be used for obtaining causal estimates using instrumental variable methods.

Stephen Burgess

MendelianRandomization

Mendelian Randomization Package

Olena Yavorska

James Staley

Fernando Hartwig

Jim Broadbent

Christopher Foley

Andrew Grant

Amy Mason

Ting Ye

Haoran Xue

Zhaotong Lin

Siqi Xu

Ashish Patel

Hyunseung Kang

Sheng Wang

Ville Karhunen

penalised.weights function

<dl><dt>.Bx</dt>
<dd>Beta-coefficient for genetic association with the risk factor.</dd>
<dt>.Bxse</dt>
<dd>Standard error of genetic association with the risk factor.</dd>
<dt>.By</dt>
<dd>Beta-coefficient for genetic association with the outcome.</dd>
<dt>.Byse</dt>
<dd>Standard error of genetic association with the outcome.</dd></dl>

Arguments

Calculates p-values for penalization of weights — penalised.weights

<dl>

<dt>.Bx</dt>
<dd>Beta-coefficient for genetic association with the risk factor.</dd>


<dt>.Bxse</dt>
<dd>Standard error of genetic association with the risk factor.</dd>


<dt>.By</dt>
<dd>Beta-coefficient for genetic association with the outcome.</dd>


<dt>.Byse</dt>
<dd>Standard error of genetic association with the outcome.</dd>

</dl>

Calculates p-values for penalization of weights

penalised.weights: Calculates p-values for penalization of weights

Description

Usage

Value

Arguments

Details

Examples