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Corrigendum: Marginal Effects in Interaction Models: Determining and Controlling the False Positive Rate

NIAID Data Ecosystem2026-03-12 收录

交互模型分析

假阳性率控制

数据链接：

https://doi.org/10.7910/DVN/HEVGTW 数据链接链接失效反馈

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资源简介：

version of original article with errors indicated via strikethrough

应用场景：

创建时间：

2020-09-01

遗传表型分析

假阳性率控制

Shown are FPRs under the null hypothesis of no phenotypic effect, estimated by resampling controls for various workflows, for 70 traits from five assays from the WTSI Mouse Genetics Project (MGP) Sele

NIAID Data Ecosystem70

False positive rates for association studies when variant identification is carried out in only cases or in both cases and controls for gene(s) with a fixed number of neutral variants.

遗传关联研究

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Results are shown for gene(s) with M = 10, 20, and 30 neutral variants with equal population frequencies of 0.001, 0.002, and 0.005, for N = 100, 200, 500, and 1,000 cases, and an equal number of cont

Figshare2015-12-02 更新20

Marginal Effects in Interaction Models: Determining and Controlling the False Positive Rate

交互模型边际效应

假阳性率控制

replication files and corrections of error

Harvard Dataverse2020-02-28 更新10

False Positive Rates for simulated data for various workflows.

遗传关联分析

假阳性率控制

Shown are FPRs under the null hypothesis of no phenotypic effect, estimated by resampling simulated controls. The y-axis’s show box-and-whisker plots of the distribution of the FPR, defined as the fra

NIAID Data Ecosystem30

Picked Protein Group FDR - Examples and System tests

蛋白质组学分析

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Example datasets and reference results for system tests for the Picked Group FDR package (https://github.com/kusterlab/picked_group_fdr). Check out the GitHub repository for the ReadMe files on how to

Zenodo2025-04-16 更新20

Corrigendum: Marginal Effects in Interaction Models: Determining and Controlling the False Positive Rate

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