Package: swfdr 1.39.0

Simina M. Boca

swfdr: Estimation of the science-wise false discovery rate and the false discovery rate conditional on covariates

This package allows users to estimate the science-wise false discovery rate from Jager and Leek, "Empirical estimates suggest most published medical research is true," 2013, Biostatistics, using an EM approach due to the presence of rounding and censoring. It also allows users to estimate the false discovery rate conditional on covariates, using a regression framework, as per Boca and Leek, "A direct approach to estimating false discovery rates conditional on covariates," 2018, PeerJ.

Authors:Jeffrey T. Leek, Leah Jager, Simina M. Boca, Tomasz Konopka

swfdr_1.39.0.tar.gz
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swfdr_1.39.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
swfdr/json (API)

# Install 'swfdr' in R:
install.packages('swfdr', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/leekgroup/swfdr/issues

Datasets:
  • BMI_GIANT_GWAS_sample - Subset of SNPs from meta-analysis of BMI GWAS study.
  • journals_pVals - P-values from abstracts from articles in 5 biomedical journals (American Journal of Epidemiology, BMJ, JAMA, Lancet, New England Journal of Medicine), over 11 years (2000-2010).

On BioConductor:swfdr-1.39.0(bioc 3.24)swfdr-1.38.0(bioc 3.23)

multiplecomparisonstatisticalmethodsoftware

6.45 score 3 stars 59 scripts 426 downloads 2 mentions 3 exports 0 dependencies

Last updated from:658066d6ab. Checks:1 ERROR, 7 WARNING, 2 OK. Indexed: yes.

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Exports:calculateSwfdrlm_pi0lm_qvalue

Dependencies:

Computing q-values conditioned on covariates using swfdr
Introduction | Installation | q-values without covariates | Conceptual datasets | pi0 | q-value | Conditioning on covariates | Conditioned pi0 | Conditioned q-values | Conditioning on multiple covariates | Conditioned pi0s with multiple covariates | Conditioned q-values with multiple covariates | Comparison with qvalue | Discussion | Technical notes | Modeling resolution (argument lambda) | Covariate matrix (argument X) | Model type (argument type) | Thresholding (argument threshold) | Smoothing (argument smoothing) | Smoothing degrees of freedom (argument smooth.df) | References
Rendered fromswfdrQ.Rmdusingknitr::rmarkdown

Last update: 2021-01-05
Started: 2019-04-22

Tutorial for swfdr package
Package overview | Estimating the science-wise false discovery rate | Example: Estimate the swfdr based on p-values from biomedical journals | The calculateSwfdr function | Results from example dataset | Estimating the proportion of true null hypothesis in the presence of covariates | Example: Adjust for sample size and allele frequency in BMI GWAS meta-analysis | The lm_pi0 function | Results from BMI GWAS meta-analysis example | References
Rendered fromswfdrTutorial.Rmdusingknitr::rmarkdown

Last update: 2019-04-22
Started: 2016-10-20