Package: SUMnlmr 0.0.0.9000

Amy Mason

SUMnlmr: Non-Linear Mendelian Randomization On Partially Summarized Data

Runs non-linear MR calculations on partly-summarized data.

Authors:Amy Mason [aut, cre], Stephen Burgess [aut], James Staley [aut], Matt Arnold [aut]

SUMnlmr_0.0.0.9000.tar.gz
SUMnlmr_0.0.0.9000.zip(r-4.5)SUMnlmr_0.0.0.9000.zip(r-4.4)SUMnlmr_0.0.0.9000.zip(r-4.3)
SUMnlmr_0.0.0.9000.tgz(r-4.4-any)SUMnlmr_0.0.0.9000.tgz(r-4.3-any)
SUMnlmr_0.0.0.9000.tar.gz(r-4.5-noble)SUMnlmr_0.0.0.9000.tar.gz(r-4.4-noble)
SUMnlmr_0.0.0.9000.tgz(r-4.4-emscripten)SUMnlmr_0.0.0.9000.tgz(r-4.3-emscripten)
SUMnlmr.pdf |SUMnlmr.html✨
SUMnlmr/json (API)

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

Peer review:

Bug tracker:https://github.com/amymariemason/sumnlmr/issues

Datasets:
  • LDL_CAD - UK Biobank dataset containing the allele score-associations with ldl-cholesterol and CAD diagnosis, by 10 quantiles of residual ldl.
  • LDL_CAD_covar - UK Biobank dataset containing the allele score-associations with ldl-cholesterol and CAD diagnosis adjusted for age, sex and first 10 principle components, by 10 quantiles of residual ldl.
  • bmi_acm - Hunt dataset containing the allele score-associations with bmi and all cause mortality, by 100 quantiles of residual BMI. As used in the paper "Body mass index and all cause mortality in HUNT and UK Biobank studies: linear and non-linear mendelian randomisation analyses" https://doi.org/10.1136/bmj.l1042
  • generated_data - Artificial genetic & phenotype data, for purposes of package tests

On CRAN:

3.40 score 21 stars 12 scripts 12 exports 39 dependencies

Last updated 6 months agofrom:bc2ed34922. Checks:OK: 3 NOTE: 4. Indexed: no.

TargetResultDate
Doc / VignettesOKOct 26 2024
R-4.5-winNOTEOct 26 2024
R-4.5-linuxNOTEOct 26 2024
R-4.4-winNOTEOct 26 2024
R-4.4-macNOTEOct 26 2024
R-4.3-winOKOct 26 2024
R-4.3-macOKOct 26 2024

Exports:create_ind_datacreate_nlmr_summarycreate_summary_datafrac_poly_summ_mrgetGRvalueshamardman.prodiv_freelowfunpiecewise_summ_figurepiecewise_summ_mrreprowupfun

Dependencies:assertthatclicolorspacedplyrfansifarvergenericsggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSmathjaxrMatrixmatrixStatsmetadatmetaformgcvmunsellnlmenumDerivpbapplypillarpkgconfigR6RColorBrewerrlangscalessurvivaltibbletidyselectutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Hunt dataset containing the allele score-associations with bmi and all cause mortality, by 100 quantiles of residual BMI. As used in the paper "Body mass index and all cause mortality in HUNT and UK Biobank studies: linear and non-linear mendelian randomisation analyses" https://doi.org/10.1136/bmj.l1042bmi_acm
Generation of individual level datacreate_ind_data
Creation of summarised mendelian randomisation local estimatescreate_nlmr_summary
generation of summary level datacreate_summary_data
Instrumental variable analysis using fractional polynomials based on summary datafrac_poly_summ_mr
Artificial genetic & phenotype data, for purposes of package testsgenerated_data
Gelman-Rubin (GR) StatisticsgetGRvalues
Hamardman producthamardman.prod
IV-free exposureiv_free
UK Biobank dataset containing the allele score-associations with ldl-cholesterol and CAD diagnosis, by 10 quantiles of residual ldl.LDL_CAD
UK Biobank dataset containing the allele score-associations with ldl-cholesterol and CAD diagnosis adjusted for age, sex and first 10 principle components, by 10 quantiles of residual ldl.LDL_CAD_covar
Lower Coefficient valuelowfun
Piecewise linear figurepiecewise_summ_figure
Instrumental variable analysis using piecewise linear method based on summary datapiecewise_summ_mr
Create plot of Fractional Polynomial Fitprint.frac_poly_mr
Print Summary Fractional Polynomial Fitsprint.summary.frac_poly_mr
Print summary of piecewise linear fitsprint.summary.piecewise_summ_mr
Repeat rowsreprow
Summarizing Fractional Polynomial Fitssummary.frac_poly_mr
Summary of piecewise linear fitssummary.piecewise_summ_mr
Upper Coefficient valueupfun