Package: MUVR2 0.1.0

Yingxiao Yan

MUVR2: Multivariate Methods with Unbiased Variable Selection

Predictive multivariate modelling for metabolomics. Types: Classification and regression. Methods: Partial Least Squares, Random Forest ans Elastic Net Data structures: Paired and unpaired Validation: repeated double cross-validation (Westerhuis et al. (2008)<doi:10.1007/s11306-007-0099-6>, Filzmoser et al. (2009)<doi:10.1002/cem.1225>) Variable selection: Performed internally, through tuning in the inner cross-validation loop.

Authors:Carl Brunius [aut], Yingxiao Yan [aut, cre]

MUVR2_0.1.0.tar.gz
MUVR2_0.1.0.zip(r-4.5)MUVR2_0.1.0.zip(r-4.4)MUVR2_0.1.0.zip(r-4.3)
MUVR2_0.1.0.tgz(r-4.4-any)MUVR2_0.1.0.tgz(r-4.3-any)
MUVR2_0.1.0.tar.gz(r-4.5-noble)MUVR2_0.1.0.tar.gz(r-4.4-noble)
MUVR2_0.1.0.tgz(r-4.4-emscripten)MUVR2_0.1.0.tgz(r-4.3-emscripten)
MUVR2.pdf |MUVR2.html✨
MUVR2/json (API)

# Install 'MUVR2' in R:
install.packages('MUVR2', repos = c('https://metabocomp.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/metabocomp/muvr2/issues

Datasets:
  • IDR - Subject identifiers for the rye metabolomics regression tutorial
  • IDR2 - Subject identifiers for the rye metabolomics regression tutorial, using unique individuals
  • XRVIP - Metabolomics data for the rye metabolomics regression tutorial
  • XRVIP2 - Metabolomics data for the rye metabolomics regression tutorial, using unique individuals
  • Xotu - Microbiota composition in mosquitos for the classification tutorial
  • YR - Rye consumption for the rye metabolomics regression tutorial
  • YR2 - Rye consumption for the rye metabolomics regression tutorial, using unique individuals
  • Yotu - Village of capture of mosquitos for the classification tutorial
  • crispEM - Effect matrix for the crisp multilevel tutorial

On CRAN:

3.81 score 1 stars 1 scripts 159 downloads 33 exports 83 dependencies

Last updated 2 months agofrom:861ac7bdd7. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 17 2024
R-4.5-winOKNov 17 2024
R-4.5-linuxOKNov 17 2024
R-4.4-winOKNov 17 2024
R-4.4-macOKNov 17 2024
R-4.3-winOKNov 17 2024
R-4.3-macOKNov 17 2024

Exports:biplotPLScheckinputconfusionMatrixcustomParamsget_rmsepgetBERgetMISSgetVargetVIRankH0_referenceH0_testmergeModelsMUVR2MUVR2_ENnearZeroVaronehotencodingpermutationPlotplotMVplotPCAplotPermplotPredplotStabilityplotVALplotVIRankpPermpredMVpreProcessQ2_calculationqMUVR2rdCVrdcvNetParamssampling_from_distributionvarClass

Dependencies:caretclasscliclockcodetoolscolorspacecpp11data.tablediagramdigestdoParalleldplyre1071fansifarverforeachfuturefuture.applygenericsggplot2glmnetglobalsgluegowerGPArotationgtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmgcvmnormtModelMetricsmunsellnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypsychpurrrR6randomForestrangerRColorBrewerRcppRcppEigenrecipesreshape2rlangrpartscalesshapeSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetzdbutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
PLS biplotbiplotPLS
Check inputcheckinput
Confusion matrixconfusionMatrix
Effect matrix for the crisp multilevel tutorialcrispEM
Make custom parameters for internal modellingcustomParams
Get RMSEPget_rmsep
Get BERgetBER
Get number of misclassificationsgetMISS
Get min, mid or max model from Elastic Net modellinggetVar
Get variable importancegetVIRank
Get reference distribution for resampling testsH0_reference
Perform permutation or resampling testsH0_test
Subject identifiers for the rye metabolomics regression tutorialIDR
Subject identifiers for the rye metabolomics regression tutorial, using unique individualsIDR2
Merge two MUVR class objectsmergeModels
MUVR2 with PLS and RFMUVR2
MUVR2 with ENMUVR2_EN
Identify variables with near zero variancenearZeroVar
One hot encodingonehotencoding
Plot permutation analysispermutationPlot
Plot predictionsplotMV
PCA score plotplotPCA
Plot for comparison of actual model fitness vs permutation/resamplingplotPerm
Plot predictions for PLS regressionplotPred
Plot stabilityplotStability
Plot validation metricplotVAL
Plot variable importance rankingplotVIRank
Calculate permutation p-value Calculate perutation p-value of actual model performance vs null hypothesis distribution. `pPerm` will calculate the cumulative (1-tailed) probability of `actual` belonging to `permutation_distribution`. `side` is guessed by actual value compared to median(permutation_distribution). Test is performed on original data OR ranked for non-parametric statistics.pPerm
Predict outcomes Predict MV object using a MUVR class object and a X testing set. At present, this function only supports predictions for PLS regression type problems.predMV
Perform matrix pre-processingpreProcess
Q2 calculationQ2_calculation
Wrapper for speedy access to MUVR2 (autosetup of parallelization)qMUVR2
Wrapper for repeated double cross-validation without variable selectionrdCV
Make custom parameters for rdcvNet internal modellingrdcvNetParams
Sampling from the distribution of somethingsampling_from_distribution
Report variables belonging to different classesvarClass
Microbiota composition in mosquitos for the classification tutorialXotu
Metabolomics data for the rye metabolomics regression tutorialXRVIP
Metabolomics data for the rye metabolomics regression tutorial, using unique individualsXRVIP2
Village of capture of mosquitos for the classification tutorialYotu
Rye consumption for the rye metabolomics regression tutorialYR
Rye consumption for the rye metabolomics regression tutorial, using unique individualsYR2