Package: mt 2.0-1.20

Wanchang Lin

mt: Metabolomics Data Analysis Toolbox

Functions for metabolomics data analysis: data preprocessing, orthogonal signal correction, PCA analysis, PCA-DA analysis, PLS-DA analysis, classification, feature selection, correlation analysis, data visualisation and re-sampling strategies.

Authors:Wanchang Lin

mt_2.0-1.20.tar.gz
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mt.pdf |mt.html✨
mt/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/wanchanglin/mt/issues

Datasets:

On CRAN:

4.57 score 3 stars 50 scripts 264 downloads 5 mentions 97 exports 16 dependencies

Last updated 10 months agofrom:d182adb8cf. Checks:OK: 5 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 08 2024
R-4.5-winNOTENov 08 2024
R-4.5-linuxNOTENov 08 2024
R-4.4-winOKNov 08 2024
R-4.4-macOKNov 08 2024
R-4.3-winOKNov 08 2024
R-4.3-macOKNov 08 2024

Exports:aam.claam.mclaccestbinestboot.errcl.auccl.perfcl.ratecl.rocclassifiercombn.pwcor.cutcor.hclcor.heatcor.heat.gramcorrgram.circlecorrgram.ellipsedat.seldf.summfeat.aggfeat.freqfeat.mfsfeat.mfs.stabfeat.mfs.statsfeat.rank.refrank.errfrankvalifs.anovafs.aucfs.bwfs.clfs.cl.1fs.kruskalfs.pcafs.plsfs.plsvipfs.plsvip.1fs.plsvip.2fs.relieffs.rffs.rf.1fs.rfefs.snrfs.welchfs.wilcoxget.fs.lengrpplothm.colslda_plot_wraplda_plot_wrap.1list2dfmaccestmbinestmc.anovamc.friedmc.normmds_plot_wrapmdsplotmv.fillmv.statsmv.zeneoscosc_sjoblomosc_wiseosc_woldpanel.ellipanel.elli.1panel.outlpanel.smooth.linepca_plot_wrappca.comppca.outlierpca.outlier.1pca.plotpcaldapcaplotpls_plot_wrapplscplsldapredict.oscpreprocpreproc.constpreproc.sdpval.rejectpval.testsave.tabshrink.liststats.matstats.vectrainindtune.pcaldatune.plsctune.plsldaun.listvaliparsvec.summvec.summ.1

Dependencies:classdeldire1071ellipseinterpjpeglatticelatticeExtraMASSplspngproxyrandomForestRColorBrewerRcppRcppEigen

Readme and manuals

Help Manual

Help pageTopics
abr1 Dataabr1
Estimate Classification Accuracy By Resampling Methodaam.cl aam.mcl accest accest.default accest.formula print.accest print.summary.accest summary.accest
Binary Classificationbinest
Calculate .632 and .632+ Bootstrap Error Rateboot.err
Boxplot Method for Class 'frankvali'boxplot.frankvali
Boxplot Method for Class 'maccest'boxplot.maccest
Assess Classification Performancescl.auc cl.perf cl.rate cl.roc
Wrapper Function for Classifiersclassifier
Correlation Analysis Utilitiescor.cut cor.hcl cor.heat cor.heat.gram corrgram.circle corrgram.ellipse hm.cols
Generate Pairwise Data Setcombn.pw dat.sel
Grouped Data Visualisation by PCA, MDS, PCADA and PLSDAlda_plot_wrap lda_plot_wrap.1 mds_plot_wrap pca_plot_wrap pls_plot_wrap
Summary Utilitiesdf.summ vec.summ vec.summ.1
Rank aggregation by Borda count algorithmfeat.agg
Frequency and Stability of Feature Selectionfeat.freq
Multiple Feature Selectionfeat.mfs feat.mfs.stab feat.mfs.stats
Feature Ranking with Resampling Methodfeat.rank.re
Feature Ranking and Validation on Feature Subsetfrank.err
Estimates Feature Ranking Error Rate with Resamplingfrankvali frankvali.default frankvali.formula fs.cl fs.cl.1 print.frankvali print.summary.frankvali summary.frankvali
Feature Selection Using ANOVAfs.anova
Feature Selection Using Area under Receiver Operating Curve (AUC)fs.auc
Feature Selection Using Between-Group to Within-Group (BW) Ratiofs.bw
Feature Selection Using Kruskal-Wallis Testfs.kruskal
Feature Selection by PCAfs.pca
Feature Selection Using PLSfs.pls fs.plsvip fs.plsvip.1 fs.plsvip.2
Feature Selection Using RELIEF Methodfs.relief
Feature Selection Using Random Forests (RF)fs.rf fs.rf.1
Feature Selection Using SVM-RFEfs.rfe
Feature Selection Using Signal-to-Noise Ratio (SNR)fs.snr
Feature Selection Using Welch Testfs.welch
Feature Selection Using Wilcoxon Testfs.wilcox
Get Length of Feature Subset for Validationget.fs.len
Plot Matrix-Like Object by Groupgrpplot
List Manipulation Utilitieslist2df shrink.list un.list
Estimation of Multiple Classification Accuracymaccest maccest.default maccest.formula print.maccest print.summary.maccest summary.maccest
Binary Classification by Multiple Classifiermbinest
Multiple Comparison by 'ANOVA' and Pairwise Comparison by 'HSDTukey Test'mc.anova
Multiple Comparison by 'Friedman Test' and Pairwise Comparison by 'Wilcoxon Test'mc.fried
Normality Test by Shapiro-Wilk Testmc.norm
Plot Classical Multidimensional Scalingmdsplot
Missing Value Utilitiesmv.fill mv.stats mv.zene
Orthogonal Signal Correction (OSC)osc osc.default osc.formula print.osc print.summary.osc summary.osc
Orthogonal Signal Correction (OSC) Approach by Sjoblom et al.osc_sjoblom
Orthogonal Signal Correction (OSC) Approach by Wise and Gallagher.osc_wise
Orthogonal Signal Correction (OSC) Approach by Wold et al.osc_wold
Panel Function for Plotting Ellipse and outlierpanel.elli panel.elli.1 panel.outl
Panel Function for Plotting Regression Linepanel.smooth.line
Outlier detection by PCApca.outlier pca.outlier.1
Classification with PCADApcalda pcalda.default pcalda.formula print.pcalda print.summary.pcalda summary.pcalda
Plot Function for PCA with Grouped Valuespca.comp pca.plot pcaplot
Plot Method for Class 'accest'plot.accest
Plot Method for Class 'maccest'plot.maccest
Plot Method for Class 'pcalda'plot.pcalda
Plot Method for Class 'plsc' or 'plslda'plot.plsc plot.plslda
Classification with PLSDAplsc plsc.default plsc.formula plslda plslda.default plslda.formula print.plsc print.plslda print.summary.plsc print.summary.plslda summary.plsc summary.plslda
Predict Method for Class 'osc'predict.osc
Predict Method for Class 'pcalda'predict.pcalda
Predict Method for Class 'plsc' or 'plslda'predict.plsc predict.plslda
Pre-process Data Setpreproc preproc.const preproc.sd
P-values Utilitiespval.reject pval.test
Save List of Data Frame or Matrix into CSV Filesave.tab
Statistical Summary Utilities for Two-Classes Datastats.mat stats.vec
Generate Index of Training Samplestrainind
Functions for Tuning Appropriate Number of Componentstune.func tune.pcalda tune.plsc tune.plslda
Generate Control Parameters for Resamplingvalipars