Man pages for mt
Metabolomics Data Analysis Toolbox

abr1abr1 Data
accestEstimate Classification Accuracy By Resampling Method
binestBinary Classification
boot.errCalculate .632 and .632+ Bootstrap Error Rate
boxplot.frankvaliBoxplot Method for Class 'frankvali'
boxplot.maccestBoxplot Method for Class 'maccest'
classifierWrapper Function for Classifiers
cl.perfAssess Classification Performances
cor.utilCorrelation Analysis Utilities
data.visualisationGrouped Data Visualisation by PCA, MDS, PCADA and PLSDA
dat.selGenerate Pairwise Data Set
df.utilSummary Utilities
feat.aggRank aggregation by Borda count algorithm
feat.freqFrequency and Stability of Feature Selection
feat.mfsMultiple Feature Selection
feat.rank.reFeature Ranking with Resampling Method
frank.errFeature Ranking and Validation on Feature Subset
frankvaliEstimates Feature Ranking Error Rate with Resampling
fs.anovaFeature Selection Using ANOVA
fs.aucFeature Selection Using Area under Receiver Operating Curve...
fs.bwFeature Selection Using Between-Group to Within-Group (BW)...
fs.kruskalFeature Selection Using Kruskal-Wallis Test
fs.pcaFeature Selection by PCA
fs.plsFeature Selection Using PLS
fs.reliefFeature Selection Using RELIEF Method
fs.rfFeature Selection Using Random Forests (RF)
fs.rfeFeature Selection Using SVM-RFE
fs.snrFeature Selection Using Signal-to-Noise Ratio (SNR)
fs.welchFeature Selection Using Welch Test
fs.wilcoxFeature Selection Using Wilcoxon Test
get.fs.lenGet Length of Feature Subset for Validation
grpplotPlot Matrix-Like Object by Group
list.utilList Manipulation Utilities
maccestEstimation of Multiple Classification Accuracy
mbinestBinary Classification by Multiple Classifier
mc.anovaMultiple Comparison by 'ANOVA' and Pairwise Comparison by...
mc.friedMultiple Comparison by 'Friedman Test' and Pairwise...
mc.normNormality Test by Shapiro-Wilk Test
mdsplotPlot Classical Multidimensional Scaling
mv.utilMissing Value Utilities
oscOrthogonal Signal Correction (OSC)
osc_sjoblomOrthogonal Signal Correction (OSC) Approach by Sjoblom et al.
osc_wiseOrthogonal Signal Correction (OSC) Approach by Wise and...
osc_woldOrthogonal Signal Correction (OSC) Approach by Wold et al.
panel.elliPanel Function for Plotting Ellipse and outlier
panel.smooth.linePanel Function for Plotting Regression Line
pcaldaClassification with PCADA
pca.outlierOutlier detection by PCA
pcaplotPlot Function for PCA with Grouped Values
plot.accestPlot Method for Class 'accest'
plot.maccestPlot Method for Class 'maccest'
plot.pcaldaPlot Method for Class 'pcalda'
plot.plscPlot Method for Class 'plsc' or 'plslda'
plscClassification with PLSDA
predict.oscPredict Method for Class 'osc'
predict.pcaldaPredict Method for Class 'pcalda'
predict.plscPredict Method for Class 'plsc' or 'plslda'
preprocPre-process Data Set
pval.utilP-values Utilities
save.tabSave List of Data Frame or Matrix into CSV File
stats.utilStatistical Summary Utilities for Two-Classes Data
trainindGenerate Index of Training Samples
tune.funcFunctions for Tuning Appropriate Number of Components
valiparsGenerate Control Parameters for Resampling
mt documentation built on Feb. 2, 2022, 1:07 a.m.