R: Metabolomics Data Analysis Toolbox

# Metabolomics Data Analysis Toolbox

## Documentation for package ‘mt’ version 2.0-1.20

- DESCRIPTION file.
- Package NEWS.

## Help Pages

A B C D F G H L M O P S T U V

## -- A --

aam.cl | Estimate Classification Accuracy By Resampling Method
aam.mcl | Estimate Classification Accuracy By Resampling Method
abr1 | abr1 Data
accest | Estimate Classification Accuracy By Resampling Method
accest.default | Estimate Classification Accuracy By Resampling Method
accest.formula | Estimate Classification Accuracy By Resampling Method

## -- B --

binest | Binary Classification
boot.err | Calculate .632 and .632+ Bootstrap Error Rate
boxplot.frankvali | Boxplot Method for Class 'frankvali'
boxplot.maccest | Boxplot Method for Class 'maccest'

## -- C --

cl.auc | Assess Classification Performances
cl.perf | Assess Classification Performances
cl.rate | Assess Classification Performances
cl.roc | Assess Classification Performances
classifier | Wrapper Function for Classifiers
combn.pw | Generate Pairwise Data Set
cor.cut | Correlation Analysis Utilities
cor.hcl | Correlation Analysis Utilities
cor.heat | Correlation Analysis Utilities
cor.heat.gram | Correlation Analysis Utilities
corrgram.circle | Correlation Analysis Utilities
corrgram.ellipse | Correlation Analysis Utilities

## -- D --

dat.sel | Generate Pairwise Data Set
df.summ | Summary Utilities

## -- F --

feat.agg | Rank aggregation by Borda count algorithm
feat.freq | Frequency and Stability of Feature Selection
feat.mfs | Multiple Feature Selection
feat.mfs.stab | Multiple Feature Selection
feat.mfs.stats | Multiple Feature Selection
feat.rank.re | Feature Ranking with Resampling Method
frank.err | Feature Ranking and Validation on Feature Subset
frankvali | Estimates Feature Ranking Error Rate with Resampling
frankvali.default | Estimates Feature Ranking Error Rate with Resampling
frankvali.formula | Estimates Feature Ranking Error Rate with Resampling
fs.anova | Feature Selection Using ANOVA
fs.auc | Feature Selection Using Area under Receiver Operating Curve (AUC)
fs.bw | Feature Selection Using Between-Group to Within-Group (BW) Ratio
fs.cl | Estimates Feature Ranking Error Rate with Resampling
fs.cl.1 | Estimates Feature Ranking Error Rate with Resampling
fs.kruskal | Feature Selection Using Kruskal-Wallis Test
fs.pca | Feature Selection by PCA
fs.pls | Feature Selection Using PLS
fs.plsvip | Feature Selection Using PLS
fs.plsvip.1 | Feature Selection Using PLS
fs.plsvip.2 | Feature Selection Using PLS
fs.relief | Feature Selection Using RELIEF Method
fs.rf | Feature Selection Using Random Forests (RF)
fs.rf.1 | Feature Selection Using Random Forests (RF)
fs.rfe | Feature Selection Using SVM-RFE
fs.snr | Feature Selection Using Signal-to-Noise Ratio (SNR)
fs.welch | Feature Selection Using Welch Test
fs.wilcox | Feature Selection Using Wilcoxon Test

## -- G --

get.fs.len | Get Length of Feature Subset for Validation
grpplot | Plot Matrix-Like Object by Group

## -- H --

hm.cols | Correlation Analysis Utilities

## -- L --

lda_plot_wrap | Grouped Data Visualisation by PCA, MDS, PCADA and PLSDA
lda_plot_wrap.1 | Grouped Data Visualisation by PCA, MDS, PCADA and PLSDA
list2df | List Manipulation Utilities

## -- M --

maccest | Estimation of Multiple Classification Accuracy
maccest.default | Estimation of Multiple Classification Accuracy
maccest.formula | Estimation of Multiple Classification Accuracy
mbinest | Binary Classification by Multiple Classifier
mc.anova | Multiple Comparison by 'ANOVA' and Pairwise Comparison by 'HSDTukey Test'
mc.fried | Multiple Comparison by 'Friedman Test' and Pairwise Comparison by 'Wilcoxon Test'
mc.norm | Normality Test by Shapiro-Wilk Test
mdsplot | Plot Classical Multidimensional Scaling
mds_plot_wrap | Grouped Data Visualisation by PCA, MDS, PCADA and PLSDA
mv.fill | Missing Value Utilities
mv.stats | Missing Value Utilities
mv.zene | Missing Value Utilities

## -- O --

osc | Orthogonal Signal Correction (OSC)
osc.default | Orthogonal Signal Correction (OSC)
osc.formula | Orthogonal Signal Correction (OSC)
osc_sjoblom | Orthogonal Signal Correction (OSC) Approach by Sjoblom et al.
osc_wise | Orthogonal Signal Correction (OSC) Approach by Wise and Gallagher.
osc_wold | Orthogonal Signal Correction (OSC) Approach by Wold et al.

## -- P --

panel.elli | Panel Function for Plotting Ellipse and outlier
panel.elli.1 | Panel Function for Plotting Ellipse and outlier
panel.outl | Panel Function for Plotting Ellipse and outlier
panel.smooth.line | Panel Function for Plotting Regression Line
pca.comp | Plot Function for PCA with Grouped Values
pca.outlier | Outlier detection by PCA
pca.outlier.1 | Outlier detection by PCA
pca.plot | Plot Function for PCA with Grouped Values
pcalda | Classification with PCADA
pcalda.default | Classification with PCADA
pcalda.formula | Classification with PCADA
pcaplot | Plot Function for PCA with Grouped Values
pca_plot_wrap | Grouped Data Visualisation by PCA, MDS, PCADA and PLSDA
plot.accest | Plot Method for Class 'accest'
plot.maccest | Plot Method for Class 'maccest'
plot.pcalda | Plot Method for Class 'pcalda'
plot.plsc | Plot Method for Class 'plsc' or 'plslda'
plot.plslda | Plot Method for Class 'plsc' or 'plslda'
plsc | Classification with PLSDA
plsc.default | Classification with PLSDA
plsc.formula | Classification with PLSDA
plslda | Classification with PLSDA
plslda.default | Classification with PLSDA
plslda.formula | Classification with PLSDA
pls_plot_wrap | Grouped Data Visualisation by PCA, MDS, PCADA and PLSDA
predict.osc | Predict Method for Class 'osc'
predict.pcalda | Predict Method for Class 'pcalda'
predict.plsc | Predict Method for Class 'plsc' or 'plslda'
predict.plslda | Predict Method for Class 'plsc' or 'plslda'
preproc | Pre-process Data Set
preproc.const | Pre-process Data Set
preproc.sd | Pre-process Data Set
print.accest | Estimate Classification Accuracy By Resampling Method
print.frankvali | Estimates Feature Ranking Error Rate with Resampling
print.maccest | Estimation of Multiple Classification Accuracy
print.osc | Orthogonal Signal Correction (OSC)
print.pcalda | Classification with PCADA
print.plsc | Classification with PLSDA
print.plslda | Classification with PLSDA
print.summary.accest | Estimate Classification Accuracy By Resampling Method
print.summary.frankvali | Estimates Feature Ranking Error Rate with Resampling
print.summary.maccest | Estimation of Multiple Classification Accuracy
print.summary.osc | Orthogonal Signal Correction (OSC)
print.summary.pcalda | Classification with PCADA
print.summary.plsc | Classification with PLSDA
print.summary.plslda | Classification with PLSDA
pval.reject | P-values Utilities
pval.test | P-values Utilities

## -- S --

save.tab | Save List of Data Frame or Matrix into CSV File
shrink.list | List Manipulation Utilities
stats.mat | Statistical Summary Utilities for Two-Classes Data
stats.vec | Statistical Summary Utilities for Two-Classes Data
summary.accest | Estimate Classification Accuracy By Resampling Method
summary.frankvali | Estimates Feature Ranking Error Rate with Resampling
summary.maccest | Estimation of Multiple Classification Accuracy
summary.osc | Orthogonal Signal Correction (OSC)
summary.pcalda | Classification with PCADA
summary.plsc | Classification with PLSDA
summary.plslda | Classification with PLSDA

## -- T --

trainind | Generate Index of Training Samples
tune.func | Functions for Tuning Appropriate Number of Components
tune.pcalda | Functions for Tuning Appropriate Number of Components
tune.plsc | Functions for Tuning Appropriate Number of Components
tune.plslda | Functions for Tuning Appropriate Number of Components

## -- U --

un.list | List Manipulation Utilities

## -- V --

valipars | Generate Control Parameters for Resampling
vec.summ | Summary Utilities
vec.summ.1 | Summary Utilities