Metabolomics Data Analysis Toolbox

Documentation for package ‘mt’ version 2.0-1.19

DESCRIPTION file.

Help Pages

-- 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
mds.plot.wrap	Grouped Data Visualisation by PCA, MDS, PCADA and PLSDA
mdsplot	Plot Classical Multidimensional Scaling
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
pca.plot.wrap	Grouped Data Visualisation by PCA, MDS, PCADA and PLSDA
pcalda	Classification with PCADA
pcalda.default	Classification with PCADA
pcalda.formula	Classification with PCADA
pcaplot	Plot Function for PCA with Grouped Values
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'
pls.plot.wrap	Grouped Data Visualisation by PCA, MDS, PCADA and PLSDA
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
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