Learn R Programming

The mt R package

Overview

This package provides 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.

Installation from CRAN

install.packages("mt")

Installation from github

library(devtools)
install_github("wanchanglin/mt")

Usage

See the help pages of the package for details.

Copy Link

Version

Install

install.packages('mt')

Monthly Downloads

208

Version

2.0-1.20

License

GPL (>= 2)

Issues

0

Pull Requests

0

Stars

3

Forks

1

Maintainer

Wanchang Lin

Last Published

February 12th, 2024

Functions in mt (2.0-1.20)

cor.util

Correlation Analysis Utilities
cl.rate

Assess Classification Performances
classifier

Wrapper Function for Classifiers
binest

Binary Classification
frank.err

Feature Ranking and Validation on Feature Subset
boot.err

Calculate .632 and .632+ Bootstrap Error Rate
boxplot.frankvali

Boxplot Method for Class 'frankvali'
dat.sel

Generate Pairwise Data Set
accest

Estimate Classification Accuracy By Resampling Method
fs.pca

Feature Selection by PCA
data.visualisation

Grouped Data Visualisation by PCA, MDS, PCADA and PLSDA
frankvali

Estimates Feature Ranking Error Rate with Resampling
fs.anova

Feature Selection Using ANOVA
boxplot.maccest

Boxplot Method for Class 'maccest'
feat.agg

Rank aggregation by Borda count algorithm
df.util

Summary Utilities
fs.auc

Feature Selection Using Area under Receiver Operating Curve (AUC)
feat.freq

Frequency and Stability of Feature Selection
fs.pls

Feature Selection Using PLS
feat.mfs

Multiple Feature Selection
fs.relief

Feature Selection Using RELIEF Method
feat.rank.re

Feature Ranking with Resampling Method
fs.bw

Feature Selection Using Between-Group to Within-Group (BW) Ratio
fs.welch

Feature Selection Using Welch Test
mdsplot

Plot Classical Multidimensional Scaling
fs.kruskal

Feature Selection Using Kruskal-Wallis Test
fs.rf

Feature Selection Using Random Forests (RF)
list.util

List Manipulation Utilities
fs.rfe

Feature Selection Using SVM-RFE
mv.util

Missing Value Utilities
osc_wise

Orthogonal Signal Correction (OSC) Approach by Wise and Gallagher.
fs.snr

Feature Selection Using Signal-to-Noise Ratio (SNR)
mc.fried

Multiple Comparison by 'Friedman Test' and Pairwise Comparison by 'Wilcoxon Test'
osc_wold

Orthogonal Signal Correction (OSC) Approach by Wold et al.
mc.norm

Normality Test by Shapiro-Wilk Test
mbinest

Binary Classification by Multiple Classifier
mc.anova

Multiple Comparison by 'ANOVA' and Pairwise Comparison by 'HSDTukey Test'
get.fs.len

Get Length of Feature Subset for Validation
grpplot

Plot Matrix-Like Object by Group
fs.wilcox

Feature Selection Using Wilcoxon Test
osc

Orthogonal Signal Correction (OSC)
plot.maccest

Plot Method for Class 'maccest'
predict.plsc

Predict Method for Class 'plsc' or 'plslda'
pcaplot

Plot Function for PCA with Grouped Values
plot.accest

Plot Method for Class 'accest'
osc_sjoblom

Orthogonal Signal Correction (OSC) Approach by Sjoblom et al.
predict.osc

Predict Method for Class 'osc'
pca.outlier

Outlier detection by PCA
predict.pcalda

Predict Method for Class 'pcalda'
plot.pcalda

Plot Method for Class 'pcalda'
preproc

Pre-process Data Set
stats.util

Statistical Summary Utilities for Two-Classes Data
trainind

Generate Index of Training Samples
maccest

Estimation of Multiple Classification Accuracy
pcalda

Classification with PCADA
plot.plsc

Plot Method for Class 'plsc' or 'plslda'
panel.elli

Panel Function for Plotting Ellipse and outlier
panel.smooth.line

Panel Function for Plotting Regression Line
plsc

Classification with PLSDA
tune.func

Functions for Tuning Appropriate Number of Components
valipars

Generate Control Parameters for Resampling
pval.util

P-values Utilities
save.tab

Save List of Data Frame or Matrix into CSV File
abr1

abr1 Data